Seminars

Liver toxicity can emerge late in drug development when early assays fail to capture human biology, exposure or injury mechanisms. This webinar introduces Axiom’s hepatic profiling approach, combining multicellular liver systems, high-content imaging, transcriptomics, proteomics and ADME measurements. Its reasoning agent integrates those results with partner assays, chemical-series context, exposure estimates and clinical evidence to assess mechanistic liver-safety risks. Rather than using a single cell-toxicity score, the approach develops interpretable, series-specific structure–toxicity hypotheses, compares analogues, ranks compounds and suggests follow-up experiments or structural changes. Performance on established liver-injury reference compounds and newer molecules is examined through case studies, showing how multimodal evidence can inform medicinal-chemistry decisions and predicted therapeutic index.

Tim Ebbels presents interpretable machine-learning models for metabolomics and multi-omics using pathway and chemical-class scores. Sparse coverage of the metabolome and uncertain annotations make conventional results difficult to interpret; pathway-based features aim to connect model output directly to biological function. The webinar examines applications from multi-assay mass spectrometry and multi-omics integration to single-cell and mass-spectrometry imaging, discusses methodological challenges and attempts to resolve them, and demonstrates how these approaches can clarify complex biological signals. Online via Zoom. Free for interested life scientists and bioinformaticians, with advance registration required through the Australian BioCommons event page. Tuesday 13 October, 16:00–17:00 AEDT (Australia/Melbourne), equivalent to 05:00–06:00 UTC. The event is subject to the BioCommons Code of Conduct and may be recorded.

Real-time measurement of local biomolecule concentrations in living tissue requires sensors that are stable, selective and minimally invasive. This seminar examines single-walled carbon nanotubes, whose durable near-infrared fluorescence and sensitivity to their surroundings support optical biosensing. Biopolymer functionalization gives the nanotubes biological compatibility and selectivity for particular molecular targets. Spectroscopy, microscopy and machine-learning-assisted analysis are used to characterize sensor interactions and extract biological information, with applications spanning live-cell imaging, wearable sensing and continuous monitoring.

How can learning systems recommend actions that prevent an undesirable predicted outcome? Rehearsal learning approaches this problem through influence relations, but existing parametric assumptions can limit its use in complex systems. This talk presents a non-parametric method based on conditional mean embeddings. It expresses the objective in a reproducing kernel Hilbert space, replaces a discontinuous desirability indicator with a smooth Probit surrogate, and uses nested kernel ridge regression to estimate outcome distributions conditional on actions. The resulting estimator can be identified from observational data and has finite-sample error bounds and consistency guarantees. Synthetic and semi-synthetic experiments assess its effectiveness and flexibility.

Tom Nowakowski explains lineage barcoding and single-cell sequencing approaches for reconstructing neural stem-cell family trees and early wiring programs in developing mouse and human brains.

Lindsay N. Oberman reviews evidence for transcranial magnetic stimulation protocols in autism spectrum disorder. The seminar first explains how TMS works and how it can probe the brain mechanisms associated with autism. It then presents NIH research using repetitive TMS as an experimental therapeutic tool, examining its effects on both brain function and behaviour to guide the development of potential interventions. Online via Duke Zoom. Wednesday 7 October 2026, 16:00 EDT (America/New_York; UTC−4). Register through the event-specific Register Here link on the Duke Autism Seminar Series page to receive remote access. The presentation will not be recorded. Accommodation requests should reach the organizer at least ten days before the event. The speaker directs NIMH’s Developmental Clinical Neurophysiology and Neurostimulation Research Program.

Deep networks are often thought of as black boxes. Their ability to encompass vast swathes of knowledge indeed makes them hard to explain. Yet many of their behaviours — generalization under overparametrization, grokking, OOD failures, neural scaling laws — recur across architectures and scales, and each, however surprising, can be reproduced and explained in controlled settings. I will review these efforts to identify and explain the universal phenomena of deep learning, and suggest that an LLM may amount to a sum of such tractable sub-phenomena, interpolative in nature. Finally, leaving scientific rigor aside, I'll argue that what separates this prosaic picture from the apparent magic of LLMs may well be the industrial scale of compute and human labour behind it, and that AGI in its deeper extrapolative sense may be much further away than claimed.

Jie Yang presents BRIDGE, a multilingual evaluation benchmark built from real clinical tasks and more than one million samples derived from electronic health records. The benchmark addresses the gap between simplified examination-style tests and the complexity of clinical data, while enabling comparison of rapidly changing language models. Evaluation of 95 models involved over 24,000 experiments and 39 million predictions. Results vary substantially by model family, task and language, with some open-source systems matching proprietary models. The study also finds that chain-of-thought prompting frequently reduces accuracy on these tasks and examines stigmatizing language produced during model reasoning at large scale.

Matthias Vigl examines whether the computational scaling strategies behind modern machine learning can benefit particle physics. Empirical scaling laws relate model performance to computing resources and help balance model size against training-data volume. For a fixed dataset, increasing model capacity beyond the interpolation threshold can improve generalization through double descent, but gains eventually encounter limits imposed by the data. The talk then considers scaling data and model size together, deriving compute-optimal relations for transformer-based jet taggers trained on as many as billions of simulated jets. It studies how training hyperparameters and input representations change these relations. The observed behaviour suggests that larger computing budgets could yield useful gains in particle physics, especially with a move toward more general-purpose foundation models.

Jan Kosinski explains how AlphaFold-style methods predict protein complexes and why their reliance on evolutionary information makes interactions between host and pathogen proteins especially difficult. The talk reviews large-scale studies, interpreting both successful predictions and failures rather than treating reported success rates as universal performance. It then examines ways to improve predictions through broader sampling, altered sequence alignments and experimental restraints, including crosslinking mass spectrometry, with influenza A virus as a worked research example. The session is suitable for students and researchers interested in host-pathogen interactions and structural bioinformatics; basic knowledge of protein structures and sequence alignments is useful, but prior AlphaFold experience is unnecessary.

Robot missions require both collision-free trajectories and logical task ordering, such as collecting a resource before entering a restricted region. Augmented graphs of convex sets combine continuous trajectory optimization and discrete task sequencing in one problem. A layered graph constructed from an exact convex partition of free space selects an optimal task order and trajectory through a shortest-path calculation, exact up to finite Bézier parameterization. Its structure corresponds to Bellman–Held–Karp dynamic programming for the travelling-salesperson problem, retaining singly exponential worst-case complexity and improving exponentially on general temporal-logic tools. A library of specifications covers ordered collection, alternative keys, combined prerequisites, timing and conditional logic with correctness guarantees. Numerical benchmarks demonstrate exponential speedups and near-global optimality. Possible extensions include moving environments, safety-aware planning with conformal prediction sets and a large galactic-survey benchmark.

Anthony Letai discusses how understanding programmed cell death can guide the selection of cancer treatments. The seminar explains how apoptotic signalling can be measured and how these measurements can identify drugs capable of killing cancer cells. Hybrid: New Research Building Auditorium, Georgetown University Medical Center, Washington, DC, and Zoom. Monday 5 October 2026, 16:30–17:30 EDT (America/New_York; UTC−4). Use the attendance registration form or the event’s Zoom link on the organizer page. Organized by Georgetown’s Distinguished Scientist Seminar Series. Letai is Director of the National Cancer Institute. An in-person reception follows the seminar.

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.

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.

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.