Topic: artificial intelligence

S-STEM: Preparing Academic Talent for High-demand STEM Fields at a Historically Black Community College

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Lawson State Community College. A total of 75 scholars pursuing Associate degrees in Biology, Chemistry, Computer Science, Engineering, Industrial Electronics Technology, Manufacturing Technology, and Mathematics will receive scholarships for up to five years. Scholars will receive multidimensional mentoring and the project will build strong scholar cohorts through peer networking and early undergraduate research. Additional activities for scholars include artificial intelligence (AI) micro-credentials. The overall goal of this Track 1 project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in engineering, science, and technology and other key areas of need. The project will be assessed by an experienced evaluator that will assess scholar growth along multiple dimensions, and the data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Harnessing the Power of Off-Dynamics Reinforcement Learning: Foundations, Algorithms, and Applications

Many important decisions in everyday life, such as choosing effective medical treatments, managing public health responses, or controlling autonomous systems, must be made step by step while learning from limited and imperfect information. Current artificial intelligence methods often require extensive trial-and-error interactions with the real world to learn effective strategies, which is impractical or unsafe in high-stakes settings where mistakes are costly or unethical. This project addresses this challenge by developing new approaches that allow intelligent systems to learn from simulated or indirect environments and reliably transfer that knowledge to real-world situations, even when conditions differ. By enabling safer and more data-efficient decision-making, the project has the potential to improve technologies in healthcare, robotics, and other critical domains, ultimately benefiting public health, economic productivity, and societal well-being. The project will also contribute to education and workforce development by training students at multiple levels, creating accessible learning materials, and conducting outreach activities to broaden participation in artificial intelligence. All resulting software, data resources, and educational materials will be made openly available to maximize their impact. This project develops a comprehensive theoretical and algorithmic framework for off-dynamics reinforcement learning, which studies how to train decision-making agents in a source domain, such as a simulator, and effectively deploy them in a target domain with different and potentially unknown transition dynamics. The research addresses fundamental challenges arising from distributional shifts between training and deployment environments. The work is organized into three main research activities: (1) developing distributionally robust learning methods that ensure reliable performance via minimax optimization over an uncertainty set of transitions when the target domain is unknown, supported by finite-sample performance guarantees; (2) designing algorithms that leverage partial access to target-domain data through data augmentation and cross-domain learning to improve transfer efficiency; and (3) establishing new frameworks for learning under limited interaction and high policy-switching costs, focusing on stability and efficiency in real-world deployment. The proposed methods will be analyzed theoretically to characterize statistical limits and performance guarantees and will be validated empirically on standard reinforcement learning benchmarks and real-world healthcare datasets. All developed algorithms will be released as open-source implementations to support reproducibility and broad adoption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Calibrating Human Trust in Artificial Intelligence through Real-Time Behavioral and Physiological Feedback in Healthcare Decision Making

Artificial intelligence is increasingly used to support decision making in healthcare, especially in time-sensitive settings such as emergency care and intensive care units. However, people do not always rely on these systems appropriately. Some users may place too much trust in incorrect recommendations, while others may ignore useful guidance. These mismatches can affect decision quality and patient safety. This project studies how people interact with artificial intelligence in such settings and explores ways to support more appropriate use. By improving how clinicians interpret and respond to artificial intelligence, the work aims to support safer and more reliable decision making. The project also contributes to education by engaging students in simulation-based learning and providing training opportunities in human-centered artificial intelligence. This project develops a framework to study how trust in artificial intelligence changes over time during decision making. The research combines behavioral data with physiological signals, including eye movements and brain activity, to better understand user responses. First, a mathematical model is developed to represent trust as a changing internal state influenced by task conditions and system performance. Second, machine learning methods are used to estimate this state in real time using data collected from clinicians interacting with simulated clinical scenarios. Third, the project explores interface strategies that provide targeted feedback to help users better align their decisions with the reliability of the system. These approaches are evaluated through controlled simulation studies with clinician participants. The project will generate data, models, and open resources to support future research on human interaction with artificial intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Designing Domain Knowledge-Guided Learning Architectures towards Wireless Network Security: Enabling Effective and Efficient Attacks and Countermeasures

Modern wireless networks have become increasingly complex and densely populated and can create a massive volume of operation data. As a result, extensive efforts from both academia and industry have focused on leveraging artificial intelligence (AI) in wireless network security related tasks such as (i) adversarial inference, (ii) adversarial generation, and (iii) data transformation. This project will focus on exploring new directions for incorporating additional domain knowledge to improve the efficiency of machine learning architecture design. The project's novelties are (i) investigating the wireless-domain knowledge used in mobile network design across different protocol layers and classifying this knowledge based on how it can be deterministically incorporated into learning model design; and (ii) designing specialized learning architectures that translate this domain knowledge into AI-friendly representations to improve both learning efficiency and performance. The project's broader significance and importance are advancing the state of the art in wireless network security, enhancing undergraduate student training opportunities, openly disseminating training materials, and carrying out outreach activities. This project targets three major categories of learning models: (i) typical centralized learning models, (ii) decentralized learning models, and (iii) large language models, and explores the incorporation of wireless-domain knowledge into each class to address different security tasks. Specifically, the project outlines three research thrusts based on both system design and practical evaluations: (i) creating a new training-efficient, wireless-specific learning model as a surrogate model for resource allocation attacks; (ii) developing a new large language model-powered multi-agent framework to enable attacks in cooperative spectrum sensing; (iii) designing a novel client-to-server parameter sharing strategy in federated learning to defend against membership inference attacks. This project will also perform comprehensive evaluations based on real-world wireless experiments to validate and improve the proposed designs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: OPENALIGN: Towards Open-World Preference Alignment for Large Language Models

As artificial intelligence (AI) systems are increasingly deployed in critical domains such as healthcare, scientific discovery, and autonomous decision-making, ensuring that foundation AI models such as large language models (LLMs) align with human values and preferences has become essential for their safe and beneficial deployment. However, most existing approaches rely on large amounts of high-quality labeled preference data and assume clean, stable, well-controlled environments. These assumptions are often violated in real-world scenarios, where data may be limited, noisy, or subject to change over time. This project addresses the fundamental problem of aligning LLMs with human preferences under such real or open-world settings. The outcomes are expected to improve the data efficiency and reliability of LLMs in high-impact applications, including medical diagnosis and molecular discovery, while also contributing to education and workforce development through the integration of research and training activities. The project focuses on three key aspects of aligning LLMs with human preferences in open-world settings. First, it develops novel data-efficient preference alignment algorithms that enable LLMs to maintain effective alignment in open-world environments with limited human-annotated preference data. Specifically, when LLMs encounter new tasks or domains, the proposed algorithms can strategically minimize reliance on extensive human or AI annotation while maximizing alignment performance across different low-data scenarios. Secondly, this project aims to enhance the reliability of LLM-based AI systems to maintain safe and robust alignment with human preferences when confronted with unreliable inputs. We will develop algorithmic solutions that can mitigate various forms of data-quality issues (e.g., distribution shifts, label noise, and human value shifts) while preserving alignment performance across different open-world environments. Lastly, this project will demonstrate successful deployment in high-stakes domains, including biochemistry and public health, and will reveal fundamental principles about domain-specific preference alignment while maintaining efficiency and reliability guarantees. This cross-interdisciplinary validation will open new research avenues for LLM alignment with human preferences in interdisciplinary research. Overall, the transition from closed-world to open-world preference alignment represents a fundamental paradigm shift that will provide critical insights about real-world deployment challenges, benefiting the entire AI community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: A Safety-Aware Learning Framework for Identifying and Mitigating Risks in Human-LLM Interactions in Healthcare

Large language models are increasingly used in healthcare applications such as virtual assistants and decision support tools, offering new opportunities to improve access to care and patient outcomes. However, these systems can introduce new kinds of risks that arise not from the model alone, but from how people interact with it. For example, patients may rely too heavily on automated advice, receive responses shaped by harmful preconceptions or be unintentionally influenced toward unsafe decisions. These risks are especially concerning in sensitive settings such as mental health and addiction recovery, where errors can have serious consequences. This project addresses these challenges by developing new methods to make interactions between people and artificial intelligence systems safer and more trustworthy. The work aims to improve the reliability of healthcare technologies, support safer patient experiences, and contribute to the broader goal of responsible artificial intelligence. Educational activities include developing interdisciplinary coursework and engaging students from diverse backgrounds in research at the intersection of artificial intelligence and health. This project develops a unified, safety-aware learning framework for identifying and mitigating risks in human-large language model interactions in healthcare. The research investigates three integrated thrusts. First, it develops predictive models to detect fundamental and emerging interaction risks, such as overreliance, stereotyping, manipulation, and privacy violations, using supervised and contrastive learning techniques with interpretable outputs. Second, it introduces robust learning methods to mitigate these risks by incorporating user intent, clinical context, and interaction dynamics, including adversarial training and personalized reinforcement learning algorithms. Third, it designs an adaptive, closed-loop method that jointly optimizes risk identification and mitigation through self-supervised and continual learning, enabling generalization to evolving risks over time. The framework is evaluated using realistic digital simulation environments for addiction recovery and mental health support. The expected outcomes include new machine learning methodologies for AI safety, insights into safe deployment of AI in healthcare, and generalizable techniques for trustworthy human-AI interaction in high-stakes domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Structure-Aware Learning from Weak Supervision for Knowledge Acquisition

Knowledge acquisition—the ability of artificial intelligence (AI) systems to extract actionable insights from vast amounts of unstructured text—is critical for advancements in healthcare, education, and scientific discovery. While Large Language Models (LLMs) have shown impressive capabilities, their reliability depends heavily on massive, perfectly curated datasets, which are expensive and often unavailable in specialized domains. This CAREER project addresses this bottleneck by developing a new paradigm called “structure-aware weak supervision.” Instead of relying on perfect human annotations, the project enables AI systems to learn autonomously from incomplete, noisy, and ambiguous data by discovering and utilizing underlying semantic structures, such as concept hierarchies and retrieval pathways. By reducing the dependency on expensive labeled data, this research democratizes the development of highly accurate, domain-specific AI tools for resource-constrained environments, such as public health agencies and community organizations. The project also integrates these research outcomes into new undergraduate and graduate curricula, open-source educational toolkits, and targeted K-12 outreach programs designed to broaden participation in computing and teach the next generation how to build reliable, human-centered AI systems. This project proposes a unified framework for learning under weak supervision by bridging unstructured language data with structured, interpretable knowledge representations. The research is organized into three synergistic thrusts. Thrust 1 tackles incomplete supervision by inducing latent ontologies from unlabeled corpora via a novel Spherical Hierarchical Expectation-Maximization (SHEM) algorithm, enabling scalable information extraction and classification without predefined schemas. Thrust 2 addresses noisy supervision by designing a Denoising Retrieval-Augmented Generation (DeRAG) framework. It integrates symbolic reasoning over the induced ontologies with Structure-Aware Contrastive Retrieval (SACRet) to actively filter distractors and reliably ground language model outputs. Thrust 3 tackles ambiguous supervision by modeling complex, multi-faceted human preferences. It introduces a Tree of Reward Models (TreeRM) and Hierarchical Dirichlet Thompson Sampling (HDTS) to capture both shared foundational values (e.g., safety, factuality) and personalized user preferences (e.g., tone), ensuring robust AI alignment. Together, these contributions advance the theoretical foundations and practical methodologies of knowledge-centric AI, creating systems that autonomously construct knowledge, dynamically adapt to supervision gaps, and reliably align with hierarchical human values. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Analog Memory Leaks: Mitigating Remanence Side-Channels in Analog Compute-in-Memory Hardware

Next-generation artificial intelligence systems use analog arithmetic to vastly improve efficiency over traditional digital artificial intelligence systems. While the energy efficiency benefits of these analog systems are known, the security vulnerabilities of these new computing architectures are not. Digital systems are built on top of a wealth of defenses and research in cybersecurity, but they do not necessarily apply to analog systems. This project’s novelties are developing guidelines and mitigations for security vulnerabilities in analog computing systems. The project's broader significance and importance are increasing security by addressing vulnerabilities in future artificial intelligence systems, especially in analog computer security. The education and outreach plan addresses an existing and urgent chip design workforce shortage in the United States through increasing the number of digital design classrooms. This project studies next-generation artificial intelligence systems that use novel analog compute-in-memory circuits. Data storage, in normal operation, causes wearout in on-chip storage circuits that change fundamental device parameters like resistance and capacitance. The analog nature of these systems implies that wear, caused by storage (memory), is visible, and can be used to form a side-channel between a user and an adversary. This project will study wearout in a broad spectrum of memory technologies using physics-based simulators and off-the-shelf systems. Ultimately, this project will develop mitigations for current and future artificial intelligence systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Numerically Literate AI via the Large Number Model and Foundational Data Curation Methods

Many modern Artificial Intelligence (AI) models can produce meaningful text, but they often fail on complex structural and numerical data involving different units, formulas, information describing the data (i.e., metadata), and hierarchies. These failures are especially concerning in areas such as medicine, finance, defense, and space, where even small quantitative mistakes can lead to misleading conclusions and significant negative consequences. This project aims to address this problem by developing a new AI model focused on accurate comprehension of complex numerical and structured data rather than natural language text. The project will help make scientific knowledge more transparent and accessible, while also supporting education through new teaching materials, student research opportunities, and outreach activities that engage learners in data reasoning. By improving the ability of AI to work correctly with complex numerical and structured data, the project advances the progress of science, supports health and welfare, and strengthens the nation’s capacity for trustworthy data-driven discovery and decision-making. The project develops the Large Number Model (LNM), a hybrid neural-symbolic model for reliable reasoning over numbers, units, formulas, and complex tabular data. The research includes three main activities: creating scalable methods to extract numerical and structured information from documents, designing model architectures that represent quantities and two-dimensional tabular structures more effectively than text-only systems, and incorporating symbolic validation to check algebraic, dimensional, and semantic consistency. The project will also develop methods for combining quantitative evidence across multiple sources and will evaluate the resulting system through controlled experiments, robustness tests, and benchmark datasets drawn from scientific and medical domains. The expected contribution is a new foundation for AI systems that are more accurate, interpretable, dependable, and compatible with the full data cycle when working with complex numerical and structured knowledge. This, in turn, is expected to maximize the utility of information resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Frontiers of Knowledge in Foundation Models

Foundation models are large artificial intelligence (AI) systems trained on vast amounts of data to perform a wide range of tasks, including answering questions, generating content, and assisting decision-making. These models are increasingly used in areas that affect everyday life, such as healthcare, education, and environmental planning. However, despite their impressive capabilities, they often rely on patterns and correlations in data rather than true causal relationships. This limitation can lead to unreliable or misleading outputs, especially in high-stakes situations. For example, a model may incorrectly assume that one factor causes another simply because they frequently appear together in data. This project addresses this critical challenge by enabling foundation models to better understand and use causal knowledge, which describes how one factor directly influences another in the real world. By improving the ability of these models to reason about cause and effect, the project aims to make them more reliable, transparent, and aligned with human reasoning. The results will support safer and more effective use of AI in important societal domains, strengthen decision-making in complex environments, and contribute to education and workforce development by training students in emerging areas of trustworthy AI. This project develops a systematic framework for understanding, leveraging, editing, and applying causal knowledge in foundation models, organized into four complementary thrusts. The first thrust introduces methods to interpret causal relationships embedded within large-scale models by analyzing internal components that encode causal knowledge across language, vision, and multimodal systems. The second thrust designs approaches to incorporate external causal knowledge into model reasoning, improving performance in tasks such as question answering, causal reasoning, and video understanding. The third thrust establishes techniques for editing causal knowledge within models, enabling targeted updates to specific relationships while preserving overall model performance and consistency. The fourth thrust focuses on empirical evaluation and application of the proposed methods across diverse application domains, including healthcare, materials science, and environmental systems. The project integrates research with education through curriculum development, student mentoring, and outreach activities, and produces open-source tools and resources to support broader adoption. Together, these efforts advance the development of interpretable, controllable, and generalizable foundation models grounded in a causal perspective. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Inferring Specifications for AI by Modeling Humans

Artificial intelligence (AI) systems increasingly influence both high stakes and everyday decisions across many sectors of the economy. These systems, however, are not developed in isolation. Instead, they depend on people to provide instructions that describe what the system should do and what outcomes it should avoid. These instructions can take many forms. They may be written explicitly by domain experts or learned from data such as human preferences over outcomes. However, providing clear and reliable instructions for intelligent systems is difficult even for relatively narrow applications. Instructions can be too rigid, too vague, or simply incorrect, and any of these problems can cause systems to behave in unintended ways. These failures occur because instructions are created by people, and human reasoning is shaped by limited information, context, and common cognitive mistakes. As AI becomes more widespread, improving how systems interpret human intent will be essential for safety and reliability. This project addresses that challenge by studying how people communicate goals to machines and by designing AI systems that can interpret imperfect instructions by reasoning about the intent behind them. The expected outcomes include safer decision-making technologies and new tools that help organizations deploy AI more effectively. This project develops computational foundations for learning AI specifications from imperfect human input. The research integrates reinforcement learning, Bayesian inference, and computational cognitive modeling with empirical studies of human decision making to better characterize how people communicate goals and where specification errors arise. The work is organized around three research thrusts. The first thrust, Modeling and Inferring AI Specifications, develops probabilistic models of human reasoning that capture systematic specification errors and uses these models to enable AI systems to infer more accurate goals from flawed instructions. The second thrust, Richer Inputs and Representations, expands how AI systems learn from people by incorporating different forms of input such as preferences, demonstrations, explanations, gestures, and structured debate. New algorithms and elicitation interfaces will integrate these signals and resolve inconsistencies across modalities. The third thrust, Personalization and Governance, develops methods for learning multiple reward models that reflect differences in human preferences, enabling scalable personalization and avoiding one-size-fits-all objectives. In parallel, the project will develop educational programs that prepare students to design and govern AI systems. These activities include revising an undergraduate AI course to emphasize human decision-making in the design of AI systems, creating a graduate course on AI policy and governance, and expanding the AI Policy Summer School to help build a national workforce that is fluent in both AI technology and public policy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Mechanism-Driven Machine Learning for Water and Wastewater Treatment

Clean and reliable water treatment is critical to protect public health. However, most treatment decisions still rely on simple models that do not reflect real‑world conditions. Artificial intelligence (AI) has the potential to help engineers make more informed decisions. This CAREER project will develop AI models that help engineers understand how and why treatment processes work at full-scale. The project will reduce risk, improve reliability, and expand access to advanced tools for communities with limited technical resources. The outcomes of this research will be shared with water utilities and used across many treatment systems. The project will also address a national need for a workforce that can use AI responsibly by integrating data science into environmental engineering education. This project will support safer water systems, prepare future engineers, and show how AI can be used as a tool for scientific discovery. This CAREER project will develop an application‑driven AI framework for modeling engineered environmental systems, using water and wastewater disinfection as a representative, high‑risk unit process. The research will integrate multi‑facility operational and water quality data with hybrid modeling approaches that combine physics‑based process models and machine learning (ML). These approaches will include mechanistic ordinary differential equation models coupled with ML components, physics‑informed neural networks, and embedded neural differential equation formulations that constrain learning using known physical, chemical, and biological relationships. Model development and evaluation will explicitly address challenges common to environmental datasets, including data sparsity, autocorrelation, measurement uncertainty, and site‑specific variability, through time‑aware validation, uncertainty quantification, and risk‑based performance metrics. Mechanistic insights inferred from the models will be tested using a pilot‑scale disinfection system to distinguish true process behavior from artifacts introduced by data collection or modeling practices. The project will also develop protocols for model reuse and adaptation using transfer learning and privacy‑preserving federated learning, enabling models trained on multi‑facility data to be applied in data‑limited systems without sharing raw data. Together, these methods will advance the scientific use of AI in environmental engineering by enabling mechanistically grounded discovery, improving generalizability across real systems, and establishing a foundation for trustworthy, reusable models for infrastructure applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Center: National Synthesis Center for Organismal Resilience to Environmental Change (NSCORE)

The National Synthesis Center for Organismal Resilience (NSCORE) will inspire, unite, and train organismal biologists and computer scientists in a collaborative effort to leverage vast amounts of real-world data utilizing advanced Artificial Intelligence (AI) techniques to address the growing challenges in understanding and predicting organismal resilience. By integrating across diverse disciplines, NSCORE will transform how scientists study and predict organismal responses to change and make recommendations to improve resilience. Ultimately, its innovative programs and collaborations will empower a new generation of scientists to address one of the most critical and growing challenges of our time. Central to NSCORE’s mission is the education and training of the next generation of computational organismal biologists. To achieve this, NSCORE will implement a hybrid interdisciplinary education program for students and educators, from K-12 to university. At the core of this vertically integrated program will be the NSCORE Nexus, an online infrastructure for broad reach, that will be complemented by in-person events for deeper engagement. Initiatives will include AI-focused Bootcamps for biologists, Virtual K-12 Teacher Institutes, and Train-the-Trainer Programs for university instructors. This manifold menu of programs will emphasize opportunities for everyone, everywhere, reaching underserved urban and rural schools, minority-serving institutions, and underdeveloped regions globally. NSCORE will also engage the public through exhibits, open talks, and online resources, thereby broadening awareness and participation. Organisms respond to complex and dynamic external conditions through a series of interrelated behavioral, physiological, morphological, and molecular mechanisms. Advances in technology and increasingly deeper connections to multiple biological disciplines, including molecular, neuro-, and developmental biology, are revolutionizing the study of organisms in their natural environments. With access to vast and complex biological datasets—from genomes to behaviors—and detailed environmental data from satellite imagery and global weather networks, we are poised to revolutionize our understanding and prediction of organismal resilience. By merging organismal biology with computer science into the field of computational organismal biology, NSCORE will help advance our ability to explain and predict organismal resiliency to change by developing the tools and expertise needed to synthesize these varied datasets and bridge multiple scales using cutting-edge AI techniques. As a global hub for computational organismal biology, NSCORE will facilitate interdisciplinary synthesis through Working Groups and Catalysis Conferences that bring together experts and students. Nucleated at Columbia University, NSCORE will collaborate with academic and research institutions across the greater New York City area, North America, Europe, Asia, and beyond to foster an integrated community of researchers dedicated to achieving its goals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Understanding nutrient dynamics in American rivers through remote sensing and artificial intelligence

This award supports the study of nutrient runoff and its effects on American river systems. Nitrogen and phosphorus are known to cause harmful algal blooms and other ecological impacts in rivers. However, knowledge about the causes of variability in nutrient loads across river networks remains limited. Through the application of artificial intelligence to data from satellite remote sensing, this project will generate a detailed map of nutrients in rivers across the United States over time. This map will then be analyzed to understand human and natural factors affecting nutrient variability. This research integrates with education for high school, undergraduate, and graduate students. Project outcomes will support water management, ecosystem protection, and public health. This project will pursue three objectives. (1) A novel modeling framework that integrates remote sensing and deep learning will be developed. This framework will be used to estimate daily, reach-level total phosphorus and total nitrogen concentration in American rivers over the past five decades. (2) Major drivers and controlling mechanisms for nutrient variability across space and time will be identified. (3) Relationships between riverine nutrients and harmful algal blooms across various settings will be quantified. Outcomes of these analyses will reveal spatial patterns of nutrient sensitivity and eutrophication risk. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Enabling Single-Step Additive Manufacturing of Ceramics via Laser-Triggered Flash Sintering and Scientific Artificial Intelligence-based Multiscale Modeling

Ceramics possess exceptional resistance to heat, wear, radiation and corrosion, yet their widespread adoption is limited because direct manufacturing complex ceramic parts requires extremely high temperatures and often leads to cracking, defects, and long processing times. This Faculty Early Career Development Program (CAREER) award supports research in additive manufacturing (AM) of high-performance ceramics to enable faster, more reliable production of components used in aerospace, nuclear energy, electronics, and biomedical systems. Current AM methods either rely on multi-step processes that are slow and prone to distortion or single-step methods that generate severe cracking and poor material quality. Research enabled by this award seeks to overcome these limitations by developing a new AM approach that enables rapid, defect-resistant fabrication of complex components. By advancing reliable manufacturing of high-performance ceramics, the award is expected to accelerate innovations in energy efficiency, advanced transportation, and resilient infrastructure, strengthening U.S. technological leadership, economic competitiveness, and national security.    This CAREER award aims to establish the scientific foundation for a transformative single-step ceramic AM process based on laser-triggered flash sintering (LTFS). A central challenge is the lack of fundamental understanding of how coupled laser heating and electric-field stimulation initiate flash sintering, govern densification kinetics, and influence microstructure evolution, defect formation, and process reliability. To address this gap, research is planned to develop an integrated experimental, computational, and data-driven framework. Specifically, the research tasks include (1) design and construct an LTFS-enabled AM testbed with in-situ monitoring for real-time process characterization; (2) investigate flash-sintering initiation, stability, and microstructure evolution through coordinated experiments and multiphysics microscale modeling; (3) establish a multiscale electro-thermal-mechanical modeling framework to quantify how manufacturing parameters influence densification, shrinkage, and resulting material properties; (4) develop a Scientific Artificial Intelligence (Sci-AI) framework that integrates in-situ data with physics-based models to capture process stochasticity, improve predictive accuracy, and enable intelligent process control; and (5) demonstrate manufacturing capability through fabrication and evaluation of complex, high-performance ceramic components. The outcomes are expected to establish quantitative process–structure–property relationships for LTFS-based ceramic manufacturing, enabling defect-controlled fabrication of advanced ceramics and advancing smart, data-driven manufacturing of high-performance materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Towards Semantic-Centric Wireless Foundations for Swarm AI

Artificial intelligence (AI) is rapidly expanding from centralized computing infrastructures into the physical world, where networks of distributed devices must sense, reason, and act together in dynamic and uncertain environments. This transformation gives rise to swarm AI, an emerging form of intelligent infrastructure that supports applications such as disaster response, environmental monitoring, precision agriculture, and autonomous mobility. Unlike traditional systems that rely on stable wired connections, swarm intelligence operates over wireless links that are intermittent, noisy, and resource constrained. Communication, therefore, becomes a central bottleneck that limits reliability, efficiency, and coordination. This project establishes a new wireless foundation for swarm AI by prioritizing the meaning and task relevance of transmitted information rather than raw bit accuracy alone. By strengthening how distributed agents share mission-critical information under challenging wireless conditions, the research enhances the resilience, scalability, and interoperability of next-generation intelligent systems. The project integrates research and education through curriculum development in communication-aware AI, hands-on mentoring of undergraduate and graduate students, outreach to K-12 learners, and open dissemination of research outcomes. These activities broaden participation in advanced wireless and intelligent systems research and contribute to workforce development in emerging communication and intelligent system technologies. The project addresses a fundamental gap between AI systems that assume ideal connectivity and wireless communication protocols that optimize bit-level fidelity without accounting for task intent. The scientific problem is how to design wireless architectures that are aware of semantic content, resilient to time-varying channel impairments, and adaptive to heterogeneous device capabilities in swarm settings. The research establishes a semantic-centric communication framework organized into three integrated thrusts: (i) robust semantic transceiver principles that identify and protect task-relevant information against dynamic wireless distortion, ensuring reliable semantic delivery under feature-dependent channel impairments; (ii) swarm-aware radio orchestration strategies that align spectrum allocation and scheduling with collective task objectives through utility-driven coordination; and (iii) heterogeneity-aware collaborative reasoning architectures that enable progressive semantic compression and resource-adaptive inference across devices with diverse sensing, computing, and communication constraints. The research combines theoretical analysis, algorithm design, and experimental validation to advance communication-aware intelligence across layers of the wireless stack. Together, these advances provide a principled foundation for building resilient, scalable, and interoperable swarm AI infrastructures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

MCA: Genomic Tools for Detecting Short-Term Evolutionary Change in Long-Term Ecological Research on Alpine Plants

Long-term studies provide critical information about how populations, communities, and ecosystems change across environments and over time. However, ecologists often lack the tools needed to understand how genetic variation within populations contributes to the ecological dynamics observed in nature. Recent advances in genomics and sequencing methods now make it possible to study species that are well represented in ecological data sets but have characteristics that previously made complementary genetic analyses intractable. This project will develop new genomic resources for alpine bistort (Bistorta vivipara), a widespread, well-studied Arctic–alpine plant species with many of the characteristics that have limited genetic study in Arctic and alpine floras, including a large and complex genome, long lifespan, and predominantly clonal reproduction. These resources will be used to reconstruct the species’ history of range expansion and contraction across the Northern Hemisphere and characterize the distribution of genetic variation within and among populations at a long-term ecological research site where it has been monitored for more than 45 years. By integrating biotechnology and AI-enabled genomic approaches with long-term ecological research, this project will advance understanding of how evolutionary processes contribute to ecological change in a representative species of Arctic and alpine ecosystems. The project will also foster a new collaboration among scientists with complementary expertise and support the development of educational materials that introduce contemporary genomic approaches into undergraduate life-science curricula. This project advances NSF's priorities in Artificial Intelligence and Biotechnology. Genomic methods are fundamental tools in evolutionary biology that are increasingly used to address both new and longstanding questions in non-model systems. As a result, they have the potential to unravel the evolutionary processes that influence the long-term ecological dynamics of organisms that play large roles in ecosystem function. This project will develop new genomic resources to evaluate the contributions of genetic, environmental, and developmental processes in driving long-term population dynamics and evolutionary change in Bistorta vivipara, a slow-growing, long-lived, clonal forb that is widespread in Arctic and alpine ecosystems of the Northern hemisphere. The genomic tools will be used to (1) resolve the phylogeographic and evolutionary history of Bistorta vivipara, (2) characterize the distribution of genetic variation in this species across heterogeneous alpine terrain at the Niwot Ridge Long-Term Ecological Research (LTER) site in the Southern Rocky Mountains, and (3) support the establishment of a new, genetically-informed long-term experiment on Niwot Ridge that will address previously intractable questions about the evolutionary dynamics of long-lived alpine plant populations. Together, the activities and results of this project will address outstanding questions about the roles of ecological and genetic processes in driving alpine plant population dynamics and advance the integration of evolutionary biology into long-term ecological research studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

III: A Declarative Data Management System for Semantic Multimodal Workflows

The amount of data stored as videos, audio recordings, and documents is growing rapidly and now far exceeds traditional structured data. Extracting useful knowledge from these diverse sources often requires combining information across formats. For example, a traffic engineer may need to analyze camera feeds in combination with road network data to form an accurate understanding of the situation. Today, performing such analyses requires significant programming expertise, is error-prone, and can be prohibitively expensive due to the cost of running artificial intelligence models on large volumes of data. This project develops a new open-source software system that allows users to express complex analyses over videos and documents using a simple, high-level interface, while the system automatically finds efficient ways to execute them. This project will advance the science of data management and by making large-scale multimodal data analysis accessible to a broader range of users, including journalists, city officials, and transportation planners. The resulting software and benchmarks will be released publicly to support further research and education. The project also supports workforce development through new course materials at the intersection of databases and artificial intelligence, and through training of graduate students and postdoctoral researchers. Specifically, this project builds a declarative data management system for authoring, optimizing, and executing semantic workflows that span multiple unstructured data modalities, focusing initially on documents and videos. The project designs a unified data model and domain-specific language (DSL) with composable operators that operate uniformly over document collections and video frame sequences, accompanied by a low-code interface for iterative workflow development. Building on this foundation, the project develops novel optimization techniques that transfer strategies across modalities. The system will be evaluated on real-world multimodal workloads drawn from collaborations in records analysis, freeway traffic monitoring, and city council meeting analysis, using benchmarks that measure execution time, inference cost, scalability, and output quality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

EduceOps: EduceLab Operations for Next-Generation Heritage Science Infrastructure

This award supports the operations of EduceLab, a University of Kentucky facility that helps researchers study and preserve important objects, collections, places, and records from the human past. Heritage science uses imaging, computing, artificial intelligence (AI) and other methods of scientific measurement and analysis, to learn from ancient items while limiting damage to rare or fragile materials, and discovering historically relevant sites. EduceLab has already enabled the identification of previously unknown grave sites, such as those from the US Civil War. It also made it possible to read ancient scrolls that were sealed in volcano ash near Pompeii, enabling first the virtual unrolling of the material and reading of the letter through novel AI methods. Museums, libraries, universities, public agencies, and community organizations often have materials that are too fragile, too rare, too large, or too complex to study with ordinary tools and lack access to advanced instruments, computing resources, and expert guidance. EduceLab will provide a shared place where scientists, scholars, students, and heritage professionals can collaborate to answer questions about artifacts, manuscripts, biological remains, museum collections, buildings, and historic landscapes. The project will advance the national interest by generating access to high quality research infrastructure, strengthening the ability of a range of institutions to study important collections, preserving historical knowledge, training students and researchers in AI and a range of techniques, and creating public materials that help people understand why science matters for preserving our heritage. By making advanced tools and expertise available through a national user facility in Kentucky, the award will promote the progress of science and contribute to education, public understanding, and knowledge of heritage resources. EduceLab will support laboratory-based imaging and materials analysis, mobile and field-based data collection, flexible instrument configurations, and cyberinfrastructure for data movement, computation, artificial-intelligence-assisted analysis, storage, and dissemination. Users will enter the facility through an intake and onboarding process that includes eligibility review, consultation, training, safety and compliance review, and joint project design with expert staff. Projects may combine techniques such as micro computed tomography, X ray fluorescence, electron microscopy, spectral and optical imaging, ground penetrating radar, three dimensional modeling, data science, machine learning, and repository services. The facility will maintain calibration, scheduling, preventive maintenance, data quality control, project reporting, and performance metrics to support reliable access and reproducible results. Expected products include imaging and materials data, computational reconstructions, curated data packages, analytical reports, publications, public case studies, educational materials, and training programs. The project will also develop a cost recovery and partnership model to support long term operation. Together, these activities will create transferable methods for data intensive heritage science and a sustainable model for shared research infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Traveling Water Waves and Stellar Collapse: Nonlinear Analysis of Free Boundary Problems in Fluid Mechanics

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Noah Stevenson is “Traveling Water Waves and Stellar Collapse: Nonlinear Analysis of Free Boundary Problems in Fluid Mechanics”. The host institution for the fellowship is ETH Zurich and the sponsoring scientist is Mikaela Iacobelli. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Newton Polygons and Higher Quasi-F-Injective Singularities

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Jack Garzela is “Newton Polygons and Higher Quasi-F-Injective Singularities”. The host institution for the fellowship is the University of Illinois at Chicago and the sponsoring scientist is Kevin Tucker. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Coupled Multispecies Systems

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Lauren Conger is “Coupled Multispecies Systems”. The host institution for the fellowship is Stanford University and the sponsoring scientist is Lenya Ryzhik. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Foundations of Positive Geometry

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Elizabeth Pratt is “Foundations of Positive Geometry”. The host institution for the fellowship is Princeton University and the sponsoring scientist is June Huh. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Homotopy Invariants of Orbifolds

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Maxine Calle is “Homotopy Invariants of Orbifolds”. The host institution for the fellowship is Brown University and the sponsoring scientist is Thomas G. Goodwillie. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Combinatorial Models and Scaling Limits in Liouville Quantum Gravity and KPZ

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Andres Contreras Hip is “Combinatorial Models and Scaling Limits in Liouville Quantum Gravity and KPZ”. The host institution for the fellowship is Columbia University and the sponsoring scientist is Ivan Corwin. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Postdoctoral Fellowship: MSPRF: Theory and Algorithms for Scalable Decision-Making under Uncertainty

This award is made as part of the FY 2026 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines such as Artificial Intelligence and Quantum Information Science, under the mentorship of a sponsoring scientist. The title of the project for this fellowship to Graham Pash is “Theory and Algorithms for Scalable Decision-Making under Uncertainty”. The host institution for the fellowship is the Massachusetts Institute of Technology and the sponsoring scientist is Youssef Marzouk. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

REU Site: Microbiology at the host-pathogen interface

This REU Site award to The University of Iowa, located in Iowa City, IA, will support the training of 10 students for 10 weeks during the summers of 2027-2029. The goals of the program are to generate high-impact discoveries about the fundamental biology of host-microbe interactions and to train the next generation of microbial scientists. Achieving these goals is important for strengthening our bioeconomy, enhancing agriculture and mitigating the threats posed by pandemics and antibiotic resistance. The students will learn to design, conduct and interpret microbiology experiments; many will have the opportunity to present their findings at scientific conferences. Assessment of the program will use a version of the Undergraduate Research Student Self-assessment, a validated tool for measuring student learning gains. In addition, students will be tracked after the program to determine their career paths. Students will apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with NSF priorities in Artificial Intelligence and Biotechnology. The focus of the program is host interactions with bacteria, viruses, and parasites. Each student will conduct an independent laboratory research project under the joint guidance of a faculty and a graduate student or postdoctoral co-mentor from the Department of Microbiology and Immunology. Participants will be instructed in communicating their research in short talks, a written report, and a campus-wide poster session. Participants will attend workshops and seminars to broaden their understanding of microbiology and learn how to use AI tools to accelerate discovery. Additional professional development activities will cover graduate school, career options, and responsible conduct of research. Applications will include a form, a personal statement, information on career goals, research interests, two letters of recommendation, and college transcripts. Prior research experience is not required. Students will be selected by the program directors based on their fit for the program’s objectives and potential for outstanding careers involving microbiology research. More information about the program is available by visiting https://microbiology.medicine.uiowa.edu/undergraduate-education/research-opportunities/summer-undergraduate-research, or by contacting the PI (Dr. David Weiss at david-weiss@uiowa.edu) or the co-PI (Dr. Gina McGrane at regina-mcgrane@uiowa.edu). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: ARTS: Training A New Generation of Systematists and Completing the Monograph of Protium (Burseraceae) a Hyperdiverse Tropical Tree Clade

Newly developed and established methods will be integrated to complete a comprehensive study of the genus Protium (in the plant family Burseraceae, which includes frankincense and myrrh), revising the taxonomy of all 183 currently published species and describing all 52 remaining unpublished species. Protium represents one of the most important tree genera in the Americas. In Amazonia, Protium includes more than 125 species, and it serves as an excellent model system to understand the processes underlying the origins and maintenance of tropical tree diversity. New field collections in six countries will complement existing informative specimens of Protium for morphological and molecular studies that will identify and characterize all the species. Cutting-edge genomic approaches will be used to understand how each species is related to each other and the evolutionary history of the diversification in the group. New taxonomic tools such as leaf architecture (to find fingerprint-like leaf vein patterns) and near-infrared (NIR) spectral signatures, which quantifies light reflectance of dried leaves, will be integrated with Artificial Intelligence (AI) tools to develop an interactive, image-driven, multi-access electronic key to all species that will be available online. The wealth of knowledge about species and traits that will be integrated by this project will help make tropical forests more understood and better protected. Moreover, this work will be conducted as part of a training program for tropical plant systematics. Training modern taxonomists is one of the most urgent priorities for tropical biology and conservation. New systematists will be trained who can build on foundations of fieldwork and a solid background of traditional plant anatomy and morphology, but also become experts in genomics, bioinformatics, web-based interactive keys, and NIR spectroscopy, so that they can become leaders in tropical botany for the rest of this century. This project will train two PhD students and one postdoctoral scholar and give several undergraduate interns the foundation to enter the field– a small but mighty investment in the future of tropical botany. This project will contribute both a taxonomic monograph and a complete phylogeny of Protium (Burseraceae), one of the largest and most important clades of tropical trees. New field work in poorly documented regions of Panama, Ecuador, Colombia, Peru, Brazil, and Guyana will be conducted to collect silica-dried leaf material and highly informative herbarium specimens and integrated with previously collected material. The project will use hybrid enrichment sequencing (Hyb-Seq), an approach using targeted sequence capture strategies with probes designed to capture low copy nuclear loci. Use of Hyb-Seq will produce a fossil-calibrated comprehensive phylogeny of the genus Protium. Leveraging this information with data on relative abundance and functional traits of Protium will lead to an increased understanding of the factors that influence commonness and rarity in tropical forests, a critical focus of conservation strategies. A growing track record of success with Protium as a model organism in developing and applying leaf architecture, NIR spectrometry, interactive keys, GIS mapping, and other resources for effective identification and characterization of trees should have profound implications for sustainable forest management and conservation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: CyberTraining: Implementation: Small: PowerCyber: Scalable Workforce Development for AI-Enabled Power Grids

The U.S. electric power system is undergoing a rapid transformation driven by inverter-based resources, distributed energy technologies, and emerging artificial intelligence-driven electricity demand. Achieving the scientific and societal benefits of this transformation requires a research workforce that integrates power engineering with advanced cyberinfrastructure, scalable computing, data-driven modeling, and trustworthy artificial intelligence. However, these capabilities remain unevenly distributed across institutions and are not yet systematically embedded in power engineering education. This CyberTraining Implementation project, PowerCyber, addresses this gap by scaling a successful Pilot effort into a national training and community-building program for artificial intelligence-enabled power grid research. The project prepares faculty, postdoctoral researchers, and graduate students to adopt, teach, and extend modern cyberinfrastructure methods through hands-on training, reusable instructional materials, open-source computational environments, and sustained community engagement. By combining a train-the-trainer model with research-ready training pathways, PowerCyber amplifies its impact beyond direct participants and accelerates curriculum adoption across the broader power engineering community. The project strengthens the national cyberinfrastructure workforce, broadens access to advanced computational training, and supports more reproducible, scalable, and trustworthy research for future electric grids. These outcomes will help prepare engineers and researchers to build a cleaner, more reliable, and more resilient energy system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: Group Structure, Hydrodynamics, and Sensing of Squids during Collective Swimming

This study focuses on understanding the potential hydrodynamic benefits of moving in groups (schooling and shoaling) in squids (Cephalopod Mollusks). Squids are ideal for this investigation because they form groups that vary greatly in size and in the degree of coordinated movements, use both a pulsed jet and dynamic fin movements for propulsion, swim forward or backward with ease, and have distinctive sensory capabilities. Organization into groups such as schools and shoals is a common feature of many aquatic animals. Although longstanding research has shown that the value of such group behaviors may include improved foraging, more rapid communication, and heightened predator detection and avoidance, the energetic costs of swimming may also be reduced. Using 3D body tracking, volumetric flow imaging, and advanced analysis tools, three different species of squids will be studied swimming in nature, against currents in water tunnels, and while sensory systems have been temporarily disabled. The goal is to identify organizational patterns and flow conditions that improve swimming efficiency in squid schools and shoals, and to determine how sensory input is used to achieve and maintain positioning within a group. In addition to providing tools and techniques for the development of swarms of underwater autonomous vehicles, the project will train a post-doctoral fellow and undergraduate and graduate students, provide hands-on experiences for middle- and high-school students, and facilitate public education through collaborations with aquariums and science communicators. Despite the significance of schooling and shoaling in aquatic animals and the importance of cephalopods to food web dynamics and global fisheries, surprisingly little is known about how squids position themselves relative to neighbors and neighbor-induced flows in schools, whether energetic costs are reduced in schools, or what role that sensing systems play in these configurations. This study seeks to: (1) quantify squid aggregations in nature using 3D tracking cameras and software, focusing on how positioning changes with group speed and species; (2) correlate the 3D positions of animals in the group with 3D flow fields recorded from squids swimming in water tunnels and nature, with the goal of identifying propulsively efficient configurations and tracking how these integrated elements change with group speed and across species; and (3) examine the relative importance of vision and vibration-sensing via epidermal hairs for collective behaviors. Integral to these aims is the implementation of graph matching and cluster analyses to characterize animal positions and wake features. The project will be the first to quantify squid group behaviors and integrate 3D spatial tracking with 3D velocimetry to understand how group dynamics change according to speed-specific gaits. This project will address an important understudied hydrodynamic aspect of swimming – pulsed jetting and finning within aggregations – and examine whether vision and/or vibration-sensing are required for group structure. Furthermore, the project promises to develop powerful, empirical tools for identifying baseline principles for schooling across multiple taxa. The techniques developed and data collected in this project can be applied to bioinspired underwater robot swarm development, information sharing routines for autonomous systems, and graph-matching applications in artificial intelligence (AI) and social network analysis. The research and training activities align with both Biotechnology and AI priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: CyberTraining: Implementation: Small: PowerCyber: Scalable Workforce Development for AI-Enabled Power Grids

The U.S. electric power system is undergoing a rapid transformation driven by inverter-based resources, distributed energy technologies, and emerging artificial intelligence-driven electricity demand. Achieving the scientific and societal benefits of this transformation requires a research workforce that integrates power engineering with advanced cyberinfrastructure, scalable computing, data-driven modeling, and trustworthy artificial intelligence. However, these capabilities remain unevenly distributed across institutions and are not yet systematically embedded in power engineering education. This CyberTraining Implementation project, PowerCyber, addresses this gap by scaling a successful Pilot effort into a national training and community-building program for artificial intelligence-enabled power grid research. The project prepares faculty, postdoctoral researchers, and graduate students to adopt, teach, and extend modern cyberinfrastructure methods through hands-on training, reusable instructional materials, open-source computational environments, and sustained community engagement. By combining a train-the-trainer model with research-ready training pathways, PowerCyber amplifies its impact beyond direct participants and accelerates curriculum adoption across the broader power engineering community. The project strengthens the national cyberinfrastructure workforce, broadens access to advanced computational training, and supports more reproducible, scalable, and trustworthy research for future electric grids. These outcomes will help prepare engineers and researchers to build a cleaner, more reliable, and more resilient energy system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: Group Structure, Hydrodynamics, and Sensing of Squids during Collective Swimming

This study focuses on understanding the potential hydrodynamic benefits of moving in groups (schooling and shoaling) in squids (Cephalopod Mollusks). Squids are ideal for this investigation because they form groups that vary greatly in size and in the degree of coordinated movements, use both a pulsed jet and dynamic fin movements for propulsion, swim forward or backward with ease, and have distinctive sensory capabilities. Organization into groups such as schools and shoals is a common feature of many aquatic animals. Although longstanding research has shown that the value of such group behaviors may include improved foraging, more rapid communication, and heightened predator detection and avoidance, the energetic costs of swimming may also be reduced. Using 3D body tracking, volumetric flow imaging, and advanced analysis tools, three different species of squids will be studied swimming in nature, against currents in water tunnels, and while sensory systems have been temporarily disabled. The goal is to identify organizational patterns and flow conditions that improve swimming efficiency in squid schools and shoals, and to determine how sensory input is used to achieve and maintain positioning within a group. In addition to providing tools and techniques for the development of swarms of underwater autonomous vehicles, the project will train a post-doctoral fellow and undergraduate and graduate students, provide hands-on experiences for middle- and high-school students, and facilitate public education through collaborations with aquariums and science communicators. Despite the significance of schooling and shoaling in aquatic animals and the importance of cephalopods to food web dynamics and global fisheries, surprisingly little is known about how squids position themselves relative to neighbors and neighbor-induced flows in schools, whether energetic costs are reduced in schools, or what role that sensing systems play in these configurations. This study seeks to: (1) quantify squid aggregations in nature using 3D tracking cameras and software, focusing on how positioning changes with group speed and species; (2) correlate the 3D positions of animals in the group with 3D flow fields recorded from squids swimming in water tunnels and nature, with the goal of identifying propulsively efficient configurations and tracking how these integrated elements change with group speed and across species; and (3) examine the relative importance of vision and vibration-sensing via epidermal hairs for collective behaviors. Integral to these aims is the implementation of graph matching and cluster analyses to characterize animal positions and wake features. The project will be the first to quantify squid group behaviors and integrate 3D spatial tracking with 3D velocimetry to understand how group dynamics change according to speed-specific gaits. This project will address an important understudied hydrodynamic aspect of swimming – pulsed jetting and finning within aggregations – and examine whether vision and/or vibration-sensing are required for group structure. Furthermore, the project promises to develop powerful, empirical tools for identifying baseline principles for schooling across multiple taxa. The techniques developed and data collected in this project can be applied to bioinspired underwater robot swarm development, information sharing routines for autonomous systems, and graph-matching applications in artificial intelligence (AI) and social network analysis. The research and training activities align with both Biotechnology and AI priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

How temporal niche partitioning and growth on urea selects for cyanobacteria in fresh waters

Harmful cyanobacterial threaten drinking water, fisheries, recreation, and local economies worldwide. Microcystis, a type of algae, often dominates these blooms and can produce microcystin, a toxin harmful to both people and ecosystems. While nutrient pollution is known to drive blooms, a key question remains: why do certain bloom-forming species outcompete other algae? This project examines the role of Microcystis under specific bloom conditions. It is hypothesized that Microcystis gains a competitive advantage by using urea, a nitrogen-rich compound common in agricultural watersheds, not only as a nitrogen source but also to help meet its carbon demands. Researchers will test how this process changes over the course of the day, how it affects competition with other algae, and whether it helps explain the persistence and intensification of harmful blooms. To address these questions, the investigators will combine laboratory experiments, field studies in Lake Erie, and advanced analyses of nutrient and energy use, together with AI-assisted predictive modeling. By linking cellular metabolism to bloom dynamics, this project will improve understanding of how blooms form, persist, and respond to the environment. The results will support better bloom forecasting and management, advance understanding of carbon-flow energetics with potential relevance to bioenergy, train students across multiple disciplines, and engage the public through citizen science and K–12 education. This project advances NSF’s priorities in Biotechnology and Artificial Intelligence. Harmful cyanobacterial blooms are increasing globally, yet the mechanisms though which conditions in the environment select for specific bloom-forming taxa remain poorly resolved. While phosphorus and nitrogen broadly constrain phytoplankton biomass, nitrogen speciation is increasingly recognized as a key driver of community composition. Bloom-forming cyanobacteria further modify their environment by depleting dissolved nutrients and elevating pH to levels that suppress competing phototrophs. Microcystis is likely to exploit these self-generated conditions through diel temporal niche partitioning, using urea as both a carbon and nitrogen source during peak daylight, when bloom-driven alkalization reduces dissolved CO2 and promotes nitrogen loss via ammonia volatilization. This metabolism is supported by a shift toward cyclic photophosphorylation late in the solar day, enabling ATP generation when linear photosynthesis is constrained. This project will quantify the efficacy of this strategy, determine how it alters competition with co-occurring phototrophs, and incorporate these mechanisms into predictive models linking cell physiology to community outcomes. Preliminary observations indicate that urea-grown Microcystis can accumulate more biomass than nitrate-grown cells while exhibiting reduced PSII activity, consistent with alternative energetic pathways. To test these ideas, investigators will integrate continuous cultures, field surveys, cell enumeration, image-based analyses, transcriptomics, metabolomics, and AI-driven mechanistic modeling. The resulting framework will resolve how nutrient form, carbonate chemistry, cellular energetics, and interspecific competition interact to determine when and why Microcystis dominates freshwater systems. These advances will improve mechanistic forecasting of harmful blooms and provide interdisciplinary graduate and undergraduate training in laboratory, field, multi-omic, and quantitative modeling approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: SHF: Scalable Cross-Layer Co-Design for Stable Mixed-Precision Acceleration

The most consequential computing workloads today all face substantial challenges in scaling up computation while delivering energy efficiency, e.g., physical artificial intelligence (AI) workloads such as real-time robot control, large-scale neural network training, power grid management, quantum physics simulation, and other scientific computing. Reducing numerical precision (i.e., using smaller, more concise representations of values) is one of the most effective levers for improving performance and energy efficiency across computing software and hardware, but it also runs the risk of catastrophic numerical failure. Today, most solutions navigate precision tradeoffs through ad hoc trial-and-error, lacking approaches that can ensure stability. This project addresses this gap with a cross-layer co-design framework that leverages two different forms of analysis, numerical precision analysis and control theory, to systematically generate streamlined computer hardware designs and optimized code with provable stability guarantees. Combined, these approaches enable the development of safe and efficient real-world computing solutions. This project takes an interdisciplinary approach, systematically interfacing numerical analysis and control theory with high-performance accelerator hardware-software co-design to establish a mixed-precision design framework that delivers both stability and performance. This project will: (1) design a unified high-level synthesis framework that uses numerical precision analysis to drive the creation of stable mixed-precision custom accelerator hardware with precision-aware resource allocation for hardware synthesis, as well as architecture-aware vectorized code generation for central processing units (CPU) and graphics processing units (GPU); (2) extend the framework to the nondeterministic, iterative algorithms that dominate physical AI workloads whose convergence behavior cannot be captured by static precision analysis alone but are well characterized by contraction analysis and noise-budget reasoning; and (3) make this framework practical at scale by accelerating the underlying analysis infrastructure through GPU-parallelized interval arithmetic, modular analysis, and code generation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: Group Structure, Hydrodynamics, and Sensing of Squids during Collective Swimming

This study focuses on understanding the potential hydrodynamic benefits of moving in groups (schooling and shoaling) in squids (Cephalopod Mollusks). Squids are ideal for this investigation because they form groups that vary greatly in size and in the degree of coordinated movements, use both a pulsed jet and dynamic fin movements for propulsion, swim forward or backward with ease, and have distinctive sensory capabilities. Organization into groups such as schools and shoals is a common feature of many aquatic animals. Although longstanding research has shown that the value of such group behaviors may include improved foraging, more rapid communication, and heightened predator detection and avoidance, the energetic costs of swimming may also be reduced. Using 3D body tracking, volumetric flow imaging, and advanced analysis tools, three different species of squids will be studied swimming in nature, against currents in water tunnels, and while sensory systems have been temporarily disabled. The goal is to identify organizational patterns and flow conditions that improve swimming efficiency in squid schools and shoals, and to determine how sensory input is used to achieve and maintain positioning within a group. In addition to providing tools and techniques for the development of swarms of underwater autonomous vehicles, the project will train a post-doctoral fellow and undergraduate and graduate students, provide hands-on experiences for middle- and high-school students, and facilitate public education through collaborations with aquariums and science communicators. Despite the significance of schooling and shoaling in aquatic animals and the importance of cephalopods to food web dynamics and global fisheries, surprisingly little is known about how squids position themselves relative to neighbors and neighbor-induced flows in schools, whether energetic costs are reduced in schools, or what role that sensing systems play in these configurations. This study seeks to: (1) quantify squid aggregations in nature using 3D tracking cameras and software, focusing on how positioning changes with group speed and species; (2) correlate the 3D positions of animals in the group with 3D flow fields recorded from squids swimming in water tunnels and nature, with the goal of identifying propulsively efficient configurations and tracking how these integrated elements change with group speed and across species; and (3) examine the relative importance of vision and vibration-sensing via epidermal hairs for collective behaviors. Integral to these aims is the implementation of graph matching and cluster analyses to characterize animal positions and wake features. The project will be the first to quantify squid group behaviors and integrate 3D spatial tracking with 3D velocimetry to understand how group dynamics change according to speed-specific gaits. This project will address an important understudied hydrodynamic aspect of swimming – pulsed jetting and finning within aggregations – and examine whether vision and/or vibration-sensing are required for group structure. Furthermore, the project promises to develop powerful, empirical tools for identifying baseline principles for schooling across multiple taxa. The techniques developed and data collected in this project can be applied to bioinspired underwater robot swarm development, information sharing routines for autonomous systems, and graph-matching applications in artificial intelligence (AI) and social network analysis. The research and training activities align with both Biotechnology and AI priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: SHF: Scalable Cross-Layer Co-Design for Stable Mixed-Precision Acceleration

The most consequential computing workloads today all face substantial challenges in scaling up computation while delivering energy efficiency, e.g., physical artificial intelligence (AI) workloads such as real-time robot control, large-scale neural network training, power grid management, quantum physics simulation, and other scientific computing. Reducing numerical precision (i.e., using smaller, more concise representations of values) is one of the most effective levers for improving performance and energy efficiency across computing software and hardware, but it also runs the risk of catastrophic numerical failure. Today, most solutions navigate precision tradeoffs through ad hoc trial-and-error, lacking approaches that can ensure stability. This project addresses this gap with a cross-layer co-design framework that leverages two different forms of analysis, numerical precision analysis and control theory, to systematically generate streamlined computer hardware designs and optimized code with provable stability guarantees. Combined, these approaches enable the development of safe and efficient real-world computing solutions. This project takes an interdisciplinary approach, systematically interfacing numerical analysis and control theory with high-performance accelerator hardware-software co-design to establish a mixed-precision design framework that delivers both stability and performance. This project will: (1) design a unified high-level synthesis framework that uses numerical precision analysis to drive the creation of stable mixed-precision custom accelerator hardware with precision-aware resource allocation for hardware synthesis, as well as architecture-aware vectorized code generation for central processing units (CPU) and graphics processing units (GPU); (2) extend the framework to the nondeterministic, iterative algorithms that dominate physical AI workloads whose convergence behavior cannot be captured by static precision analysis alone but are well characterized by contraction analysis and noise-budget reasoning; and (3) make this framework practical at scale by accelerating the underlying analysis infrastructure through GPU-parallelized interval arithmetic, modular analysis, and code generation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAIG: Advancing AI atmospheric emulators through rigorous theoretical evaluation and uncertainty quantification

Accurately predicting how regional weather extremes like heat waves and droughts will evolve in the coming decades is essential for national preparedness and resilience. Traditional physics-based Earth system models (ESMs) exhibit discrepancies when compared to available observations, especially in regional trends, limiting their usefulness. Artificial intelligence (AI) models trained directly on observational data offer a promising alternative, having already shown an ability to predict regional extremes more skillfully than traditional models in some cases. However, before these AI tools can be trusted to forecast future conditions — particularly conditions never seen in the historical record — scientists need rigorous ways to evaluate their reliability and quantify their uncertainty. This project brings together geoscientists, statisticians, and applied mathematicians to rigorously test and improve AI-based atmospheric models, making them more trustworthy for predicting future regional conditions. The resulting tools and open-source software will benefit researchers, policymakers, and communities seeking to understand and prepare for evolving regional weather hazards, while also training the next generation of scientists in this interdisciplinary field. This project will advance AI-based atmospheric emulators through three interconnected research thrusts: (1) rigorous theoretical evaluation of how well AI models capture extremes relative to physics-based models and observations; (2) development of robust methods, including conformal prediction, for quantifying aleatoric and epistemic uncertainty in multi-decadal AI emulator predictions; and (3) construction of a hierarchy of physically consistent, coupled atmosphere-land AI emulators that incorporate physical coupling constraints. The work will clarify the roles of extrapolation versus translocation in regional discrepancies and produce emulators with quantified uncertainty suitable for out-of-sample and out-of-distribution regional prediction. The interdisciplinary team will train PhD students and undergraduate researchers, contribute to the Data Science Institute's AI+Science summer school, and release code and emulators through open-access repositories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

RUI: Enabling phylogenomic research on microbial eukaryotes with EukPhylo

The vast majority of species on Earth are microbial, including most eukaryotes (i.e. species whose cells have nuclei, like humans). Yet, few tools exist to study diverse microeukaryotes (e.g., flagellates, amoebae), largely because most bioinformatic pipelines focus only on data from bacteria or animals. Many existing platforms also require considerable technical expertise, preventing access for many scientists, and as a result, science in the USA does not efficiently leverage data from microbial species. The work proposed addresses this problem by developing a comprehensive bioinformatic platform, EukPhylogenomics (EukPhylo), for the analysis of genome-scale data from microbial species. The team will expand and refine EukPhylo, an open-source pipeline created at Smith College, via the integration of best practices from software engineering. EukPhylo will generate robust, reproducible, and standardized results while also enabling community members with variable bioinformatic skills to analyze diverse datatypes. The proposal also intersects with the NSF priority to advance artificial intelligence (AI): both AI and machine learning will be integrated in the development and deployment of the toolkit, improving reliability and reproducibility. An additional outcome will be the growth of the workforce with expertise in bioinformatics/biotechnology, achieved by training undergraduate and graduate students and a postdoctoral fellow. Other products will include tutorials (written and video) as well as public-facing training sessions. Combined, these efforts will produce a user-friendly toolkit that will transform studies of microscopic species, allowing hypothesis testing on ecologically important groups as well as on the many microbial species that cause diseases in humans. Through the expansion and democratization of the EukPhylo cyberinfrastructure, the proposed work will create reproducible and widely accessible bioinformatics workflows that facilitate the curation of data and testing of hypotheses involving eukaryotic microorganisms. The resulting infrastructure will be open-source and fully containerized for easy installation and will come with documentation in the form of written (e.g., publications plus GitHub wiki) and video trainings. EukPhylo will be tested through collaboration with labs throughout the US and beyond while building educational/training modules and tutorials for an expanded range of supported analyses. The work addresses three aims, the first of which is to transform EukPhylo into an easy-to-use pipeline for the analysis of eukaryotic gene families, leveraging artificial intelligence and machine learning for the reproducible detection of both contaminants and gene transfers. The second aim is to expand the functionality of EukPhylo by adding tools to support additional input data types (e.g., metagenomic and metatranscriptomic) and incorporating new analysis modules (e.g., population-scale analyses, detecting signatures of natural selection). The third aim is to reduce computational barriers for researchers working across disciplines through workshops and the generation of educational materials. In support of all aims, the project will train multiple undergraduate and graduate students, and at least one postdoctoral fellow. Additional outcomes will include educational modules/tutorials for training, which will be aimed at a diversity of levels. Hence the project will transform analyses of the wealth of genome-scale data from microeukaryotic species. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Collaborative Research: Group Structure, Hydrodynamics, and Sensing of Squids during Collective Swimming

This study focuses on understanding the potential hydrodynamic benefits of moving in groups (schooling and shoaling) in squids (Cephalopod Mollusks). Squids are ideal for this investigation because they form groups that vary greatly in size and in the degree of coordinated movements, use both a pulsed jet and dynamic fin movements for propulsion, swim forward or backward with ease, and have distinctive sensory capabilities. Organization into groups such as schools and shoals is a common feature of many aquatic animals. Although longstanding research has shown that the value of such group behaviors may include improved foraging, more rapid communication, and heightened predator detection and avoidance, the energetic costs of swimming may also be reduced. Using 3D body tracking, volumetric flow imaging, and advanced analysis tools, three different species of squids will be studied swimming in nature, against currents in water tunnels, and while sensory systems have been temporarily disabled. The goal is to identify organizational patterns and flow conditions that improve swimming efficiency in squid schools and shoals, and to determine how sensory input is used to achieve and maintain positioning within a group. In addition to providing tools and techniques for the development of swarms of underwater autonomous vehicles, the project will train a post-doctoral fellow and undergraduate and graduate students, provide hands-on experiences for middle- and high-school students, and facilitate public education through collaborations with aquariums and science communicators. Despite the significance of schooling and shoaling in aquatic animals and the importance of cephalopods to food web dynamics and global fisheries, surprisingly little is known about how squids position themselves relative to neighbors and neighbor-induced flows in schools, whether energetic costs are reduced in schools, or what role that sensing systems play in these configurations. This study seeks to: (1) quantify squid aggregations in nature using 3D tracking cameras and software, focusing on how positioning changes with group speed and species; (2) correlate the 3D positions of animals in the group with 3D flow fields recorded from squids swimming in water tunnels and nature, with the goal of identifying propulsively efficient configurations and tracking how these integrated elements change with group speed and across species; and (3) examine the relative importance of vision and vibration-sensing via epidermal hairs for collective behaviors. Integral to these aims is the implementation of graph matching and cluster analyses to characterize animal positions and wake features. The project will be the first to quantify squid group behaviors and integrate 3D spatial tracking with 3D velocimetry to understand how group dynamics change according to speed-specific gaits. This project will address an important understudied hydrodynamic aspect of swimming – pulsed jetting and finning within aggregations – and examine whether vision and/or vibration-sensing are required for group structure. Furthermore, the project promises to develop powerful, empirical tools for identifying baseline principles for schooling across multiple taxa. The techniques developed and data collected in this project can be applied to bioinspired underwater robot swarm development, information sharing routines for autonomous systems, and graph-matching applications in artificial intelligence (AI) and social network analysis. The research and training activities align with both Biotechnology and AI priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Integrated Experimental and Computational Approach to Unveil Mechanisms of Deformation in Fibrous Biological Materials

Human tissues are composed of fibers that deform in response to forces produced by the resident cells. In turn, those deformations provide cues to the cells that trigger regeneration, repair, and progression of disease. For example, in cancer cell invasion, permanent deformations can produce tunnels that cancer cells use to move and spread throughout the human body. Although it is well recognized that permanent deformations occur in biological tissues, the underlying causes are unclear. Furthermore, it is unclear how different chemical connections between the fibers, called crosslinkers, affect permanent deformations. This project will answer these questions using a combination of experiments and computational modeling. The experiments will precisely deform tissue mimics while simultaneously capturing images of the fibers with a microscope, which will identify exactly how the fibers deform relative to each other. The computational model will simulate the physical deformations of each fiber. Computational results will be combined with the experimental data using an artificial intelligence (AI)-based method, which will enable accurate prediction of how tissues permanently deform in response to forces. The predictions will be tested in further experiments that mimic invasion of cancer cells. In addition to providing essential knowledge required to predict and target cancer cell invasion, this project will enhance STEM education and improve the STEM workforce through a combination of a summer computing program for high school students, an exhibit in an annual Engineering Expo, and a Research Experience for Teachers program, in which high school science teachers will participate in the research and build hands-on activities to implement in the classroom. This project will advance understanding of the underlying mechanisms for plastic deformation in the extracellular matrix. The experiments will deform networks of collagen I fibers and simultaneously use confocal microscopy to quantify stretching of individual fibers and relative sliding between fibers. A mechanics-based fiber network model will be closely integrated with the experimental results to infer model parameters and quantify the interplay between fiber stretching and sliding. The experiments and modeling will be repeated for collagen networks in the presence of different crosslinkers, which will identify how the different crosslinkers affect the underlying mechanisms for plastic deformation. Further, model predictions will be tested in a set of experiments in a cancer cell invasion assay, to quantify the effects of fiber density, alignment, and crosslinking on cell-induced plasticity and cell migration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Moritz Grosse-Wentrup

We have an open position for a postdoctoral researcher with experience in brain-computer interfacing and artificial intelligence to further advance our new class of Brain-Artificial Intelligence (BAI) interfaces. A central part of your research would be to further develop our BAI for single-unit data recorded in language areas of a post-stroke aphasia patient, a project we carry out in close collaboration with the Translational NeuroTechnology Lab at TUM, headed by Simon Jacob.

A personal journey on understanding intelligence

The focus of this talk is not about my research in AI or Robotics but my own journey on trying to do research and understand intelligence in a rapidly evolving research landscape. I will trace my path from conducting early-stage research during graduate school, to working on practical solutions within a startup environment, and finally to my current role where I participate in more structured research at a major tech company. Through these varied experiences, I will provide different perspectives on research and talk about how my core beliefs on intelligence have changed and sometimes even been compromised. There are no lessons to be learned from my stories, but hopefully they will be entertaining.

Short and Synthetically Distort: Investor Reactions to Deepfake Financial News

Recent advances in artificial intelligence have led to new forms of misinformation, including highly realistic “deepfake” synthetic media. We conduct three experiments to investigate how and why retail investors react to deepfake financial news. Results from the first two experiments provide evidence that investors use a “realism heuristic,” responding more intensely to audio and video deepfakes as their perceptual realism increases. In the third experiment, we introduce an intervention to prompt analytical thinking, varying whether participants make analytical judgments about credibility or intuitive investment judgments. When making intuitive investment judgments, investors are strongly influenced by both more and less realistic deepfakes. When making analytical credibility judgments, investors are able to discern the non-credibility of less realistic deepfakes but struggle with more realistic deepfakes. Thus, while analytical thinking can reduce the impact of less realistic deepfakes, highly realistic deepfakes are able to overcome this analytical scrutiny. Our results suggest that deepfake financial news poses novel threats to investors.

Memory Decoding Journal Club: Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating

Join us for the Memory Decoding Journal Club, a collaboration between the Carboncopies Foundation and BPF Aspirational Neuroscience. This month, we're diving into a groundbreaking paper: 'Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating' by Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong from Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College. Dr. Randal Koene will guide us through an engaging discussion on these exciting findings and their implications for neuroscience and memory research.

Active Predictive Coding and the Primacy of Actions in Natural and Artificial Intelligence

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

The Brain Prize winners' webinar

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

LLMs and Human Language Processing

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech-based LLMs excel at explaining early auditory cortical responses. However, purely low-level features can drive part of these alignments, complicating interpretations. New methods, including perturbation analyses, highlight which linguistic variables matter for each cortical area and time scale. Further, “brain tuning” of LLMs—fine-tuning on measured neural signals—can improve semantic representations and downstream language tasks. Despite open questions about interpretability and exact neural mechanisms, these results demonstrate that LLMs provide a promising framework for probing the computations underlying human language comprehension and production at multiple spatiotemporal scales.

Llama 3.1 Paper: The Llama Family of Models

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Trends in NeuroAI - Brain-like topography in transformers (Topoformer)

Dr. Nicholas Blauch will present on his work "Topoformer: Brain-like topographic organization in transformer language models through spatial querying and reweighting". Dr. Blauch is a postdoctoral fellow in the Harvard Vision Lab advised by Talia Konkle and George Alvarez. Paper link: https://openreview.net/pdf?id=3pLMzgoZSA Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Generative models for video games (rescheduled)

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Generative models for video games

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Trends in NeuroAI - Unified Scalable Neural Decoding (POYO)

Lead author Mehdi Azabou will present on his work "POYO-1: A Unified, Scalable Framework for Neural Population Decoding" (https://poyo-brain.github.io/). Mehdi is an ML PhD student at Georgia Tech advised by Dr. Eva Dyer. Paper link: https://arxiv.org/abs/2310.16046 Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Reimagining the neuron as a controller: A novel model for Neuroscience and AI

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.

Trends in NeuroAI - Meta's MEG-to-image reconstruction

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: Brain-optimized inference improves reconstructions of fMRI brain activity Abstract: The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively refine these reconstructions using a brain-optimized encoding model that maps images to brain activity. At each iteration, we sample a small library of images from an image distribution (a diffusion model) conditioned on a seed reconstruction from the previous iteration. We select those that best approximate the measured brain activity when passed through our encoding model, and use these images for structural guidance during the generation of the small library in the next iteration. We reduce the stochasticity of the image distribution at each iteration, and stop when a criterion on the "width" of the image distribution is met. We show that when this process is applied to recent decoding methods, it outperforms the base decoding method as measured by human raters, a variety of image feature metrics, and alignment to brain activity. These results demonstrate that reconstruction quality can be significantly improved by explicitly aligning decoding distributions to brain activity distributions, even when the seed reconstruction is output from a state-of-the-art decoding algorithm. Interestingly, the rate of refinement varies systematically across visual cortex, with earlier visual areas generally converging more slowly and preferring narrower image distributions, relative to higher-level brain areas. Brain-optimized inference thus offers a succinct and novel method for improving reconstructions and exploring the diversity of representations across visual brain areas. Speaker: Reese Kneeland is a Ph.D. student at the University of Minnesota working in the Naselaris lab. Paper link: https://arxiv.org/abs/2312.07705

Trends in NeuroAI - Meta's MEG-to-image reconstruction

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution (≈0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution (≈5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that MEG signals primarily contain high-level visual features, whereas the same approach applied to 7T fMRI also recovers low-level features. Overall, these results provide an important step towards the decoding - in real time - of the visual processes continuously unfolding within the human brain. Speaker: Dr. Paul Scotti (Stability AI, MedARC) Paper link: https://arxiv.org/abs/2310.19812

Trends in NeuroAI - SwiFT: Swin 4D fMRI Transformer

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

Use of Artificial Intelligence by Law Enforcement Authorities in the EU

Recently, artificial intelligence (AI) has become a global priority. Rapid and ongoing technological advancements in AI have prompted European legislative initiatives to regulate its use. In April 2021, the European Commission submitted a proposal for a Regulation that would harmonize artificial intelligence rules across the EU, including the law enforcement sector. Consequently, law enforcement officials await the outcome of the ongoing inter-institutional negotiations (trilogue) with great anticipation, as it will define how to capitalize on the opportunities presented by AI and how to prevent criminals from abusing this emergent technology.

BrainLM Journal Club

Connor Lane will lead a journal club on the recent BrainLM preprint, a foundation model for fMRI trained using self-supervised masked autoencoder training. Preprint: https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1 Tweeprint: https://twitter.com/david_van_dijk/status/1702336882301112631?t=Q2-U92-BpJUBh9C35iUbUA&s=19

Foundation models in ophthalmology

Abstract to follow.

Cognitive Computational Neuroscience 2023

CCN is an annual conference that serves as a forum for cognitive science, neuroscience, and artificial intelligence researchers dedicated to understanding the computations that underlie complex behavior.

Algonauts 2023 winning paper journal club (fMRI encoding models)

Algonauts 2023 was a challenge to create the best model that predicts fMRI brain activity given a seen image. Huze team dominated the competition and released a preprint detailing their process. This journal club meeting will involve open discussion of the paper with Q/A with Huze. Paper: https://arxiv.org/pdf/2308.01175.pdf Related paper also from Huze that we can discuss: https://arxiv.org/pdf/2307.14021.pdf

1.8 billion regressions to predict fMRI (journal club)

Public journal club where this week Mihir will present on the 1.8 billion regressions paper (https://www.biorxiv.org/content/10.1101/2022.03.28.485868v2), where the authors use hundreds of pretrained model embeddings to best predict fMRI activity.

In search of the unknown: Artificial intelligence and foraging

Diverse applications of artificial intelligence and mathematical approaches in ophthalmology

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.

Consciousness in the age of mechanical minds

We are now clearly entering a new age in our relationship with machines. The power of AI natural language processors and image generators has rapidly exceeded the expectations of even those who developed them. Serious questions are now being asked about the extent to which machines could become — or perhaps already are — sentient or conscious. Do AI machines understand the instructions they are given and the answers they provide? In this talk I will consider the prospects for conscious machines, by which I mean machines that have feelings, know about their own existence, and about ours. I will suggest that the recent focus on information processing in models of consciousness, in which the brain is treated as a kind of digital computer, have mislead us about the nature of consciousness and how it is produced in biological systems. Treating the brain as an energy processing system is more likely to yield answers to these fundamental questions and help us understand how and when machines might become minds.

How AI is advancing Clinical Neuropsychology and Cognitive Neuroscience

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.

Deep learning applications in ophthalmology

Deep learning techniques have revolutionized the field of image analysis and played a disruptive role in the ability to quickly and efficiently train image analysis models that perform as well as human beings. This talk will cover the beginnings of the application of deep learning in the field of ophthalmology and vision science, and cover a variety of applications of using deep learning as a method for scientific discovery and latent associations.

AI for Multi-centre Epilepsy Lesion Detection on MRI

Epilepsy surgery is a safe but underutilised treatment for drug-resistant focal epilepsy. One challenge in the presurgical evaluation of patients with drug-resistant epilepsy are patients considered “MRI negative”, i.e. where a structural brain abnormality has not been identified on MRI. A major pathology in “MRI negative” patients is focal cortical dysplasia (FCD), where lesions are often small or subtle and easily missed by visual inspection. In recent years, there has been an explosion in artificial intelligence (AI) research in the field of healthcare. Automated FCD detection is an area where the application of AI may translate into significant improvements in the presurgical evaluation of patients with focal epilepsy. I will provide an overview of our automated FCD detection work, the Multicentre Epilepsy Lesion Detection (MELD) project and how AI algorithms are beginning to be integrated into epilepsy presurgical planning at Great Ormond Street Hospital and elsewhere around the world. Finally, I will discuss the challenges and future work required to bring AI to the forefront of care for patients with epilepsy.

Does subjective time interact with the heart rate?

Decades of research have investigated the relationship between perception of time and heart rate with often mixed results. In search of such a relationship, I will present my far journey between two projects: from time perception in the realistic VR experience of crowded subway trips in the order of minutes (project 1); to the perceived duration of sub-second white noise tones (project 2). Heart rate had multiple concurrent relationships with subjective temporal distortions for the sub-second tones, while the effects were lacking or weak for the supra-minute subway trips. What does the heart have to do with sub-second time perception? We addressed this question with a cardiac drift-diffusion model, demonstrating the sensory accumulation of temporal evidence as a function of heart rate.

On the link between conscious function and general intelligence in humans and machines

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this talk, I will examine the validity and potential application of this seemingly intuitive link between consciousness and intelligence. I will do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST), and demonstrating that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we will turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Given this apparent trend, I will use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a unified model. I believe that doing so can enable the development of artificial agents which are not only more generally intelligent but are also consistent with multiple current theories of conscious function.

Do large language models solve verbal analogies like children do?

Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s home). Interestingly, despite state-of-the-art AI-language models having superhuman encyclopedic knowledge and superior memory and computational power, our pilot studies show that these large language models often make mistakes providing associative rather than relational responses to verbal analogies. For example, when we asked four- to eight-year-olds to solve the analogy “body is to feet as tree is to …?” they responded “roots” without hesitation, but large language models tend to provide more associative responses such as “leaves”. In this study we examine the similarities and differences between children's and six large language models' (Dutch/multilingual models: RobBERT, BERT-je, M-BERT, GPT-2, M-GPT, Word2Vec and Fasttext) responses to verbal analogies extracted from an online adaptive learning environment, where >14,000 7-12 year-olds from the Netherlands solved 20 or more items from a database of 900 Dutch language verbal analogies.

Lifelong Learning AI via neuro inspired solutions

AI embedded in real systems, such as in satellites, robots and other autonomous devices, must make fast, safe decisions even when the environment changes, or under limitations on the available power; to do so, such systems must be adaptive in real time. To date, edge computing has no real adaptivity – rather the AI must be trained in advance, typically on a large dataset with much computational power needed; once fielded, the AI is frozen: It is unable to use its experience to operate if environment proves outside its training or to improve its expertise; and worse, since datasets cannot cover all possible real-world situations, systems with such frozen intelligent control are likely to fail. Lifelong Learning is the cutting edge of artificial intelligence - encompassing computational methods that allow systems to learn in runtime and incorporate learning for application in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature, and in particular neuroscience, for its underlying principles and mechanisms and then translates them to this new technology. Our presentation will introduce a number of state-of-the-art approaches to achieve AI adaptive learning, including from the DARPA’s L2M program and subsequent developments. Many environments are affected by temporal changes, such as the time of day, week, season, etc. A way to create adaptive systems which are both small and robust is by making them aware of time and able to comprehend temporal patterns in the environment. We will describe our current research in temporal AI, while also considering power constraints.

Associative memory of structured knowledge

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture (VSA) scheme. We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

What do neurons want?

AI-assisted language learning: Assessing learners who memorize and reason by analogy

Vocabulary learning applications like Duolingo have millions of users around the world, but yet are based on very simple heuristics to choose teaching material to provide to their users. In this presentation, we will discuss the possibility to develop more advanced artificial teachers, which would be based on modeling of the learner’s inner characteristics. In the case of teaching vocabulary, understanding how the learner memorizes is enough. When it comes to picking grammar exercises, it becomes essential to assess how the learner reasons, in particular by analogy. This second application will illustrate how analogical and case-based reasoning can be employed in an alternative way in education: not as the teaching algorithm, but as a part of the learner’s model.

Learning static and dynamic mappings with local self-supervised plasticity

Animals exhibit remarkable learning capabilities with little direct supervision. Likewise, self-supervised learning is an emergent paradigm in artificial intelligence, closing the performance gap to supervised learning. In the context of biology, self-supervised learning corresponds to a setting where one sense or specific stimulus may serve as a supervisory signal for another. After learning, the latter can be used to predict the former. On the implementation level, it has been demonstrated that such predictive learning can occur at the single neuron level, in compartmentalized neurons that separate and associate information from different streams. We demonstrate the power such self-supervised learning over unsupervised (Hebb-like) learning rules, which depend heavily on stimulus statistics, in two examples: First, in the context of animal navigation where predictive learning can associate internal self-motion information always available to the animal with external visual landmark information, leading to accurate path-integration in the dark. We focus on the well-characterized fly head direction system and show that our setting learns a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Second, we show that incorporating global gating by reward prediction errors allows the same setting to learn conditioning at the neuronal level with mixed selectivity. At its core, conditioning entails associating a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Solving the generic problem of pattern-to-pattern associations naturally leads to emergent cognitive phenomena like blocking, overshadowing, saliency effects, extinction, interstimulus interval effects etc. Surprisingly, we find that the same network offers a reductionist mechanism for causal inference by resolving the post hoc, ergo propter hoc fallacy.

A Framework for a Conscious AI: Viewing Consciousness through a Theoretical Computer Science Lens

We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. However, the CTM is not a standard Turing Machine. It’s not the input-output map that gives the CTM its feeling of consciousness, but what’s under the hood. Nor is the CTM a standard GW model. In addition to its architecture, what gives the CTM its feeling of consciousness is its predictive dynamics (cycles of prediction, feedback and learning), its internal multi-modal language Brainish, and certain special Long Term Memory (LTM) processors, including its Inner Speech and Model of the World processors. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature. Reference. L. Blum and M. Blum, "A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine," PNAS, vol. 119, no. 21, 24 May 2022. https://www.pnas.org/doi/epdf/10.1073/pnas.2115934119

Feedforward and feedback processes in visual recognition

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

Careers for neuroscience in Artificial Intelligence

The purpose of this event is twofold: to raise awareness of careers in AI to neuroscience postgraduate and Early Career Researchers (ECRs), and to give the chance for commercial organisations to acquire and diversify their talent pool.  We know that our early career members are highly motivated and interested in different career pathways, and wish to help them fulfil their ambitions. This will be a hybrid event held in person at Arca Blanca, Covent Garden, London and also available online. FREE for BNA members!

Faking emotions and a therapeutic role for robots and chatbots: Ethics of using AI in psychotherapy

In recent years, there has been a proliferation of social robots and chatbots that are designed so that users make an emotional attachment with them. This talk will start by presenting the first such chatbot, a program called Eliza designed by Joseph Weizenbaum in the mid 1960s. Then we will look at some recent robots and chatbots with Eliza-like interfaces and examine their benefits as well as various ethical issues raised by deploying such systems.

Forensic use of face recognition systems for investigation

With the increasing development of automatic systems and artificial intelligence, face recognition is becoming increasingly important in forensic and civil contexts. However, face recognition has yet to be thoroughly empirically studied to provide an adequate scientific and legal framework for investigative and court purposes. This observation sets the foundation for the research. We focus on issues related to face images and the use of automatic systems. Our objective is to validate a likelihood ratio computation methodology for interpreting comparison scores from automatic face recognition systems (score-based likelihood ratio, SLR). We collected three types of traces: portraits (ID), video surveillance footage recorded by ATM and by a wide-angle camera (CCTV). The performance of two automatic face recognition systems is compared: the commercial IDEMIA Morphoface (MFE) system and the open source FaceNet algorithm.

Understanding Natural Language: Insights From Cognitive Science, Cognitive Neuroscience, and Artificial Intelligence

Artificial Intelligence and Racism – What are the implications for scientific research?

As questions of race and justice have risen to the fore across the sciences, the ALBA Network has invited Dr Shakir Mohamed (Senior Research Scientist at DeepMind, UK) to provide a keynote speech on Artificial Intelligence and racism, and the implications for scientific research, that will be followed by a discussion chaired by Dr Konrad Kording (Department of Neuroscience at University of Pennsylvania, US - neuromatch co-founder)

Interdisciplinary College

The Interdisciplinary College is an annual spring school which offers a dense state-of-the-art course program in neurobiology, neural computation, cognitive science/psychology, artificial intelligence, machine learning, robotics and philosophy. It is aimed at students, postgraduates and researchers from academia and industry. This year's focus theme "Flexibility" covers (but not be limited to) the nervous system, the mind, communication, and AI & robotics. All this will be packed into a rich, interdisciplinary program of single- and multi-lecture courses, and less traditional formats.

Cognitive Maps

Ample evidence suggests that the brain generates internal simulations of the outside world to guide our thoughts and actions. These mental representations, or cognitive maps, are thought to be essential for our very comprehension of reality. I will discuss what is known about the informational structure of cognitive maps, their neural underpinnings, and how they relate to behavior, evolution, disease, and the current revolution in artificial intelligence.

Implementing structure mapping as a prior in deep learning models for abstract reasoning

Building conceptual abstractions from sensory information and then reasoning about them is central to human intelligence. Abstract reasoning both relies on, and is facilitated by, our ability to make analogies about concepts from known domains to novel domains. Structure Mapping Theory of human analogical reasoning posits that analogical mappings rely on (higher-order) relations and not on the sensory content of the domain. This enables humans to reason systematically about novel domains, a problem with which machine learning (ML) models tend to struggle. We introduce a two-stage neural net framework, which we label Neural Structure Mapping (NSM), to learn visual analogies from Raven's Progressive Matrices, an abstract visual reasoning test of fluid intelligence. Our framework uses (1) a multi-task visual relationship encoder to extract constituent concepts from raw visual input in the source domain, and (2) a neural module net analogy inference engine to reason compositionally about the inferred relation in the target domain. Our NSM approach (a) isolates the relational structure from the source domain with high accuracy, and (b) successfully utilizes this structure for analogical reasoning in the target domain.

Analogical Reasoning with Neuro-Symbolic AI

Knowledge discovery with computers requires a huge amount of search. Analogical reasoning is effective for efficient knowledge discovery. Therefore, we proposed analogical reasoning systems based on first-order predicate logic using Neuro-Symbolic AI. Neuro-Symbolic AI is a combination of Symbolic AI and artificial neural networks and has features that are easy for human interpretation and robust against data ambiguity and errors. We have implemented analogical reasoning systems by Neuro-symbolic AI models with word embedding which can represent similarity between words. Using the proposed systems, we efficiently extracted unknown rules from knowledge bases described in Prolog. The proposed method is the first case of analogical reasoning based on the first-order predicate logic using deep learning.

Human-like scene interpretation by a brain-inspired model

From single cell to population coding during defensive behaviors in prefrontal circuits

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. Over the past years, we used a combination we used a combination of extracellular recordings, neuronal decoding approaches, and state of the art optogenetic manipulations to identify key neuronal elements and mechanisms controlling defensive fear responses. I will present an overview of our recent work ranging from analyses of dedicated neuronal types and oscillatory and synchronization mechanisms to artificial intelligence approaches used to decode the activity or large population of neurons. Ultimately these analyses allowed the identification of high dimensional representations of defensive behavior unfolding within prefrontal networks.

Towards a More Authentic Vision of the (multi)Coding Potential of RNA

Ten of thousands of open reading frames (ORFs) are hidden within transcripts. They have eluded annotations because they are either small or within unsuspected locations. These are named alternative ORFs (altORFs) or small ORFs and have recently been highlighted by innovative proteogenomic approaches, such as our OpenProt resource, revealing their existence and implications in biological functions. Due to the absence of altORFs from annotations, pathogenic mutations within these are being ignored. I will discuss our latest progress on the re-analysis of large-scale proteomics datasets to improve our knowledge of proteomic diversity, and the functional characterization of a second protein coded by the FUS gene. Finally, I will explain the need to map the coding potential of the transcriptome using artificial intelligence rather than with conventional annotations that do not capture the full translational activity of ribosomes.

Maths, AI and Neuroscience meeting

To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent. In this meeting we bring together experts from Mathematics, Artificial Intelligence and Neuroscience for a three day long hybrid meeting. We will have talks on mathematical tools in particular Topology to understand high dimensional data, explainable AI, how AI can help neuroscience and to what extent the brain may be using algorithms similar to the ones used in modern machine learning. Finally we will wrap up with a discussion on some aspects of neural hardware that may not have been considered in machine learning.

Artificial Intelligence towards Autonomous Manufacturing

In this talk, Frans Cronje will be speaking about the journey towards autonomous manufacturing. He will demonstrate how artificial intelligence (AI) can be implemented to achieve a reduction in poor quality costs in manufacturing. The talk will showcase the power of applied AI.

Causal Reasoning: Its role in the architecture and development of the mind

The seminar will first outline the architecture of the human mind, specifying general and domain-specific mental processes. The place of causal reasoning and its relations with the other processes will be specified. Experimental, psychometric, developmental, and brain-based evidence will be summarized. The main message of the talk is that causal thought involves domain-specific core processes rooted in perception and served by special brain networks which capture interactions between objects. With development, causal reasoning is increasingly associated with a general abstraction system which generates general principles underlying inductive, analogical, and deductive reasoning and also heuristics for specifying causal relations. These associations are discussed in some detail. Possible implications for artificial intelligence and educational implications are also discussed.

Embodied Artificial Intelligence: Building brain and body together in bio-inspired robots

TBC

AI UPtake: Panel discussion on collaborative research

Artificial intelligence (AI) and machine learning (ML) can facilitate new paradigms and solutions in almost every research field. Collaboration is essential to achieve tangible and concrete progress in impactful and meaningful AI and ML research, due to its transdisciplinary nature. Come and meet University of Pretoria (UP) academics that are embracing and exploring the opportunities that AI and ML offer to transcend the conventional boundaries of their disciplines. Join the discussion to debate this new frontier of opportunities and challenges that may enable you to look beyond the obvious, and discover new directions and opportunities that we may offer for tomorrow — together!

Career in Data Science Webinar

What does an executive at a South African Bank, a machine learning lead, and a CEO of an AI company have in common? They all will be on a panel talking about careers in Data Science, Machine Learning and Artificial Intelligence

Seeing things clearly: Image understanding through hard-attention and reasoning with structured knowledges

In this talk, Jonathan aims to frame the current challenges of explainability and understanding in ML-driven approaches to image processing, and their potential solution through explicit inference techniques.

Can connectomics help us understand the brain and sustain the revolution in AI?

3 short talks and a panel discussion on the topic of "Can connectomics help us understand the brain and sustain the revolution in AI?" Expect beautiful connectomics data, provocative dreaming, realistic critiques and everything in between. Students & post-docs, stay on to meet our 3 amazing speakers. Moderator: Dr Greg Jefferis https://www2.mrc-lmb.cam.ac.uk/group-leaders/h-to-m/gregory-jefferis/

Playing StarCraft and saving the world using multi-agent reinforcement learning!

This is my C-14 Impaler gauss rifle! There are many like it, but this one is mine!" - A terran marine If you have never heard of a terran marine before, then you have probably missed out on playing the very engaging and entertaining strategy computer game, StarCraft. However, don’t despair, because what we have in store might be even more exciting! In this interactive session, we will take you through, step-by-step, on how to train a team of terran marines to defeat a team of marines controlled by the built-in game AI in StarCraft II. How will we achieve this? Using multi-agent reinforcement learning (MARL). MARL is a useful framework for building distributed intelligent systems. In MARL, multiple agents are trained to act as individual decision-makers of some larger system, while learning to work as a team. We will show you how to use Mava (https://github.com/instadeepai/Mava), a newly released research framework for MARL to build a multi-agent learning system for StarCraft II. We will provide the necessary guidance, tools and background to understand the key concepts behind MARL, how to use Mava building blocks to build systems and how to train a system from scratch. We will conclude the session by briefly sharing various exciting real-world application areas for MARL at InstaDeep, such as large-scale autonomous train navigation and circuit board routing. These are problems that become exponentially more difficult to solve as they scale. Finally, we will argue that many of humanity’s most important practical problems are reminiscent of the ones just described. These include, for example, the need for sustainable management of distributed resources under the pressures of climate change, or efficient inventory control and supply routing in critical distribution networks, or robotic teams for rescue missions and exploration. We believe MARL has enormous potential to be applied in these areas and we hope to inspire you to get excited and interested in MARL and perhaps one day contribute to the field!