Fall 2025 Lecture in Climate Data Science: STEPHAN MANDT

LEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.

LEAP Center

JOIN THE LEARNING THE EARTH WITH ARTIFICIAL INTELLIGENCE + PHYSICS (LEAP) CENTER AT COLUMBIA FOR A SEMINAR ON CLIMATE DATA SCIENCE.

Columbia Innovation Hub - Tang Family Hall

<h3><strong><strong>TITLE: "</strong></strong><strong><strong>SCIENTIFIC INFERENCE WITH DIFFUSION GENERATIVE MODELS"</strong></strong></h3><h3><strong><strong>SPEAKER: STEPHAN MANDT (UC Irvine)</strong></strong></h3><p><br></p><h3>Date: October 30, 2025</h3><h3>Time: 12:00 p.m.</h3><h3>Format: Hybrid</h3><h3>Virtual: Zoom link provided upon registration</h3><h3>In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027</h3><p><br></p><p>*Please note that in-person space is limited.*</p><p>__________________________________________________________________________________</p><p><strong><strong>Abstract</strong></strong>: Diffusion models have revolutionized generative modeling in vision and language. But can they also serve as tools for scientific inference? I present a Bayesian view on diffusion models as tools for solving inverse problems—inferring hidden states or parameters from noisy observational data—with applications ranging from climate modeling to imaging. Unlike creative applications, scientific use cases demand more than photorealism: they require calibrated uncertainty, faithful distributions, efficient conditional sampling, and the capacity to represent extreme and heavy-tailed data. I will highlight four recent advances addressing these needs: Variational Control for expressive conditional generation (ICML ’25), Heavy-Tailed Diffusion Models for sparse and extreme-valued data (ICLR ’25), Conjugate Integrators for fast conditional sampling without retraining (NeurIPS ’24), and Generative Uncertainty for assessing epistemic uncertainty in generation (UAI ’25). Together, these results point to diffusion models as principled engines of scientific inference.</p><p><strong><strong>Bio</strong></strong>: Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. His research focuses on the foundations and applications of generative AI, including probabilistic generative modeling of 2D, 3D, and sequential data; statistical inference algorithms; resource-efficient learning; and AI for scientific discovery. He is a Chan Zuckerberg Initiative Investigator and AI Resident, and has received the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award, and a Kavli Fellowship. Prior to joining UCI, he led the machine learning group at Disney Research and held postdoctoral positions at Princeton University and Columbia University. He frequently serves as a Senior Area Chair for NeurIPS, ICML, and ICLR, and has recently been Program Chair (AISTATS 2024) and General Chair (AISTATS 2025).</p><p><br></p><p>Learn More: <a href="https://leap.columbia.edu" title="https://leap.columbia.edu" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong><strong>LEAP</strong></strong></a></p>

<p><strong><strong>STEPHAN MANDT</strong></strong></p><p>UC Irvine</p>

Summer 2026 Lecture in Climate Data Science: LILY XU

Summer 2026 Lecture in Climate Data Science: COURTNEY COGBURN

Spring 2026 Lecture in Climate Data Science: Research Update by KARA LAMB

Summer 2026 Lecture in Climate Data Science: ANN LEE

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Fall 2025 Lecture in Climate Data Science: STEPHAN MANDT

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