Seminars

Youth sport can support growth, physical health and community, but year-round schedules, early specialization and social-media pressure can also make participation stressful. Meredith Whitley examines changes in sports culture amid rising youth mental-health difficulties and presents a seven-part framework for healthier sport environments. The lecture considers how young athletes can develop life skills while retaining enjoyment of the game. Whitley’s research includes designing and evaluating school and community sport programmes and developing US mental-health guidelines for youth sport. Speaker: Meredith Whitley, PhD, Professor of Sport-Based Youth Development at Adelphi University and Research Fellow at Stellenbosch University’s Centre for Sports Leadership. Organized by the Alan Hu Foundation. Tuesday 13 October 2026, 17:30–18:30 America/Los_Angeles (Pacific Daylight Time). Free online Zoom webinar, with a 15-minute question-and-answer segment advertised. Register using REGISTER NOW on the official lecture page at least one day beforehand to receive the Zoom link. Simultaneous Chinese and Spanish interpretation is available.

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.

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.

Haruki Ebina discusses the marked power spectrum as a two-point approach to non-Gaussian density information, reducing the data-vector and covariance challenges of direct bispectrum analysis while retaining perturbative modelling. Recent work incorporates survey geometry, bringing applications to observations closer. Time permitting, he will also cover high-redshift DESI Run 2 cosmology and perturbative modelling of fast-radio-burst dispersion measures. Samuel Goldstein presents new constraints on fundamental physics from the cosmic microwave background and large-scale structure: axions using Atacama Cosmology Telescope observations, primordial non-Gaussianity, multi-field inflation using DESI, and component-separated temperature, polarization, thermal Sunyaev–Zeldovich and kinetic Sunyaev–Zeldovich maps from early Simons Observatory data. Speakers: Haruki Ebina, UC Berkeley, and Samuel Goldstein, Columbia University. Organized by Stanford KIPAC. Tuesday 29 September 2026, 10:40–11:30 America/Los_Angeles (PDT). Online via Zoom using Join the zoom meeting on the official event page. The organizer lists the intended audience as Faculty/Staff, Members and Students. Only remote attendance is verified for this listing.

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.

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.

Harvard T.H. Chan School of Public Health Department of Epidemiology seminar by Jeremy Labrecque, PhD, MSc (Assistant Professor of Epidemiology and Causal Inference, Erasmus MC, Rotterdam) on 'dark causal inference' — implied but unstated causal reasoning that pervades epidemiology — the misuse of 'association' language, and gaps in teaching causal reasoning.

Yuko Munakata presents evidence challenging capacity-only accounts of self-control and explains how experience, effort, and expected payoff shape executive-function engagement and later outcomes.

Margaret Livingstone of Harvard Medical School presents in Princeton Neuroscience Institute's public seminar series. Livingstone's laboratory studies how visual recognition occurs in the primate brain using behavior, brain imaging, and electrophysiology.

Arka Banerjee introduces k-nearest-neighbor distributions as summaries of cosmological survey data that capture information beyond two-point statistics. They respond to moments of all N-point correlations while retaining a computational cost comparable to two-point measurements. The talk covers auto-correlations and cross-correlations in discrete and continuous datasets, their relationship to other higher-order summaries, and applications that improve detection significance or parameter constraints. It also explores modeling these distributions with methods already successful for two-point functions in real and redshift space.

Rachna Reddy studies relationships among wild chimpanzees and bonobos to investigate adolescence as a period of both vulnerability and resilience. Drawing on more than a decade observing chimpanzees, the presentation brings together biological, anthropological, psychological and neuroscientific perspectives through cross-species empirical research. It connects questions about development in our closest living relatives with understanding the human life course; Reddy’s fellowship work also includes a coming-of-age novel informed by the lives of the chimpanzees she studies.

Sharan Banagiri uses gravitational-wave observations to investigate how stellar-mass binary black holes form. The first part of the fourth LIGO–Virgo–KAGRA observing run more than doubled the number of detections, revealing new population features and clarifying earlier ones. The talk surveys notable and puzzling subpopulations, relates them to formation channels, and connects the evidence with transient phenomena and compact-object astrophysics. As the sample grows, these identifiable subpopulations can support a broader physical account of binary black-hole formation.

Although stress can be considered as an ongoing process that helps an organism to cope with present and future challenges, when it is too intense or uncontrollable, it can lead to adverse consequences for physical and mental health. Social stress specifically, is a highly prevalent traumatic experience, present in multiple contexts, such as war, bullying and interpersonal violence, and it has been linked with increased risk for major depression and anxiety disorders. Nevertheless, not all individuals exposed to strong stressful events develop psychopathology, with the mechanisms of resilience and vulnerability being still under investigation. During this talk, I will identify key gaps in our knowledge about stress vulnerability and I will present our recent data from our contextual fear learning protocol based on social defeat stress in mice.

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

Functional connectomics reveals general wiring rule in mouse visual cortex