artificial intelligence

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: 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: 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: 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: 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: 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: 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: 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.

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: 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.

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: 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: 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: 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: 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.

REU Site: Engaging Undergraduates in Interdisciplinary Evolutionary Science

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. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in the program. Students will be trained by faculty mentors how research is conducted in evolutionary science, will participate directly in that research, and will learn how to communicate science to public audiences. Potential career paths that exist for evolutionary scientists and for the application of evolutionary science will be discussed and explored. Many participants will present the results of their work at scientific conferences. Required formal mentor training of faculty mentors will have a lasting effect on their future mentoring efforts. Assessment of the program will be done through online surveys. Students will be tracked after the program to determine their career paths. Students should 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 this REU is evolutionary science, with students conducting research projects across several disciplines. Scientist-mentors in seven academic departments will offer research projects that span a wide range of topics, including evolutionary ecology, behavior, paleontology, genomics, bioinformatics, evolution of infectious disease, and developmental biology. Students will work on evolution-themed projects in one of these specific areas and will also work as a cohort to make broad connections among disciplines. As part of the program, students will receive training in ethical and responsible conduct in research, participate in career workshops, make formal research presentations based on their work, and create an interactive digital research poster. All students will be encouraged to participate in a series of three optional short courses in computational methods and phylogenetics. Students will be selected by program directors based on previous academic performance, enthusiasm for conducting research, interest in specific faculty research projects, and potential for future success in a research-related career. Students who have limited research opportunities at their home institution will be especially encouraged to apply. More information about the program is available by visiting https://biology.uiowa.edu/reu, or by contacting the PI (Dr. John Logsdon at john-logsdon@uiowa.edu) or the co-PI (Dr. Andrew Kitchen at andrew-kitchen@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.

Travel: NSF Student Travel Grant for the Programming Languages Mentoring Workshop at ACM SIGPLAN Symposium on Principles of Programming Languages, 2027-2029

A Programming Languages Mentoring Workshop (PLMW) is organized as part of the ACM SIGPLAN Symposium on Principles of Programming Languages (POPL), the flagship conference in the field of programming language theory, and one of the premier conferences in all of computer science. The 2027 conference will be held in Mexico City, Mexico. Many POPL papers directly address administration priorities in artificial intelligence (AI) and Quantum computing. The impact of the award relates to providing opportunities for students to receive mentoring from leading researchers, and building the next generation of researchers and knowledgeable practitioners in programming languages. The award's broader significance and importance include building international community, lasting professional connections to design novel programming languages and implement tools, and enhancing education of US students. The workshop also provides students exposure to and multiple opportunities to interact with leading-edge research and researchers. By supporting students, the workshop thus imparts training to the next generation of researchers in programming languages and systems and contributes to building a national workforce in these topics. 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: Elements: Physics-Informed Digitized Cyberinfrustructure Towards Next-G Underwater Networks

Underwater wireless communication and networking are critical for monitoring aquatic environments, improving maritime safety, supporting offshore exploration, and enhancing national security. However, research progress in this field has been slow compared with terrestrial wireless systems because conducting underwater wireless experiments is challenging. Natural underwater environments are uncontrollable, and indoor pools and tanks are static and small, which significantly limits research reproducibility, innovation, and public accessibility. To bridge this gap, this project implements a remotely accessible underwater communication and networking platform in a water tunnel that enables experimentation, dataset collection, and artificial intelligence model building under a range of controlled, reconfigurable, reproducible conditions. The testbed, datasets, and developed software enable new wireless communication technology development without the high cost and complications of natural underwater deployments. By sharing advanced experimental tools, datasets, and software with the research community, this project advances scientific discovery and strengthens national leadership in next-generation underwater communication and networking systems. In addition, this project integrates research with education and actively trains students in communication, networking, sensing, and artificial intelligence to support workforce development and address critical national needs. This project designs and deploys a hybrid underwater acoustic, magnetic, and visible light networking system that integrates a reconfigurable water-tunnel testbed, physics-informed multi-modal deep generative channel models, and a scalable digital twin for dynamic underwater networking simulation and optimal control. First, the remotely accessible, reconfigurable testbed instrument enables the collection of acoustic, magnetic, and visible light communication channel data under dynamic water flow and blockage conditions. Second, the collected datasets are used to train physics-informed deep generative channel models that extend beyond the physical testbed to enable large-scale, measurement-driven simulations. Last, the physical testbed and channel models are integrated to develop a networking digital twin, which allows researchers to evaluate multi-modal scheduling strategies, resource allocation schemes, and networking protocols under realistic dynamic underwater conditions. All software, datasets, models, and documentation will be publicly released through open repositories and public websites. By linking physical experimentation with scalable digital simulation, this project will provide sustainable cyberinfrastructure that accelerates data-driven and artificial intelligence-enabled innovation in underwater wireless communication and networking. 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.

RET Site: Empowering High School Teachers through Research Experiences on Quantum and Cybersecurity to Prepare the Future Workforce

This project establishes a three-year, fully online Research Experiences for Teachers (RET) Site focused on quantum computing and cybersecurity education. The project prepares high school teachers from across the United States to bring emerging topics in quantum information science and cybersecurity into their classrooms. As quantum technologies continue to develop, future workers will need new knowledge and skills to understand how quantum computing affects digital security. However, most high schools do not currently have the resources or training to teach these topics. This project addresses this need by engaging teachers in summer research experiences and supporting them throughout the academic year as they develop and use new classroom materials. By preparing teacher-leaders and providing accessible instructional resources, the project expands access to emerging science and engineering topics, supports national workforce development in cybersecurity and quantum information science. The project engages teachers in educational research focused on developing and evaluating classroom instructional units that teach cybersecurity concepts in the context of quantum computing using game-based and technology-supported learning approaches. Each summer, participating teachers complete a six-week online research experience in which they work with university researchers to develop standards-aligned lesson plans, classroom activities, and laboratory exercises. During the academic year, teachers participate in follow-up activities including biweekly meetings, classroom implementation, curriculum refinement, and sharing of instructional materials and research results. Teachers and students use an online platform that provides access to a browser-based quantum circuit simulator, automated assessment tools, and artificial intelligence (AI) learning support. The project studies how game-based learning and interactive tools can help make complex quantum cybersecurity concepts accessible at the high school level and produces classroom-ready instructional materials that can be adopted nationwide. 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.

RET Site: Project-Based Learning for Rural Alabama STEM Middle School Teachers in Artificial Intelligence, Machine Learning, and Robotics

This award establishes a renewed Research Experience for Teachers (RET) Site at Auburn University. The site will provide unique and holistic research experiences for 24 middle school math and science teachers in the 7th-8th grades from rural areas of Alabama. The research focus is on smart humanoid and mobile robots enabled by cutting-edge technologies of artificial intelligence (AI) and machine learning (ML). The goals of the site are to equip teachers with knowledge and skills in AI and ML and robotics and promote their interests in these areas and facilitate teachers’ development and implementation of engaging project-based curricular modules for their classrooms. The site will provide research experiences to eight (8) middle school math and science teachers in the 7th-8th grades each year via a six-week summer program and nine-month academic year follow-up, with the research focused on smart mobile robots based on AI and ML. The site has five primary objectives to reach its goals of providing a rigorous and engaging RET experience: 1) provide education and training activities on the fundamentals of AI/ML and robotics, and novel platforms of ML-based smart humanoid and mobile robots for research and education; 2) engage teachers in hands-on research projects on ML-based smart robots that match well with faculty mentors’ research projects; 3) allow teachers to collaborate with engineering and STEM education faculty to develop the project-based curricular modules; 4) foster teachers’ leadership and pedagogical skills via teacher leader mentoring and practice of teaching the RET curricular modules; 5) assist teachers to implement the RET curricular modules via academic year follow-up. 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: Hands-On Research in Federated Learning Security through Red Team vs. Blue Team Exercises

This Research Experiences for Undergraduates Site at the University of Nevada, Las Vegas supports 10 students each year in a 10-week summer research program on the security of federated learning, a way for many devices or organizations to train a shared artificial intelligence model without exchanging their raw data. This approach can help protect privacy, but it also creates new security risks because attackers may try to corrupt the training process, steal information from the model, or reduce system reliability. The project’s novelties are the integration of hands-on attack-and-defense research across the full federated learning process and the use of Red Team versus Blue Team exercises to study these problems in realistic settings. The project's broader significance and importance are that it advances safer privacy-preserving artificial intelligence, expands access to advanced undergraduate research opportunities, and helps prepare the future artificial intelligence and cybersecurity workforce. The project contributes to a stronger national capacity for building trustworthy data-driven systems. The research project focuses on threats and defenses in the data collection, training, and inference stages of federated learning. Students and mentors investigate representative attacks including botnet-style disruption, poisoning, backdoor insertion, privacy leakage, membership inference, and data reconstruction, and they evaluate defenses such as robust aggregation, anomaly detection, and differential privacy. The work uses a dedicated federated learning cybersecurity range, realistic datasets from computer vision, language, and network traffic applications, and distributed computing resources for controlled experimentation. Through iterative Red Team and Blue Team studies, the project produces software, tutorials, datasets, and empirical results that improve understanding of secure and privacy-preserving distributed learning. The anticipated outcome is stronger technical foundations for trustworthy artificial intelligence and a broader pipeline of students prepared for research and professional practice in cybersecurity and 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.

REU SITE: WAVE SURF: Workforce Advancement in Verification and Emulation of Semiconductor Chips -- Undergraduate Research Fellowships

Semiconductor chips power nearly every aspect of modern life, from smartphones and medical devices to national defense systems and critical infrastructure. As these chips grow more complex, verifying that they function correctly and securely has become one of the most costly and time-consuming stages of development. At the same time, the United States faces a critical shortage of engineers trained in chip verification, a gap that threatens both economic competitiveness and national security. This project establishes a Research Experiences for Undergraduates (REU) Site at Texas A&M University to train the next generation of verification engineers and semiconductor researchers. Each summer, ten undergraduate students will participate in a ten-week immersive research program focused on chip verification and the application of artificial intelligence (AI) to semiconductor design evaluation. The project specifically targets students from community colleges, regional universities, and institutions with limited research infrastructure, with emphasis on first-generation college students, veterans, and students with no prior research experience. Participants will receive layered mentorship from faculty, graduate students, and industry professionals, along with professional development training in scientific communication, ethics, and career readiness. Industry partners will contribute guest lectures, mentorship, and site visits, connecting students directly to career pathways in the semiconductor workforce. By combining cutting-edge research training with inclusive recruitment and sustained post-program engagement, this project addresses a pressing national workforce need while broadening participation in a strategically vital field. This project engages undergraduate researchers in four interconnected themes spanning hardware security, performance analysis, design automation, and functional verification. The first theme develops scalable security verification frameworks that adapt fuzzing, formal analysis, and symbolic execution to detect vulnerabilities in hardware designs described at the register-transfer level and prototyped on field-programmable gate arrays. The second theme applies machine learning (ML) to processor performance debugging, training models to automatically detect and localize performance anomalies using hardware counter data, estimate fine-grained performance breakdowns from limited counter sets, and accelerate design simulation through early-run inference. The third theme investigates the use of large language models to automate chip design tasks, including hardware description language code generation, physical synthesis using open-source tool flows, and timing optimization. The fourth theme targets ML-driven functional verification, developing techniques for automated testbench generation, structure-aware coverage acceleration, multimodal failure triage that combines text and waveform data, and closed-loop testbench correction guided by dynamic bug mutation analysis. Students will work with commercial electronic design automation tools and open-source processor platforms, producing weekly progress reports, final presentations, and potential conference submissions. The program integrates structured mentorship, professional skills training, and industry exposure through site visits and seminars with engineers from leading semiconductor companies. An external evaluator will conduct formative and summative assessments, and participants will be tracked for five years to measure long-term impact on graduate school enrollment, research productivity, and career outcomes. 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: Undergraduate Research Experience in Edge Intelligence

This project establishes a Research Experiences for Undergraduates (REU) site at the University of Nevada, Reno, to provide undergraduate students nationwide with immersive research experiences in the field of edge intelligence. Edge intelligence, where data collection, processing, and analysis are performed close to the data source rather than on centralized cloud-based systems, is a rapidly advancing paradigm critical for applications such as autonomous vehicles, smart cities, industrial automation, and healthcare systems. However, deploying artificial intelligence (AI) on resource-limited edge devices presents substantial challenges in accuracy, efficiency, and robustness, with significant implications for public health, national security, and economic competitiveness. By engaging students in hands-on research and equipping them with the skills to address these challenges, this project aims to develop a highly skilled STEM workforce capable of advancing practical AI technologies. The program will foster sustained interest in research and encourage participants to pursue graduate studies and careers in STEM. Through the development of accurate, efficient, and trustworthy AI systems, the project will contribute to technological innovation, strengthen public trust in AI, and support societal well-being and national security. This REU site develops undergraduate research capacity in edge intelligence by leveraging the interdisciplinary expertise of University of Nevada, Reno faculty in AI, robotics, and cyber-physical systems. The research is structured around three core thrusts in edge-based machine learning (ML) systems: (1) algorithms and architectures for resource-constrained devices, (2) scalable and robust learning in heterogeneous edge environments, and (3) resource management for energy-efficient and sustainable operation. Representative projects include accelerating federated learning in heterogeneous wireless networks, developing memory-efficient training of large language models for edge devices, designing decentralized federated learning with workload balancing, federated fine-tuning of vision models for wildfire detection, and collaborative multi-robot deep reinforcement learning. Guided by experienced faculty and graduate student mentors, students will engage in cutting-edge research and contribute to the development of ML systems for real-world applications such as smart Internet-of-Things devices, autonomous vehicles, and robotics. The program is complemented by training in research methodology, scientific communication, and career development, preparing participants for graduate study and careers in STEM fields. 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.

RET Site: Research Experiences for Teachers in Cybersecurity and Artificial Intelligence

Computing and cybersecurity skills are increasingly essential across nearly every sector of the modern economy, yet many students in Mississippi and similar rural states have limited exposure to these fields, particularly in schools with fewer resources. Teachers play a pivotal role in shaping student interest in science and technology careers, but many lack the research experience needed to bring authentic computing content into their classrooms. This project addresses that gap by immersing high school and community college educators in hands-on cybersecurity research at Mississippi State University, enabling them to return to their classrooms with deeper knowledge, stronger confidence, and ready-to-use instructional materials. By strengthening teacher expertise in computing and cybersecurity, the project generates a multiplier effect that reaches hundreds of students annually, expanding access to computing pathways in communities. The project also supports workforce development by preparing a pipeline of students who are better equipped for careers in computing, national security, and related fields that are critical to the nation's economic strength and security. This award supports a Research Experiences for Teachers (RET) site in which thirty educators participate over three years in a six-week summer research program at Mississippi State University. Participants are embedded in faculty-led research groups focused on areas such as machine learning for radio frequency interference detection, algorithmic resilience in wireless networks, software reverse engineering, adversarial attacks on robot vision systems, and cybersecurity of electric vehicle charging infrastructure. The first week provides foundational training in computing concepts, research ethics, and laboratory practices, after which participants engage directly in ongoing research projects under faculty mentorship. Beginning in the fourth week, teachers develop standards-aligned curricular modules that connect their research experiences to mathematics and computing courses at the secondary and community college level. Academic-year follow-up activities include faculty classroom visits, virtual mentoring, peer collaboration through a shared digital platform, and presentations at state and regional teacher conferences. Program effectiveness is assessed through a mixed-methods evaluation framework examining teacher self-efficacy, curricular integration, and long-term changes in classroom practice. The project is led by faculty in the Department of Computer Science and Engineering at Mississippi State University, in partnership with the Starkville Oktibbeha Consolidated School District, Hinds Community College, and others. 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: Frameworks: ML4GW, a machine learning ecosystem for gravitational wave data analysis

Since the first direct observation of gravitational waves in 2015, a new field of astronomy has fundamentally changed how the universe can be explored. A gravitational wave is a ripple in spacetime produced when two extremely dense objects, such as black holes or neutron stars, collide. The detectors that observe these signals now record hundreds of such events per year, and the rate is expected to grow to several events per day within the next few years. Sifting through this flood of data to find the rare astrophysical signals buried in detector noise, and then alerting telescopes around the world quickly enough to capture the light produced by a collision, is one of the most demanding computational problems in modern science. This project develops an open, community machine learning framework that makes gravitational-wave discovery faster, more accurate, and more accessible. The framework will reduce the time between the crossing of a gravitational wave through the detectors and a public astronomical alert to less than a second, make it possible to detect events that traditional analysis methods miss, and lower the computing cost of these analyses by orders of magnitude. The project trains the next generation of scientists, including high-school students, undergraduates, graduate students, and researchers at smaller institutions, by sharing open code, open data, open trained models, and open lessons. It also strengthens shared national computing infrastructure that benefits not just gravitational-wave science but also particle physics, neutrino astronomy, and time-domain astronomy more broadly, advancing the national interest by accelerating discovery and broadening participation in science. The project develops ml4gw, an open-source PyTorch-based machine learning (ML) framework for gravitational wave (GW) data analysis. ml4gw provides Graphics Processing Unit (GPU)-accelerated implementations of the data ingestion, signal-processing, waveform-generation, and inference operations that historically ran on central-processing-unit clusters of the Laser Interferometer Gravitational-wave Observatory (LIGO) Data Grid, and integrates the resulting models into the international low-latency alert pipeline. The award covers three coordinated work packages. The first extends model coverage to long-duration binary-neutron-star and neutron-star-black-hole signals using multi-rate and multi-band processing, integrates auxiliary detector channels through multimodal architectures, and develops state-space and ensemble models together with a shared foundation backbone from which task-specific models can be fine-tuned. The second work package builds production-grade cyberinfrastructure that targets the National Artificial Intelligence Research Resource, the National Research Platform, and the Open Science Data Federation, including an Inference-as-a-Service deployment built on the NVIDIA Triton inference server and on the SuperSONIC service that already supports particle and neutrino physics experiments. The third work package delivers community resources: standardized benchmark datasets with persistent digital object identifiers on Zenodo, versioned reference models on Hugging Face, comprehensive documentation and tutorials hosted on Read the Docs, containerized release artifacts, an upgraded continuous integration system that reduces test runtime by an order of magnitude, and an agent-driven development scaffold for community-led code contribution. Training and outreach activities include hands-on tutorials at international collaboration meetings, an annual hands-on lesson at the University of Minnesota Time-Domain Astrophysics Summer School, and a public machine learning challenge focused on binary-neutron-star detection that builds on a prior challenge that engaged hundreds of teams and roughly a thousand individual participants. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program within the Physics Section of the Directorate for Mathematical and Physical Sciences. 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: Elements: Physics-Informed Digitized Cyberinfrustructure Towards Next-G Underwater Networks

Underwater wireless communication and networking are critical for monitoring aquatic environments, improving maritime safety, supporting offshore exploration, and enhancing national security. However, research progress in this field has been slow compared with terrestrial wireless systems because conducting underwater wireless experiments is challenging. Natural underwater environments are uncontrollable, and indoor pools and tanks are static and small, which significantly limits research reproducibility, innovation, and public accessibility. To bridge this gap, this project implements a remotely accessible underwater communication and networking platform in a water tunnel that enables experimentation, dataset collection, and artificial intelligence model building under a range of controlled, reconfigurable, reproducible conditions. The testbed, datasets, and developed software enable new wireless communication technology development without the high cost and complications of natural underwater deployments. By sharing advanced experimental tools, datasets, and software with the research community, this project advances scientific discovery and strengthens national leadership in next-generation underwater communication and networking systems. In addition, this project integrates research with education and actively trains students in communication, networking, sensing, and artificial intelligence to support workforce development and address critical national needs. This project designs and deploys a hybrid underwater acoustic, magnetic, and visible light networking system that integrates a reconfigurable water-tunnel testbed, physics-informed multi-modal deep generative channel models, and a scalable digital twin for dynamic underwater networking simulation and optimal control. First, the remotely accessible, reconfigurable testbed instrument enables the collection of acoustic, magnetic, and visible light communication channel data under dynamic water flow and blockage conditions. Second, the collected datasets are used to train physics-informed deep generative channel models that extend beyond the physical testbed to enable large-scale, measurement-driven simulations. Last, the physical testbed and channel models are integrated to develop a networking digital twin, which allows researchers to evaluate multi-modal scheduling strategies, resource allocation schemes, and networking protocols under realistic dynamic underwater conditions. All software, datasets, models, and documentation will be publicly released through open repositories and public websites. By linking physical experimentation with scalable digital simulation, this project will provide sustainable cyberinfrastructure that accelerates data-driven and artificial intelligence-enabled innovation in underwater wireless communication and networking. 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: Cryptography, Coding Theory and Quantum Computing at the USF

This Research Experiences for Undergraduates (REU) Site award funds a renewal of a site focused on cryptography, coding theory, and quantum computing at the University of South Florida. Modern life depends on the secure movement, storage, and processing of digital information. Cryptography protects the confidentiality, integrity, and authenticity of data, while coding theory helps preserve data when noise, transmission errors, or equipment failures occur. These areas are increasingly shaped by the rise of quantum technologies, which create both new opportunities and new threats to digital security. The project’s novelties are the integration of cryptography, coding theory, and quantum computing within a single undergraduate research program, the use of research teams that pair undergraduates with faculty and near-peer mentors, and an expanded scope that connects foundational mathematics with applications to national security, secure communication, and privacy in emerging technologies. The project's broader significance and importance are that it helps prepare a future workforce with the technical depth needed to address pressing challenges in cybersecurity, quantum information, and trustworthy data-driven systems. For each of three summers, this REU Site offers 10 undergraduate students the opportunity to perform research for 10 weeks under the mentorship of an interdisciplinary team with expertise spanning mathematics, computer science, engineering, and physics. This REU Site focuses on active and interdisciplinary research problems in post-quantum cryptography, quantum error correction, and related areas of coding theory and quantum information. Students investigate cryptographic constructions based on code equivalence and lattice isomorphism, including algorithms, security analysis, and implementation issues relevant to future quantum-resistant systems. Additional projects study quantum low-density parity-check codes and decoding failure mechanisms, the design of highly nonlinear functions for block ciphers, locally recoverable codes for distributed storage, physical models for reliable qubit implementation, and privacy-preserving methods for collaborative training of artificial intelligence models. The site combines these research activities with technical training, mentoring in computational tools, workshops on intellectual property and graduate school applications, and a summer symposium in which participants present their results. 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: CER: From Classroom to Career Readiness: Enhancing Undergraduate Computing Education Through Collaborative Research Experience in AI Security and Privacy

Artificial Intelligence (AI) technologies are transforming daily life, but their rapid adoption has also introduced serious security and privacy challenges. Addressing these risks requires a workforce that can both advance AI innovation and safeguard its deployment. This project will help meet that need by strengthening undergraduate computing education through a curriculum-based research experience program that connects classroom learning with real world research experiences. The effort will integrate the latest AI security and privacy topics into existing computing courses while helping students build professional skills such as communication, teamwork, and leadership. By creating flexible learning modules that can be used across a range of undergraduate computing courses and institutions, the project will support workforce development and contribute to the secure, reliable, and responsible use of AI in society. The project will establish a curriculum-based undergraduate research experience program focused on AI security and privacy across partner institutions. The research team will design, implement, and evaluate flexible educational modules including labs, tutorials, assignments, and research activities in computer vision, speech and audio, and network systems. These modules will address vulnerabilities across the AI lifecycle and will be designed for seamless integration into undergraduate computing courses. The instructional materials will also be aligned with the NICE (National Initiative for Cybersecurity Education) Cybersecurity Workforce Framework to strengthen career-relevant competencies. In parallel, the research team will study educational approaches that embed research into coursework, including project-based and competition-based learning, and evaluate their effects on student engagement, success, technical growth, and professional skill development. The project will generate transferable resources and evidence-based practices that can be adopted more broadly in computing education and shared with academic and community audiences. 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: Knowledge Beyond Language with Vector Embeddings

This three-year Research Experiences for Undergraduates (REU) site at the University of North Texas will support 10 students for 10 weeks each summer and train them to build artificial intelligence (AI) systems capable of sharing knowledge across domains through vector embeddings. Vector embeddings translate information (e.g., images, text or sound) into numerical data. These data are then converted into lists that allow the numbers to convey context and meaning. Current AI systems who are working in areas such as visual object recognition, speech recognition, and understanding natural language require extensive training, with much of the learned knowledge locked within the structure of the system, which is then difficult to reuse. However, students in this program will focus on creating and leveraging highly-trained AI systems to represent information in ways that preserve the nuanced understanding learned by these systems and make it accessible for other applications. This REU brings together an interdisciplinary team to support projects that showcase the benefits of AI systems that can exchange and reuse learned knowledge. Early in the program, each student will identify a project domain and a faculty advisor with whom they can work. Students will also participate in a long-standing AI summer research program integrating current university students and external REU students to facilitate collaboration across departments and student expertise. Specifically, the training in this REU will allow students to more efficiently represent and transfer the knowledge acquired by self-supervised deep learning models. Each year, students will create vector representations of entities that appear across multiple domains, apply these embeddings to improve prediction models, and systematically evaluate, document, and contribute them to a shared, reusable knowledge base. These efforts are coordinated through common documentation, evaluation, and sharing practices that enable comparison and reuse of embeddings across projects. For the first five weeks, the students will be exposed to different embedding strategies and machine learning applications that use them, then transition to developing, testing, and refining their individual research efforts in the last five weeks. This REU will help prepare a workforce of students not only adept at using deep learning models but also capable of extending their functionality through reusable and shareable representations. Additionally, this project will train a diverse range of students from college partners with limited research resources to work in interdisciplinary teams at a Carnegie R1 research institution. 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.

CER: Assessing relationships between high school teacher professional development and student outcomes in computer science and artificial intelligence

Many high school students do not have the chance to take computer science classes. This project aims to help more students gain access to computer science by training teachers in new ways to teach important topics like problem-solving, computer basics, and how computers can be used responsibly. The project is expected to increase student interest in computer science, stay in computer science classes, and even choose careers in technology. It will work with one of California's largest school districts (Elk Grove Unified School District) and has the potential to improve education and job opportunities for thousands of students. The results could also provide insights to other schools across the country to better support student learning about computers and technology. This project builds on previous work to expand access to computer science in high schools by using a researcher-practitioner partnership to study the effects of teacher professional development on student motivation and retention in computational courses. The research will use design-based methods to investigate how training teachers in design thinking, core computer science concepts, and key artificial intelligence topics affects student motivation, engagement, and enrollment in information and communication technology pathways. The project will involve collaboration between university faculty and public school educators in order to increase the number of participating schools, teachers, and students. By generating new evidence on how teacher training impacts student outcomes, this work will contribute to the field of computer science education and inform future practices aimed at student recruitment and retention in computational subjects. 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: Research in Consumer Networking Technologies

This Research Experience for Undergraduates (REU) Site at the University of Missouri – Columbia will investigate a variety of interesting and challenging problems that involve consumer networking applications and services that are of significance to the economy and quality of life in areas such as public safety, health care, nature conservation and education. The students will participate in the faculty mentors’ on-going funded research, investigate technically challenging issues and develop viable solutions and insights. They will participate in professional development activities to prepare them for future graduate studies and a broad range of emerging computing careers. Using an already established network of recruiting venues, participants will be recruited from a broad range of educational and geographic backgrounds. The intellectual merit of the project rests with the leadership, an experienced research group with excellent expertise and experience in the research area. The research will focus on broad topics such as software-defined networking/virtualization for resource control, resilient visual computing at the drone network edge, secure and artificial intelligence enabled mixed reality, mobile sensing and environment recognition, securing networked consumer applications, and application-aware network performance optimization. The research activities will lead to a better understanding of the multitude of efficiency, performance, reliability, scalability, and security issues and tradeoffs in consumer networking technologies and related applications. Advanced networking environments and software developed in the previous REU site programs as well as novel testbeds such as the NSF-supported AERPAW/FABRIC resources, Mizzou CAVE (mixed reality), smart device equipment, and sensor-based monitoring will be leveraged by the participating students in hands-on experiments within their research projects. 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: Frameworks: ML4GW, a machine learning ecosystem for gravitational wave data analysis

Since the first direct observation of gravitational waves in 2015, a new field of astronomy has fundamentally changed how the universe can be explored. A gravitational wave is a ripple in spacetime produced when two extremely dense objects, such as black holes or neutron stars, collide. The detectors that observe these signals now record hundreds of such events per year, and the rate is expected to grow to several events per day within the next few years. Sifting through this flood of data to find the rare astrophysical signals buried in detector noise, and then alerting telescopes around the world quickly enough to capture the light produced by a collision, is one of the most demanding computational problems in modern science. This project develops an open, community machine learning framework that makes gravitational-wave discovery faster, more accurate, and more accessible. The framework will reduce the time between the crossing of a gravitational wave through the detectors and a public astronomical alert to less than a second, make it possible to detect events that traditional analysis methods miss, and lower the computing cost of these analyses by orders of magnitude. The project trains the next generation of scientists, including high-school students, undergraduates, graduate students, and researchers at smaller institutions, by sharing open code, open data, open trained models, and open lessons. It also strengthens shared national computing infrastructure that benefits not just gravitational-wave science but also particle physics, neutrino astronomy, and time-domain astronomy more broadly, advancing the national interest by accelerating discovery and broadening participation in science. The project develops ml4gw, an open-source PyTorch-based machine learning (ML) framework for gravitational wave (GW) data analysis. ml4gw provides Graphics Processing Unit (GPU)-accelerated implementations of the data ingestion, signal-processing, waveform-generation, and inference operations that historically ran on central-processing-unit clusters of the Laser Interferometer Gravitational-wave Observatory (LIGO) Data Grid, and integrates the resulting models into the international low-latency alert pipeline. The award covers three coordinated work packages. The first extends model coverage to long-duration binary-neutron-star and neutron-star-black-hole signals using multi-rate and multi-band processing, integrates auxiliary detector channels through multimodal architectures, and develops state-space and ensemble models together with a shared foundation backbone from which task-specific models can be fine-tuned. The second work package builds production-grade cyberinfrastructure that targets the National Artificial Intelligence Research Resource, the National Research Platform, and the Open Science Data Federation, including an Inference-as-a-Service deployment built on the NVIDIA Triton inference server and on the SuperSONIC service that already supports particle and neutrino physics experiments. The third work package delivers community resources: standardized benchmark datasets with persistent digital object identifiers on Zenodo, versioned reference models on Hugging Face, comprehensive documentation and tutorials hosted on Read the Docs, containerized release artifacts, an upgraded continuous integration system that reduces test runtime by an order of magnitude, and an agent-driven development scaffold for community-led code contribution. Training and outreach activities include hands-on tutorials at international collaboration meetings, an annual hands-on lesson at the University of Minnesota Time-Domain Astrophysics Summer School, and a public machine learning challenge focused on binary-neutron-star detection that builds on a prior challenge that engaged hundreds of teams and roughly a thousand individual participants. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program within the Physics Section of the Directorate for Mathematical and Physical Sciences. 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: Research on Prescriptive Analytics for AI-enabled Operations Engineering

This Research Experiences for Undergraduates Site renewal will engage 10 undergraduate students each year in a 9-week summer research program on artificial intelligence (AI)-enabled operations engineering. Many important systems in transportation, healthcare, manufacturing, and services operate under uncertainty, limited resources, and changing conditions. These systems increasingly depend on analytical and computational methods that combine operations research and artificial intelligence to improve planning, coordination, and real-time decision-making. The project will provide students with mentored research, technical training, and professional development that strengthen preparation for graduate study and technical careers in computing, operations research, analytics, and intelligent systems. The research activities will focus on AI-enabled operations engineering, including prescriptive analytics methods that combine optimization, simulation, machine learning, and related computational tools to support complex operational decisions. Students will engage with decision problems in healthcare, next-generation transportation systems, advanced manufacturing, and contested logistics. They will receive training in problem formulation, data analysis, optimization, simulation, algorithm development, machine learning, computational experimentation, and solution evaluation. Sample projects that are computationally challenging include collaborative blood inventory planning, network-aware drone logistics, urban air mobility coordination, additive manufacturing scheduling, and decision-making for logistics operations under disrupted or contested conditions. The program will strengthen student preparation in both computing and operations research methods while helping build a workforce prepared to address complex real-world challenges in critical operational 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.

REU Site: AI Research for Intelligent Systems and Efficiency

Artificial Intelligence (AI) serves as a strategic engine for economic growth, national security, and global competitiveness. This project establishes a Research Experiences for Undergraduates (REU) site, AI Research for Intelligent Systems and Efficiency (ARISE), in the Electrical and Computer Engineering Department at the University of Arizona. The project recruits and trains eight undergraduate students each summer, engaging them in immersive research at the intersection of AI applications, efficient algorithms, and hardware system optimization. The overarching goal is to address real-world engineering challenges through sustainable and effective computational solutions. The project’s novelties are its focus on the "full-stack" nature of AI integrating the systems and hardware architecture with high-level AI applications and algorithms and a collaborative mentoring approach that provides participants with multidisciplinary expertise. The project's broader significance and importance are the development of a skilled workforce in national priority areas; the creation of educational demos and course materials for the public; and the fostering of technology transfer to industry partners to strengthen the domestic technology ecosystem. The ARISE REU site organizes research into three complementary thrusts: AI Applications, Efficient Algorithms, and Hardware Systems. Each project is co-mentored by faculty paired from different thrusts to facilitate interdisciplinary collaboration across the computing stack. Research projects focus on the frontiers of the field, exposing students to state-of-the-art AI algorithms, efficient training and inference techniques, and distributed learning. The technical approach emphasizes application-guided optimization, high-performance computing, reconfigurable systems, and hardware-software co-design to solve real-world engineering tasks, including autonomous agents, design automation, healthcare, and defense applications. Participants gain expertise in energy-efficient AI through intensive bootcamps, research seminars, and systematic training in research methodologies. Structured mentoring and professional development activities prepare participants for diverse careers in the AI infrastructure and semiconductor sectors. The project shares outcomes through technical publications, demonstrations, and the integration of research findings into university curricula and industry-university cooperative research centers. This work strengthens the national capacity for AI innovation by preparing an interdisciplinary cohort of researchers to solve the efficiency bottlenecks of next-generation AI computing and 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.

REU Site: Applied Research in Deep Learning

This project will provide undergraduate students with hands-on research experiences in the rapidly growing field of deep learning. Over three years, the program will engage 30 students from computer science, engineering, information technology, and related disciplines in meaningful research projects guided by faculty mentors. Participants will explore how deep learning techniques can be used to address real-world challenges in cybersecurity, robotics, hate speech in social media, and autonomous systems. Through these experiences, students will gain exposure to how deep learning technologies can be used to benefit society across a wide range of applications. In addition to research activities, the program will provide structured training to help students develop important professional skills, including scientific writing, oral communication, teamwork, and responsible research practices. By working in a collaborative and supportive environment, students will strengthen both their technical abilities and their confidence as emerging researchers. Overall, the program aims to inspire and prepare the next generation of scientists and engineers by giving them early, meaningful exposure to research in deep learning and its many beneficial uses. This project establishes a structured, research-intensive undergraduate training program aimed at increasing student engagement in state-of-the-art deep learning methodologies and applications. Research activities will focus on the design, analysis, and application of modern deep learning techniques to address contemporary challenges in areas such as multimedia security, robotic motion planning, autonomous multi-agent coordination, and malicious network activity detection. Faculty-mentored projects will emphasize both methodological advances and applied system development, with students contributing to end-to-end research pipelines including data preprocessing, model design, training, evaluation, and deployment-oriented analysis. Key objectives include: (i) developing students’ problem-solving and critical thinking skills in the context of deep learning research; (ii) building proficiency in contemporary deep learning frameworks and methodologies; (iii) training students in scholarly communication and research dissemination practices; and (iv) enhancing collaboration and teamwork. Collectively, this REU Site aims to cultivate a pipeline of well-prepared undergraduate researchers equipped for graduate study and careers in machine learning and artificial intelligence research. 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: CER: From Classroom to Career Readiness: Enhancing Undergraduate Computing Education Through Collaborative Research Experience in AI Security and Privacy

Artificial Intelligence (AI) technologies are transforming daily life, but their rapid adoption has also introduced serious security and privacy challenges. Addressing these risks requires a workforce that can both advance AI innovation and safeguard its deployment. This project will help meet that need by strengthening undergraduate computing education through a curriculum-based research experience program that connects classroom learning with real world research experiences. The effort will integrate the latest AI security and privacy topics into existing computing courses while helping students build professional skills such as communication, teamwork, and leadership. By creating flexible learning modules that can be used across a range of undergraduate computing courses and institutions, the project will support workforce development and contribute to the secure, reliable, and responsible use of AI in society. The project will establish a curriculum-based undergraduate research experience program focused on AI security and privacy across partner institutions. The research team will design, implement, and evaluate flexible educational modules including labs, tutorials, assignments, and research activities in computer vision, speech and audio, and network systems. These modules will address vulnerabilities across the AI lifecycle and will be designed for seamless integration into undergraduate computing courses. The instructional materials will also be aligned with the NICE (National Initiative for Cybersecurity Education) Cybersecurity Workforce Framework to strengthen career-relevant competencies. In parallel, the research team will study educational approaches that embed research into coursework, including project-based and competition-based learning, and evaluate their effects on student engagement, success, technical growth, and professional skill development. The project will generate transferable resources and evidence-based practices that can be adopted more broadly in computing education and shared with academic and community audiences. 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.

N/A

The Department of Engineering Mathematics at the University of Bristol is seeking an outstanding candidate to fill the role of Professor in Artificial Intelligence. You will have the opportunity to provide visionary leadership to the department and its staff, students, & partners, helping to strengthen and further develop our already impressive research and teaching programs in AI. Our Intelligent Systems Group supports the Faculty of Engineering's AI/Data Science Theme, fostering an inclusive environment for all.

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.

Mitra Baratchi

We are looking for an excellent candidate with a master’s degree in MSc in Artificial Intelligence, Computer Science, Mathematics, Statistics, or a closely related field to join a project focused on developing an advanced transparent machine learning framework with application on movement behavioural analysis. Smartwatches and other wearable technologies allow us to continuously collect data on our daily movement behaviour patterns. We would like to understand how machine learning techniques can be used to learn causal effects from time-series data to identify and recommend effective changes in daily activities (i.e., possible behavioural interventions) that are expected to result in concrete health improvements (e.g., improving cardiorespiratory fitness). This research, at the intersection of machine learning and causality, aims to develop algorithms for finding causal relations between behavioural indicators learned from the time series data and associated health-outcomes.

Professor Geoffrey J Goodhill

The Center for Theoretical and Computational Neuroscience (CTCN) at Washington University in St Louis invites applications from outstanding Postdoctoral Fellows to work at the interface between theoretical and experimental neuroscience labs at WashU. The CTCN is a joint initiative between the Schools of Medicine, Engineering, and Arts and Sciences, and provides a hub for neuroscientists to collaborate with mathematicians, physicists and engineers to find creative solutions to some of the most difficult problems currently facing neuroscience and artificial intelligence. Each CTCN Postdoctoral Fellow is based in at least two labs, but also has the opportunity to seek out new collaborations which help build new connections within the WashU community. We are looking for people with drive, independence and outstanding prior achievement, who are committed to leveraging interdisciplinary collaboration to drive forward the field of theoretical and computational neuroscience. Washington University in St Louis is ranked in the top 10 worldwide for Neuroscience and Behavior. Salary for CTCN Fellows is significantly above standard NIH postdoc rates, and funds for conference travel are included. In addition, WashU offers excellent benefits and comprehensive access to career development, professional and personal support. The St Louis metropolitan area has a population of almost 3M and is rich in culture, green spaces and thriving music and arts scenes, with a highly accessible cost of living.

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!

Non-invasive brain-machine interface control with artificial intelligence copilots

COSYNE 2025

Availability of information on artificial intelligence-enhanced hearing aids: A social media analysis

FENS Forum 2024

Constructing an artificial intelligence algorithm based on awake mouse brain calcium imaging as a rapid screening platform for the development of Parkinson's disease drugs

FENS Forum 2024

Development of NTS2-selective non-opioid analgesics using artificial intelligence

FENS Forum 2024