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DTSTART;TZID=America/Los_Angeles:20260105T160000
DTEND;TZID=America/Los_Angeles:20260105T170000
DTSTAMP:20260520T030631
CREATED:20251217T182411Z
LAST-MODIFIED:20251218T002005Z
UID:10005858-1767628800-1767632400@events.ucsc.edu
SUMMARY:AM Seminar with Dr. Truong Vu
DESCRIPTION:Presenter: Dr. Truong Vu\, IPAM and MSU \nDescription: We present a framework for the gradient flow of sharp-interface surface energies that couple to embedded curvature active agents. We use a penalty method to develop families of locally incompressible gradient flows that couple interface stretching or compression to local flux of interfacial mass. We establish the convergence of the penalty method to an incompressible flow both formally for a broad family of surface energies and rigorously for a more narrow class of surface energies. \nBio: Dr. Vu received a Ph.D. in Applied Mathematics from the Department of Mathematics\, Statistics\, and Computer Science at University of Illinois at Chicago. Dr. Vu is currently a Postdoctoral Fellow at the Institute for Pure and Applied Mathematics (UCLA) and a visiting faculty in the Department of Mathematics at Michigan State University. \nHosted by: Applied Mathematics 
URL:https://events.ucsc.edu/event/am-seminar/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/12/txvu.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20260520T030631
CREATED:20260112T164010Z
LAST-MODIFIED:20260112T164010Z
UID:10008343-1768233600-1768237200@events.ucsc.edu
SUMMARY:AM Seminar: Science in the Age of Foundation Models
DESCRIPTION:Presenter: Dr. Danielle Robinson\, AWS AI \nDescription: In this talk\, I will discuss the large impact of foundation models within the sciences with a particular focus on the importance of physical constraints and uncertainty quantification. First\, I will detail our novel ProbConserv framework for enforcing hard constraints within black-box deep learning models. ProbConserv provides uncertainty quantification\, and can be used to enforce conservation law constraints as well as other nonlinear constraints. Next\, I will discuss its extensions to ensembles of Neural Operators and out-of-distribution (OOD) estimations\, as well as how it can be used in constrained generative modeling of PDEs. I will then show applications of our work in computational fluid dynamics (CFD)\, including weather forecasting\, aerodynamics and chaotic systems. Lastly\, I will conclude with a forward-looking view of the next steps for designing a physics foundation model that can be applied across various types of flows\, geometries and boundary conditions\, and what is needed for such a model to be developed. \n\n\n\n\n\n\n\n\n\nBio: Danielle Maddix Robinson is a Senior Applied Scientist in the Machine Learning Forecasting Group within AWS AI. She graduated with her PhD in Computational and Mathematical Engineering from the Institute of Computational and Mathematical Engineering (ICME) at Stanford University. She was advised by Professor Margot Gerritsen and developed robust numerical methods to remove spurious temporal oscillations in the degenerate nonlinear Generalized Porous Medium Equation. She is passionate about the underlying numerical analysis\, linear algebra and optimization methods behind numerical PDEs and applying these techniques to deep learning. During her PhD\, she also did an internship at NVIDIA with Joe Eaton and Alex Fender\, and implemented an efficient and load-balanced sparse matrix vector multiplication (spmv) in cuSPARSE and nvGRAPH libraries. She is excited to be back at NVIDIA today. After graduating\, Danielle joined AWS in 2018\, and has been working on developing statistical and deep learning foundation models for time series forecasting including Chronos. Over the last several years\, she has been leading the research initiative on developing models for physics-constrained machine learning for scientific computing on the DeepEarth team. In particular\, she has researched how to apply ideas from numerical methods\, e.g.\, finite volume schemes\, to improve the accuracy of black-box ML models for PDEs with applications to ocean and climate models\, aerodynamics and chaotic systems. \n\n\n\nHosted by: Applied Mathematics\n\n\n\nLink: https://ucsc.zoom.us/j/96136632376?pwd=yb27lop8mnhnsairAPgezmVJZzFb74.1.
URL:https://events.ucsc.edu/event/am-seminar-science-in-the-age-of-foundation-models/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option-1.jpg
LOCATION: https://ucsc.zoom.us/j/96136632376?pwd=yb27lop8mnhnsairAPgezmVJZzFb74.1.
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T170000
DTEND;TZID=America/Los_Angeles:20260112T183000
DTSTAMP:20260520T030631
CREATED:20251209T200526Z
LAST-MODIFIED:20251218T001742Z
UID:10005751-1768237200-1768242600@events.ucsc.edu
SUMMARY:Be Inspired: Explore Graduate Studies in STEM
DESCRIPTION:Not sure if graduate school is right for you? \nJoin us to learn what graduate school is really about and explore whether it’s the right path for you. We’ll cover topics such as qualifying exams\, funding options\, common misconceptions\, and more! \nClick the link below to register for the event: \nhttps://ucsc.zoom.us/webinar/register/WN_31OHhwc7QPqJ7nSyiuAUNg
URL:https://events.ucsc.edu/event/be-inspired-explore-graduate-studies-in-stem/
CATEGORIES:Seminars,Workshop
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/12/Graduate-Student-Workshop-Flyer.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T120000
DTEND;TZID=America/Los_Angeles:20260126T130000
DTSTAMP:20260520T030631
CREATED:20260121T182735Z
LAST-MODIFIED:20260121T182735Z
UID:10009084-1769428800-1769432400@events.ucsc.edu
SUMMARY:Statistics Seminar: Boosting Biomedical Imaging Analysis via Distributed Functional Regression and Synthetic Surrogates
DESCRIPTION:Presenter: Guannan Wang\, Associate Professor\, The College of William & Mary \nDescription: 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\nbe 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. \nBio: 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. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/92479478035?pwd=S6b9SNtCorApA04sISbDwWqaF3wyPZ.1
URL:https://events.ucsc.edu/event/statistics-seminar-boosting-biomedical-imaging-analysis-via-distributed-functional-regression-and-synthetic-surrogates/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/01/option-3-2.png
LOCATION:https://ucsc.zoom.us/j/92479478035?pwd=S6b9SNtCorApA04sISbDwWqaF3wyPZ.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T160000
DTEND;TZID=America/Los_Angeles:20260126T170000
DTSTAMP:20260520T030631
CREATED:20260120T184336Z
LAST-MODIFIED:20260120T184604Z
UID:10008394-1769443200-1769446800@events.ucsc.edu
SUMMARY:AM Seminar: Probing Forced Responses and Causality in Data-Driven Climate Emulators: Conceptual Limitations and the Role of Reduced-Order Models
DESCRIPTION:Presenter: Fabrizio Falasca\, New York University \nDescription: 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. \nBio: 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. \n\n\n\n\n\nHosted by: Applied Mathematics \nZoom Link: https://ucsc.zoom.us/j/97450297092?pwd=Bp4GIgR8dAuBeCd1Sz9vXo8unkYWQW.1
URL:https://events.ucsc.edu/event/am-seminar-probing-forced-responses-and-causality-in-data-driven-climate-emulators-conceptual-limitations-and-the-role-of-reduced-order-models/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option2.jpg
LOCATION: https://ucsc.zoom.us/j/97450297092?pwd=Bp4GIgR8dAuBeCd1Sz9vXo8unkYWQW.1
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