Seminars

UCL Department of Neuroinflammation Seminar by Professor Emad Moeendarbary (Professor of Cell Mechanics and Mechanobiology, UCL Mechanical Engineering) on how biophysical factors such as substrate stiffness and axon geometry regulate oligodendrocyte behavior and myelin formation, presenting the AxoMetic in vitro myelination platform for discovery of remyelinating therapies.

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.

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.

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.

ISTA Lecture by Klaus Robert Müller (TU Berlin & Korea University, Seoul) on how machine learning and AI enable scientific research, particularly in medicine and chemistry, and on explainability techniques for extracting understanding from machine learning models.

This seminar discusses a proof-of-concept gravitational-waveform model described in arXiv:2511.10522. It augments post-Newtonian equations using physics-informed neural-network methods and universal differential equations. The talk also covers ongoing development of a precessing surrogate model designed to evaluate in less than one millisecond while covering a broader portion of the LVK frequency band.

Tal Linzen, an associate professor of linguistics and data science at New York University and a research scientist at Google, will speak in MIT's Siegel Family Quest for Intelligence seminar series. His work combines behavioral experiments and computational methods to study language learning and comprehension, alongside large-language-model post-training, evaluation, and interpretability.

Debashree Ghosh discusses quantum-chemistry approaches for strongly correlated polyaromatic hydrocarbons, including acenes and carotenoids. The talk examines why large valence active spaces are needed to describe optical properties and singlet–triplet gaps, and how density-matrix renormalization-group methods address these systems. Changes in molecular topology and odd-membered rings are connected to spin frustration and optical behaviour. The seminar also covers neural-network configuration interaction, connections between artificial neural networks and matrix-product-state wavefunctions, and restricted-Boltzmann-machine approaches to quantum wavefunctions. Applications include singlet fission and multiexcitonic phenomena. The hybrid event takes place in ICTP’s Euler Lecture Hall and online; the official event page provides the registration link.

Michelle M. Li examines how computational analyses can preserve the biological context of a proposed treatment, including cell type, disease state, genetic background and patient characteristics. She introduces Medea, an AI system that combines biological software, predictive models and literature retrieval while checking intermediate steps and reconciling evidence. The seminar presents evaluations involving cell-specific target selection, cancer-cell synthetic lethality and immunotherapy response. A separate yeast experiment tests predictions against previously unpublished measurements of gene-pair interactions under DNA-damaging treatments. The research addresses whether an agent can transfer useful evidence between contexts while recognizing when that transfer is unsupported. Reported comparisons cover predictive performance, computational failures and the ability to abstain. This recording retains the original seminar date.

Convergence Seminar by Alexis Battle, PhD (Wu and Zhang Professor of Biomedical Engineering and Computer Science, Johns Hopkins University), on computational and machine-learning approaches for analyzing how genetic variation impacts gene regulation and disease, including noncoding sequences and rare variants.

Jiaqi Zhang examines how computational models can learn the effects of genetic interventions from single-cell experiments. Such experiments reveal causal relationships, but their high-dimensional measurements are costly to collect and difficult to interpret. The seminar connects identifiable causal representations with a predictive method for previously unseen perturbations. The approach incorporates prior biological knowledge and changes in data distributions to estimate responses at individual-cell resolution. It also uses predictions to guide subsequent experiments. An application identifies and experimentally validates previously unknown T-cell regulators with potential relevance to cancer immunotherapy. The recording follows the original July seminar; the series lists Zhang at MIT, while the recording biography describes her incoming Columbia appointment.

Benoit Assi examines limits on reliable theoretical predictions for present and future particle colliders. Low-order simulations of QCD radiation carry large uncertainties, hadronization is commonly modeled rather than derived, and effective theories contain more operators than measurements can constrain. Information theory and machine learning can incorporate improved calculations into simulations, quantify uncertainty, choose informative observables and advance hadronization theory. Geometry of effective-theory field space combines infinite operator families into finite physical quantities, while information measures identify the combinations experiments can resolve. The talk develops these complementary approaches to extracting the available information from LHC and future-collider data.

Useful internal representations should explain the patterns of regularities and dependencies among observations. Probabilistic graphical models promise a principled way to uncover latent factors as such, but they are hard to scale to handle high-dimensional sensory observations and complicated dependencies structures. Neural-networks, on the other hand, excel at approximating complicated high-dimensional functions, but their internal representations do not easily lend themselves to a probabilistic interpretation. Despite some successes, a general unified approach is still missing for integrating the two approaches. I will describe a novel approach towards merging adaptive neural-network components into a probabilistic framework, based on three core ideas. The first is to train a set of networks to collectively perform inference, leveraging the ability of pattern-recognition methods to amortise complicated transformations. The second is to constrain the way in which the outputs of these networks are interpreted, transformed, and combined together. These constraints, together with the learning objective itself, are derived directly from probabilistic considerations encoded in a graphical model. Finally, the third core idea is that of recognition-parametrisation, allowing the inference ("recognition") procedure to directly define the model itself, without requiring an explicit "generative" decoder. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-03-04. Recording duration: 00:48:26.

1. Can we reconstruct images that a person saw, directly from their fMRI brain recordings? 2. Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network? The answer to both of these intriguing questions is “Yes!” In this talk I will present some of our work in both domains. I will then show how combining the power of Brains and Machines can lead to significant breakthroughs in both areas, and potentially bridge the gap between Minds and Machines. Finally, I will show how combining the power of Multiple Brains (with NO shared data) may lead to new breakthrough discoveries in Brain-Science, and allow mapping of information between different brains. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-02-18. Recording duration: 00:50:24.

Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher processing stages. Instead, it suppresses the most predictable spikes and transmits a compressed representation of the input. Using only biologically plausible spike-timing based learning rules, PCL reproduces a wealth of findings on information processing in visual cortex and permits strong performance in downstream classification tasks. Overall, PCL offers a new approach to predictive coding and its implementation in natural and artificial spiking neural networks

Robert Gower introduces Polar Express for the polar decomposition and matrix sign function, motivated by Muon neural-network training. Using only matrix-matrix products makes the method suited to high-throughput GPUs. Each iteration adapts its polynomial update through minimax optimization, building on Chen and Chow and Nakatsukasa and Freund. Worst-case error minimization gives rapid initial and asymptotic convergence. The talk addresses finite-precision implementation in bfloat16 and reports improved validation loss when training GPT-2 on one billion FineWeb tokens across several learning rates.