ºÃÉ«ÏÈÉúTV

March 27, 2026

, University of Wisconsin

 Title: Invisible Cities: Imagining the next era of AI-enabled fundamental physics research

Abstract: This talk explores the nuanced relationship between data and methodology. Fundamental physics data arguably has several qualities that are relatively under-explored in mainstream machine learning settings — but to what extent will these qualities actually require specialized treatment and novel methods? I will present an overview of several recent ML methods that successfully reflect or exploit particular qualities of fundamental physics data. Ultimately, however, I will propose that broadening our analysis strategies across datasets, detectors, and even scientific disciplines via large-scale and highly multimodal foundation models could be critical to yielding major insights to come. 

Bio: Mariel Pettee is an assistant professor of Physics and the Bernice Durand Faculty Fellow at the University of Wisconsin—Madison. Her research involves developing ML methods for particle physics and astrophysics applications as well as building scientific foundation models, with a particular focus on representation learning in multimodal and multidisciplinary scientific data. Previously, she was a Chamberlain Postdoctoral Fellow at Lawrence Berkeley National Laboratory and received her PhD in Physics from Yale University. 

April 17, 2026 - HYBRID EVENT

, University of Missouri

Location: Zoom, Posner 151

Title: Flexible and Efficient Spatial Extremes Estimation and Emulation via Extreme-
Aware Variational Autoencoders

Abstract: The world is full of extreme events. For example, a central question in public
health planning might be to assess the likelihood of extreme exposures
(meteorological conditions, air pollution, social stress, etc.). Similarly, complex
phenomena such as atmospheric turbulence and large wildfires can be viewed
as extremes. Such extreme events typically occur in spatial and/or temporal
clusters. Yet, the standard methodologies that statisticians deal with spatially
dependent processes (Gaussian processes and Markov random fields) are not
suitable for complex tail dependence structures. More flexible spatial extremes
models exhibit appealing extremal dependence properties but are often
exceedingly prohibitive to fit and simulate from in high dimensions. Here I present
recent work where we develop a new spatial extremes model that has flexible
non-stationary extremal dependence properties, and we integrate it in the
encoding-decoding structure of a extremes-aware variational autoencoder
(XVAE), whose parameters are estimated via variational Bayes combined with
deep learning. The XVAE is amortized and can be used to efficiently analyze
high-dimensional data or as a spatio-temporal emulator that characterizes the
distribution of data or mechanistic model output states and produces outputs that
have the same statistical properties as the inputs, especially in the tail. Through
extensive simulation studies, we show that our XVAE is substantially more time-
efficient than traditional Bayesian inference for such models, while also
outperforming many spatial extremes models with a stationary dependence
structure. We demonstrate our method applied to a high-resolution satellite-
derived dataset of sea surface temperature in the Red Sea and to a high-
resolution simulation model of a turbulent plume, such as one would find in a
wildfire. We present some current extensions.
This is joint work with Likun Zhang and Xiaoyu Ma (University of Missouri),
Raphael Huser (KAUST), and Kiran Bhaganagar (University of Texas-San
Antonio).

Bio: Christopher K. Wikle is Curators’ Distinguished Professor of Statistics at U. Missouri
(MU), with additional appointments in Soil, Environmental and Atmospheric Sciences
and the Truman School of Public Affairs. He is currently the Director of the College of
Arts and Science Center for Spatio-Temporal Statistics and AI. He obtained his Ph.D.
from Iowa State University in 1996 and has been on the faculty at MU for 28 years. His
research specialty is spatio-temporal statistics, with primary applications to geophysical
processes, complex biological processes, and the environment. He focuses on
developing computationally efficient deep hierarchical Bayesian dynamic spatio-
temporal models motivated by scientific principles, with more recent work at the
interface of deep neural modeling and statistics. He is Fellow of the ASA, IMS, ISI, and
AAAS and has published 2 award winning books in spatio-temporal statistics. Dr. Wikle
is Associate Editor for several journals and is one of six inaugural members of the
Statistics Board of Reviewing Editors for the AAAS flagship journal, Science.