Seminars

Yixuan He (Arizona State University) considers graph neural networks for social, financial and biological networks in which edge signs and directions contain information needed for clustering and prediction. After an introduction to graph neural networks, the talk explains how signed and directed methods preserve relationships lost when every connection is treated as unsigned and undirected. The seminar then asks when a representation learned before the final task can transfer to a different graph or to a setting with few labels. Two theoretical perspectives address transfer across graph domains and the use of link prediction for linear community detection. TopoDIG and TopoSIGN combine encoders that preserve edge structure with persistent topological features, enabling pre-training and prompt-based adaptation on directed and signed networks.

Antonio Ocello tests the idea that heavy-tailed noise improves diffusion and flow-based generative models by matching heavy-tailed data and encouraging diverse samples. Replacing Gaussian noise also changes the estimation task. A combined theoretical and experimental study derives sampling-error bounds for representative heavy- and light-tailed diffusion models, finding that heavy-tailed noise makes statistical estimation harder and produces less favourable bounds. Experiments with synthetic and real data recover the predicted trade-off. The results question whether heavy-tailed initialization reliably improves exploration of rare regions. This is joint work with Hamza Cherkaoui and Hélène Halconruy. Online via Microsoft Teams. Friday 2 October 2026 at 15:30 CEST / 13:30 UTC / 14:30 BST (Europe/Paris). Follow the Microsoft Teams link beneath this occurrence on the seminar page; the public route offers browser or app access. Ocello is affiliated with ENSAE Paris. Organized by GAMEX — Generative AI Modeling for Extreme Events, with GLE²N and CIRCE support; the series welcomes scientific exchange beyond the network.

Srinivasan Keshav (University of Cambridge) presents an interface designed to help ecologists use AI for remote sensing despite unfamiliarity with embeddings and algorithms. The Tessera Embeddings Explorer lets practitioners privately explore a region of interest, create habitat maps and perform regressions using their domain expertise and ground-truth knowledge. The talk demonstrates the tool and explains the design principles and the software architecture that follows from them, connecting usable ecological workflows with AI representations. Keshav is the Robert Sansom Professor of Computer Science at Cambridge.

Picower Institute Colloquium on the Brain and Cognition featuring Christopher Harvey, PhD, of Harvard University, held in Singleton Auditorium (46-3002) at MIT Building 46, 43 Vassar Street.

Alok Puranik (Jane Street) examines data mixing for large language models: allocating model capacity and computation among training sources. A small model’s preferred data mix may differ from that of a larger model, yet the cost of large-model training forces many decisions to depend on smaller experiments. Scaling laws can predict performance as size and computation increase only when trends remain stable or change predictably. The talk uses experiments from Puranik’s work and frontier laboratories to examine data-mix questions that violate these assumptions, explain why small-scale results can mislead, and explore better methods for choosing training data. His research at Jane Street concerns scaling laws for sequence models in trading.

Scott J. Russo examines how chronic psychosocial stress alters communication between the immune system and the brain in depression and anxiety. Human observations and mouse experiments connect persistent increases in circulating monocytes and inflammatory signals with stress-related illness. The talk describes stress-responsive neurons that project to bone marrow and regulate monocyte production, release and trafficking. It also considers how stress disrupts the blood–brain barrier, allowing peripheral inflammatory proteins to affect reward regions such as the nucleus accumbens. Together, these findings examine how peripheral immune cells act at the neurovascular interface to change brain circuits and behaviour, providing potential mechanisms relevant to treatment-resistant mood disorders. Online via Rutgers Brain Health Institute Zoom. Thursday 1 October 2026, 12:00–13:00 EDT (America/New_York; UTC−4). Open the organizer’s event page and follow its Zoom link; the meeting ID and passcode are supplied there. Hosted by Yong Kim as part of the BHI Plenary Seminar Series. This listing advertises the verified remote option.

Ivo D. Dinov examines augmented intelligence as a partnership between people and computational systems. The talk traces the technical and philosophical development of neural networks, then introduces complex-time, or kime, representations and the inference methods they support. Applications include medical imaging, healthcare and economic forecasting. It also considers the academic mission, societal consequences of AI and tensions between rapid technical development and human values, framing mathematical AI methods as extensions of human reasoning. Free hybrid seminar hosted by Wayne State University's Institute for AI and Data Science, AIDaS: CAD Seminar Series. Wednesday 30 September 2026, 14:30–15:30 EDT (America/Detroit; UTC−4). Attend in Room 1146, Faculty/Administration Building, 656 W. Kirby, Detroit, MI 48202, USA, or use the Zoom link on the official event page. Researchers, practitioners, students and faculty are welcome; the organizer encourages in-person attendance.

Navid Azizan presents methods that make neural models obey physical, safety and operational constraints at deployment. Hard-constrained neural networks, or HardNets, enforce input-dependent constraints by construction while preserving universal approximation within the feasible function class. Applications include models of chaotic dynamics with bounded trajectories, energy-constrained operator learning, safe reinforcement learning and control with formal guarantees. The talk then turns to enforcing constraints during sampling from pretrained diffusion and flow-matching models. Formulating generation as trajectory optimization allows receding-horizon control to guide outputs toward feasibility without retraining or imposing excessive restrictions on the sampling process. Examples from fluid dynamics, robot planning and control, PDE control and language-guided image editing illustrate how constraints can define admissible behavior while retaining expressive learning and generation.

Lenka Zdeborová introduces the Rules-and-Facts model, in which some observations follow a shared rule while others are isolated exceptions requiring memorization. The framework studies when a learner can acquire the rule and retain those exceptions simultaneously. Its results emphasize how capacity is organized and deployed, rather than capacity alone. The talk examines how regularization and the geometry of kernels or learned feature maps can reserve resources for memorization without undermining rule learning. It connects the balance between abstraction and memory to architectural and algorithmic choices, providing a theoretical account of neural networks that both generalize and recall specific facts.

Charlie Wilson examines how artificial intelligence affects energy transitions and carbon emissions, from data centres to the wider economy. The talk evaluates evidence behind competing claims, identifies optimism bias in assessments extending beyond data-centre electricity use, and presents research quantifying AI’s effects on global emissions pathways to 2050. It considers how aligning AI development with climate action changes the feasibility and cost of meeting long-term climate targets. Wilson is Professor at the Environmental Change Institute, University of Oxford. Takeshi Kuramochi provides commentary; Naoko Shimazu moderates. Online via Zoom Webinar. Wednesday 30 September 2026, 14:00–15:00 JST (Asia/Tokyo; UTC+9). Public advance registration is required through the organizer’s event page. The lecture is in English with Japanese interpretation. Organized by Tokyo College, The University of Tokyo Institutes for Advanced Study.

MIT Biology Colloquium Series talk by Peter Koo (Cold Spring Harbor Laboratory), hosted by Yunha Hwang, on using AI to learn the gene regulatory code.

Mansi Chhajed examines how the move from home sleep patterns to disrupted hospital sleep relates to cognitive outcomes after surgery. The session combines longitudinal sleep measurements with clinical assessments of postoperative delirium to identify baseline markers of vulnerability and consider practical ways to improve recovery. It connects sleep architecture, biological and environmental influences, and preoperative risk with proactive, sleep-informed patient care. Chhajed is a Clinical Research Coordinator at Massachusetts General Hospital. Online via Zoom Webinar. Tuesday 29 September 2026 at 14:00 ET / 13:00 CT (America/New_York; EDT, UTC−4). Organized by the Sleep Research Society — Virtual Seminar Series. Follow Register Today on the organizer's page to the event-specific Zoom form, which requests name and email. The form is publicly accessible; advance registration supplies webinar access.

Vanessa Siffredi explores why the corpus callosum is central to typical brain development, yet some children born without it or with callosal abnormalities develop comparably to their peers. The talk connects callosal structure and communication between hemispheres with cognition, attention, executive function, memory, language and socio-emotional development. Structural and functional neuroimaging studies of typical development and callosal dysgenesis examine how developing brains compensate for alterations in this major white-matter pathway. Siffredi is affiliated with CHUV and UNIL; Carole Guedj chairs the seminar. Hybrid: Auditoire de pédiatrie Fred Bamatter, HUG, Hôpital des Enfants, Geneva; online via Zoom. Tuesday 29 September 2026, 09:30–10:30 CEST (Europe/Zurich; UTC+2), followed by CIBM news and networking until 11:00. Organized by CIBM Center for Biomedical Imaging — Breakfast and Science. In-person attendance is free without registration. Remote attendees must register through the Zoom link on the event page and select the 29 September occurrence.

Irene Rombel discusses how artificial intelligence and machine learning can address bottlenecks, quality issues, and production costs in cell, gene, and RNA therapy manufacturing. The seminar focuses on real-world process-development applications that improve reproducibility, speed, and cost effectiveness.

Paco Villaescusa examines how simulations and artificial intelligence change the extraction of physical knowledge from precision cosmological surveys. The talk asks how much information lies in large-scale structure beyond power spectra, including nonlinear scales and higher-order statistics, and introduces the Quijote and Backlight simulation programmes. It then addresses the uncertain astrophysical processes that complicate small-scale inference through CAMELS, a framework combining cosmology, galaxy formation and machine learning. DREAMS is presented as a route to predicting dark-matter observables while accounting for baryonic uncertainties. The final part considers coupling simulations with increasingly capable AI: Denario is introduced as an agentic research system that generates hypotheses, designs analyses, writes and executes code, interprets results and refines research directions. The discussion considers what coordinated populations of agents and simulations spanning physical scales could make possible.

Merav Ahissar examines how expertise emerges through learning the statistics of task-relevant stimuli. Slow learning builds familiarity with stable regularities, such as syllable patterns in language, while fast adaptation supports flexible responses to changing situations, including surprising social interactions. The talk considers atypical learning dynamics in dyslexia and autism and how these may explain difficulties in linguistic and social skill acquisition, respectively. The discussion draws on Gertsovski and Ahissar, Trends in Cognitive Sciences, 2026. Speaker: Merav Ahissar, Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem. Organized by Cornell Cognitive Science. Friday 25 September 2026, 15:00–16:30 America/New_York (EDT). Attend online through the public Join via Zoom link on the official event page; the link opens Zoom’s app and browser join options. Cornell advertises the source event as hybrid, but this listing covers the verified online attendance route.

Mark Harnett presents work on how synaptic organization, nonlinear dendritic processing, and neuronal activity patterns interact to support computation, flexibility, and learning in the adult mammalian neocortex. The Brain and Cognitive Sciences colloquium is followed by a reception.

Bowen Li explains how ionizable lipids are designed for lipid nanoparticles that deliver messenger RNA. The seminar covers combinatorial and machine-learning-guided design, four-component reactions for rapidly producing lipid libraries, and barcoded screening in living systems to identify formulations targeting organs and cells, including lungs, T cells and tumours. It then examines applications of AI-guided nanoparticle platforms to mRNA therapeutics. Li is an Associate Professor and Canada Research Chair at the University of Toronto's Leslie Dan Faculty of Pharmacy. Online via Zoom. Thursday 24 September 2026, 11:00–12:00 Eastern Daylight Time (America/New_York; UTC−4). Use Join via Zoom on Western University's event page. The stated audience includes researchers, students, alumni and the general public. Organized by Western University's Western Bioinformatics Research Seminar.

Florian Marquardt surveys approaches to reducing the energy and computational cost of neural networks by implementing their functions in physical devices. The central question is how experiments can efficiently determine updates to adjustable parameters, allowing the hardware itself to learn. Examples include learning through time reversal, obtaining nonlinear computation from linear optical scattering, and training analog quantum simulators as neuromorphic systems. The seminar is available in person and through the registration link on the organizer’s event page.

Qihao Ye examines practical, affordable ways for individual mathematicians to use AI in literature searches, proof development and verification. The talk identifies seven recurring failure modes and introduces the open-source QED multi-agent system designed to address them. A case study on constructing Carleman weights for wave equations on the half-line shows how automated search can be combined with a researcher’s mathematical insight and rigorous checking. The organizer links both the implementation and the accompanying research paper. Online via Rutgers DIMACS Zoom. Wednesday 23 September 2026, 10:00–11:00 EDT (America/New_York; UTC−4). Use the Zoom link and passcode on the official event page. Organized by the DIMACS AI and Mathematics Seminar. This listing advertises the verified remote attendance option.