Events

Statistics Seminar: Boosting Biomedical Imaging Analysis via Distributed Functional Regression and Synthetic Surrogates
2026-01-26 12:00 - 13:00 (America/Los_Angeles) | https://ucsc.zoom.us/j/92479478035?pwd=[REDACTED]
Presenter: Guannan Wang, Associate Professor, The College of William & Mary
Description: Generative AI has emerged as a powerful tool for synthesizing biomedical images, offering new solutions to challenges such as data scarcity, privacy constraints, and modality imbalance. However, the reliable use of synthetic images in scientific analysis requires principled statistical frameworks that can assess fidelity and rigorously quantify uncertainty. In this talk, I present a distributed functional data analysis approach for comparing original and AI- generated biomedical images through their mean and covariance structures. Using spline-based representations on complex imaging domains, we construct simultaneous confidence regions, enabling formal inference on original-synthetic differences and providing statistical safeguards for downstream analyses. Building on this foundation, I demonstrate how synthetic images can
be safely incorporated into functional regression models to learn spatially varying covariate effects when key imaging modalities are partially observed. Applications to large-scale neuroimaging studies illustrate how integrating generative AI with rigorous statistical inference enhances the reliability, interpretability, and scientific value of modern biomedical imaging analyses.
Bio: Guannan Wang is a Diamond Term Distinguished Associate Professor in the Department of Mathematics at William & Mary. She received a Ph.D. in Statistics and an M.S. in Computer Science from the University of Georgia in 2015. Her research focuses on the statistical foundations of generative AI, distributed and federated learning, and spatial and functional data analysis, with applications to neuroimaging, public health, and environmental and social sciences. She has published over 30 peer-reviewed articles in leading statistical journals, including JASA, JCGS, Statistica Sinica, Biometrics, and JMLR, and her work has been supported by the NIH, NSF, and the Simons Foundation.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/92479478035?pwd=[REDACTED]

AM Seminar: Probing Forced Responses and Causality in Data-Driven Climate Emulators: Conceptual Limitations and the Role of Reduced-Order Models
2026-01-26 16:00 - 17:00 | https://ucsc.zoom.us/j/97450297092?pwd=[REDACTED]
Presenter: Fabrizio Falasca, New York University
Description: A central challenge in climate science and applied mathematics is developing data-driven models of multiscale systems that capture both stationary statistics and responses to external perturbations. Current neural climate emulators aim to resolve the atmosphere–ocean system in all its complexity but often struggle to reproduce forced responses, limiting their use in causal studies such as Green’s function experiments. To explore the origin of these limitations, we first examine a simplified dynamical system that retains key features of climate variability. We argue that the ability of emulators of multiscale systems to reproduce perturbed statistics depends critically on (i) the choice of an appropriate coarse-grained representation and (ii) careful parameterizations of unresolved processes. These insights highlight reduced-order models, tailored to specific goals, processes, and scales, as valid alternatives to general-purpose emulators. We next consider a real-world application, developing a neural model to investigate the joint variability of the surface temperature field and radiative fluxes. The model infers a multiplicative noise process directly from data, largely reproduces the system’s probability distribution, and enables causal studies through forced responses. We discuss its limitations and outline directions for future work. These results expose key challenges in data-driven modeling of multiscale physical systems and underscore the value of coarse-grained, stochastic approaches.Throughout, we propose linear response theory as a rigorous framework for evaluating neural models beyond stationary statistics, probing causal mechanisms, and guiding model design.
Bio: Fabrizio Falasca is physicist working at the intersection of statistical physics, applied mathematics and climate science. He acquired his master degree in Physics of Complex Systems in the University of Turin in Italy. He then moved to Atlanta to pursue a PhD in Climate Science under the supervision of Annalisa Bracco. In the last 5 years he has been working in the Courant Institute of Mathematical Science in the group of Laure Zanna. His work span response theory, causal inference, data-driven modeling, and their applications to climate dynamics and change.

Hosted by: Applied Mathematics
Zoom Link: https://ucsc.zoom.us/j/97450297092?pwd=[REDACTED]

Statistics Seminar: Inferring Unobserved Trajectories from Multiple Temporal Snapshots
2026-01-28 12:00 - 13:00 | https://ucsc.zoom.us/j/93769232971?pwd=[REDACTED]
Presenter: Yunyi Shen, Ph.D. Candidate, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Description: Practitioners often aim to infer an unobserved population trajectory using sample snapshots at multiple time points. E.g. given single-cell sequencing data, scientists would like to learn how gene expression changes over a cell’s life cycle. But sequencing any cell destroys that cell. So we can access data for any particular cell only at a single time point, but we have data across many cells. The deep learning community has recently explored using Schrödinger bridges (SBs) and their extensions in similar settings. However, existing methods either (1) interpolate between just two time points or (2) require a single fixed reference dynamic (often set to Brownian motion within SBs). But learning piecewise from adjacent time points can fail to capture long-term dependencies. And practitioners are typically able to specify a model family for the reference dynamic but not the exact values of the parameters within it. So I propose a new method that (1) learns the unobserved trajectories from sample snapshots across multiple time points and (2) requires specification only of a family of reference dynamics, not a single fixed one. I demonstrate the advantages of my method on simulated and real data, across applications in biology and oceanography.
Bio: Yunyi Shen is currently a Ph.D. candidate in the Department of Electrical Engineering and Computer Science at MIT. He works in probabilistic machine learning and statistics on problems where data are scarce or noisy, and as a result require adaptive data collection, incorporation of domain-specific structure, and careful downstream evaluation. Drawing on a background in the physical and life sciences, his work is shaped by close interdisciplinary collaborations and motivated by scientific problems in biology and physics, such as gene regulation, fluid dynamics in cells, wildlife monitoring, and time-domain astronomy.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/93769232971?pwd=[REDACTED]
