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DTSTART;TZID=America/Los_Angeles:20260504T160000
DTEND;TZID=America/Los_Angeles:20260504T170000
DTSTAMP:20260429T174906Z
CREATED:20260312T222740Z
LAST-MODIFIED:20260429T174906Z
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SUMMARY:Statistics Seminar: Advancing Statistical Rigor in Single-Cell and Spatial Omics Using In Silico Control Data
DESCRIPTION:Presenter: Guan’ao Yan\, Assistant Professor\, Michigan State University \nDescription: Single-cell and spatial transcriptomics technologies now let us map cellular diversity and tissue organization at high resolution\, but the computational methods built to analyze these data are difficult to evaluate in a rigorous\, reproducible way. Two key barriers are the lack of realistic synthetic data with known ground truth and the ambiguity in how we define biologically meaningful spatial patterns. This talk will introduce two simulation frameworks—scReadSim for single-cell RNA-seq and ATAC-seq data\, and scIsoSim for isoform-level expression and splicing—that generate realistic sequencing reads while preserving user-specified truth. These tools enable fair\, controlled benchmarking of quantification and splicing methods across experimental protocols. The talk will also present a systematic review of 34 methods for detecting spatially variable genes (SVGs) in spatial transcriptomics data\, proposing a new categorization of SVGs and outlining how future benchmarks should be designed. Overall\, the goal is to improve statistical rigor\, interpretability\, and comparability in single-cell and spatial omics analysis. \nBio: Guan’ao Yan is an Assistant Professor of Computational Mathematics\, Science & Engineering at Michigan State University. He received his Ph.D. in Statistics from UCLA. His research focuses on statistical and computational methods for modern statistical genomics\, particularly single-cell and spatial omics\, with an emphasis on rigorous benchmarking\, interpretability\, and biomedical discovery. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-advancing-statistical-rigor-in-single-cell-and-spatial-omics-using-in-silico-control-data/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/03/Guanao-scaled.jpeg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260331T181211Z
CREATED:20260331T181211Z
LAST-MODIFIED:20260331T181211Z
UID:10011822-1776700800-1776704400@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Hierarchical Clustering with Confidence
DESCRIPTION:Presenter: Snigdha Panigrahi\, Associate Professor\, Department of Statistics\, University of Michigan \nDescription:Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However\, its greedy nature makes it highly sensitive to small perturbations in the data\, often producing different clustering results and making it difficult to separate genuine structure from spurious patterns. In this talk\, I will show how randomizing hierarchical clustering can be useful not just for measuring stability but also for designing valid hypothesis testing procedures based on the clustering results. We propose a simple randomization scheme to construct valid p-values at each node of a hierarchical clustering dendrogram\, quantifying evidence against greedy merges while controlling the Type I error rate. Our method applies to any linkage without case-specific derivations\, is substantially more powerful than existing selective inference approaches\, and provides an estimate of the number of clusters with a probabilistic guarantee on overestimation. \nBio:Snigdha Panigrahi is an Associate Professor of Statistics at the University of Michigan\, where she also holds a courtesy appointment in the Department of Biostatistics. She received her PhD in Statistics from Stanford University in 2018 and has been a faculty member at Michigan since then. Her research focuses on converting purely predictive machine learning algorithms into principled inferential methods. She is an elected member of the International Statistical Institute\, and her work has been recognized with an NSF CAREER Award and the Bernoulli New Researcher’s Award. Her editorial service\, past and present\, includes Journal of Computational and Graphical Statistics\, Bernoulli\, and Journal of the Royal Statistical Society: Series B. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-hierarchical-clustering-with-confidence/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/03/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260331T180549Z
CREATED:20260331T180549Z
LAST-MODIFIED:20260331T180549Z
UID:10011821-1776700800-1776704400@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Variational Inference and Density Estimation with Non-Negative Tensor Train
DESCRIPTION:Presenter: Dr. Xun Tang\, Stanford University \nDescription: This talk covers an efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format. The two settings we consider are variational inference and density estimation\, whereby one has access to either the unnormalized analytic formula of the distribution or the samples generated from the distribution. In particular\, the compression is done through a two-stage approach. In the first stage\, we use existing subroutines to encode the distribution function in a tensor train format. In the second stage\, we use an NTT ansatz to fit the obtained tensor train. For the NTT fitting procedure\, we use a log barrier term to ensure the positivity of each tensor component\, and then utilize a second-order alternating minimization scheme to accelerate convergence. In practice\, we observe that the proposed NTT fitting procedure exhibits drastically faster convergence than an alternative multiplicative update method that has been previously proposed. Through challenging numerical experiments\, we show that our approach can accurately compress target distribution functions. \nBio: Xun Tang is a postdoc in Stanford University\, department of mathematics\, hosted by Prof. Lexing Ying. Xun works on tensor network methods for scientific computing and data science\, and Xun also works on optimal transport algorithms. Xun will join HKUST department of mathematics in August 2026 as an incoming assistant professor. \nHosted by: Applied Mathematics Department
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-variational-inference-and-density-estimation-with-non-negative-tensor-train/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/03/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260413T160000
DTEND;TZID=America/Los_Angeles:20260413T170000
DTSTAMP:20260312T223836Z
CREATED:20260312T223749Z
LAST-MODIFIED:20260312T223836Z
UID:10011318-1776096000-1776099600@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Calibration Weighting-Style Diagnostics for Nonlinear Bayesian Hierarchical Models
DESCRIPTION:Presenter: Dr. Ryan Giordano\, UC Berkeley Statistics \nDescription: Multilevel Regression with Post-stratification (MrP) has become a workhorse method for estimating population quantities using non-probability surveys\, and is the primary alternative to traditional survey calibration weights\, e.g.~ as computed by raking. For simple linear regression models\, MrP methods admit “equivalent weights”\, allowing for direct comparisons between MrP and traditional calibration weights (Gelman 2006). In the present work\, we develop a more general framework for computing and interpreting “MrP local equivalent weights” (MrPlew)\, which admit direct comparison with calibration weights in terms of important diagnostic quantities such as covariate balance\, frequentist sampling variability\, and partial pooling. MrPlew is based on a local approximation\, which we show in theory and practice to be accurate and meaningful for the target diagnostics. Importantly\, MrPlew can be easily computed based on existing MCMC samples and conveniently wraps standard MrP software implementations. \nBio: Dr. Ryan Giordano is currently an assistant professor of statistics at UC Berkeley. Dr. Ryan Giordano earned a PhD in Statistics from UC Berkeley advised by Michael Jordan\, Tamara Broderick\, and Jon McAuliffe\, an MSc with distinction in econometrics and mathematical economics from the London School of Economics\, and undergraduate degrees in mathematics and engineering mechanics from the University of Illinois in Urbana-Champaign. Dr. Ryan Giordano has worked as a postdoctoral researcher at MIT under Tamara Broderick\, as an engineer for Google and HP\, and served for two years as an education volunteer in the US Peace Corps in Kazakhstan. Dr. Ryan Giordano’s research interests include machine learning\, variational inference\, Bayesian methods\, robustness quantification\, and what it even means to do statistics at all. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-calibration-weighting-style-diagnostics-for-nonlinear-bayesian-hierarchical-models/
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260318T171956Z
CREATED:20260318T171956Z
LAST-MODIFIED:20260318T171956Z
UID:10011340-1775491200-1775494800@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Some Recent Results on Transfer Learning
DESCRIPTION:Presenter: Oscar Hernan Madrid Padilla\, Assistant Professor\, University of California\, Los Angeles \nDescription: In the first part of the talk\, I will introduce TRansfer leArning via guideD horseshoE prioR (TRADER)\, a novel approach enabling multi-source transfer through pre-trained models in high-dimensional linear regression. TRADER shrinks target parameters towards a weighted average of source estimates\, accommodating sources with different scales. Theoretical investigation shows that TRADER achieves faster posterior contraction rates than standard continuous shrinkage priors when sources align well with the target while preventing negative transfer from heterogeneous sources. Extensive numerical studies and a real-data application demonstrate that TRADER improves estimation and inference accuracy over state-of-the-art transfer learning methods. In the second part of the talk\, I will discuss some ongoing work involving transfer learning in nonparametric regression with ReLU networks \nBio: Oscar Madrid Padilla is a tenure-track Assistant Professor in the Department of Statistics at the University of California\, Los Angeles. Previously\, from July 2017 to June 2019\, he was a Neyman Visiting Assistant Professor in the Department of Statistics at the University of California\, Berkeley. Before that\, he earned his Ph.D. in Statistics from The University of Texas at Austin in May 2017 under the supervision of Professor James Scott. He completed his undergraduate degree\, a B.S. in Mathematics\, at CIMAT in Mexico in April 2013. \nHosted by: Statistics Department 
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-some-recent-results-on-transfer-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/03/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260325T181208Z
CREATED:20260204T222651Z
LAST-MODIFIED:20260325T181208Z
UID:10009162-1775491200-1775494800@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: The Thinking Eye: AI That Sees\, Reads\, and Reasons in Medicine
DESCRIPTION:Presenter: Yuyin Zhou\, Assistant Professor\, UCSC \nDescription: Medical AI is undergoing a profound transformation\, evolving from simple pattern recognition to systems capable of complex clinical reasoning. This talk will chart this evolution across three dimensions: data\, models\, and evaluation. I will first highlight the shift from limited\, unimodal datasets to massive multimodal resources. In particular\, I will introduce MedTrinity-25M—a novel collection of over 25 million richly annotated medical images that serves as a foundation for multimodal tasks such as visual question answering and report generation. Building on this\, I will describe how grounding decision processes in a structured medical knowledge graph enables the generation of high-fidelity reasoning chains. Using these chains\, we construct a large-scale medical reasoning dataset\, which in turn allows us to develop a new class of reasoning models. These models not only achieve state-of-the-art performance on multiple clinical Q&A benchmarks but also produce reasoning outputs that physicians across seven specialties have independently verified as clinically reliable\, interpretable\, and more factually accurate than existing large language models. Finally\, the talk will offer a deep dive into the critical evaluation of these advanced models\, moving beyond standard benchmarks to expose their current limitations—particularly in interpreting dynamic clinical scenarios such as tracking disease progression from temporal image sequences. To foster a holistic understanding of the mechanisms underlying these reasoning models\, I will introduce a new evaluation framework that examines performance from two complementary perspectives: their grasp of static knowledge versus their capacity for dynamic reasoning. Together\, these advances point toward a future where AI systems can holistically analyze patient information and function as true collaborative partners in complex medical decision-making. \nBio: Yuyin Zhou is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz. Her research interests lie at the intersection of machine learning and computer vision\, with a primary focus on AI for healthcare and scientific discovery. Her work (70+ peered-reviewed publications with18\,000+ citations) has been recognized with honors including 2025 Google Research Scholar Award\, Best Paper Award at KDD 2025 Health Day and at Computerized Medical Imaging and Graphics 2024\, 2023 Hellman Fellowship\, Best Paper Honorable Mention at DART 2022\, and finalist recognition for the MICCAI Young Scientist Publication Impact Award in 2022. Beyond her research\, Yuyin has organized over 20 workshops and tutorials at major conferences including ICML\, MICCAI\, ML4H\, ICCV\, CVPR\, and ECCV\, with coverage in media outlets such as ICCV Daily and Computer Vision News. She serves as a regular Area Chair for CVPR\, ICLR\, MICCAI\, CHIL\, and ISBI\, an associate editor for SPIE medical imaging\, Image and Vision Computing\, and was the Doctoral Consortium Chair for WACV 2025. \nHosted by: Applied Mathematics Department
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-the-thinking-eye-ai-that-sees-reads-and-reasons-in-medicine/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260330T160000
DTEND;TZID=America/Los_Angeles:20260330T170000
DTSTAMP:20260325T182049Z
CREATED:20260325T182049Z
LAST-MODIFIED:20260325T182049Z
UID:10011767-1774886400-1774890000@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar:  Flexible Filaments and Swimming Cups: Just Go with the Flow
DESCRIPTION:Presenter: Lisa Fauci\, Professor\, Tulane University \nDescription: The motion of waving or rotating filaments in a fluid environment is a common element in many biological and engineered systems. Examples at the microscale include chains of diatoms moving in the ocean\, flagella of individual cells comprising multicellular colonies\, as well as engineered nanorobots designed to deliver drugs to tumors. In this talk we will present mathematical and computational insights into these flows at the microscale. Our modeling approaches will vary from detailed models that capture flagellar material properties and wave geometry to minimal force-dipole models that represent a flagellum by a single point. We will investigate a few intriguing systems\, including the journey of extremely long insect sperm flagella through tortuous female reproductive tracts\, and the hydrodynamic performance of shape-shifting Choanoeca flexa colonies. \nBio: Lisa Fauci received her PhD from the Courant Institute of Mathematical Sciences at New York University\, and directly after that joined the Department of Mathematics at Tulane University in New Orleans\, Louisiana\, USA. Her research focuses on biological fluid dynamics\, with an emphasis on using modeling and simulation to study the basic biophysics of organismal locomotion and reproductive mechanics. Lisa served as president of the Society for Industrial and Applied Mathematics (SIAM) in 2019-2020. She is a fellow of SIAM\, the American Mathematical Society\, the Association for Women in Mathematics\, and the American Physical Society. In 2023\, she was elected to the US National Academy of Sciences. \nHosted by: Applied Mathematics Department
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-flexible-filaments-and-swimming-cups-just-go-with-the-flow/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/03/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260225T190019Z
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009357-1773072000-1773075600@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/2/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260217T230434Z
CREATED:20260217T230434Z
LAST-MODIFIED:20260217T230434Z
UID:10009244-1773072000-1773075600@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Solution Discovery in Fluids with High Precision Using Neural Networks
DESCRIPTION:Presenter: Ching-Yao Lai\, Assistant Professor\, Stanford University \nDescription: I will discuss examples utilizing neural networks (NNs) to find solutions to partial differential equations (PDEs) that facilitate new discoveries. Despite being deemed universal function approximators\, neural networks\, in practice\, struggle to fit functions with sufficient accuracy for rigorous analysis. Here\, we developed multi-stage neural networks (Wang and Lai\, J. Comput. Phys. 2024) that can reduce the prediction error to nearly the machine precision of double-precision floating points within a finite number of iterations. We use accurate NNs to tackle the challenge of searching for singularities in fluid equations (Wang-Lai-Gómez-Serrano-Buckmaster\, Phys. Rev. Lett. 2023). Unstable singularities\, especially in dimensions greater than one\, are exceptionally elusive. With NNs we demonstrate the first discovery of smooth unstable self-similar singularities to unforced incompressible fluid equations (Wang et al.\, arXiv:2509.14185). The example illustrates how deep learning can be used to discover new and highly accurate numerical solutions to PDEs. \nBio: Ching-Yao Lai (Yao) is an Assistant Professor in the Department of Geophysics and an Affiliated Faculty of the Institute for Computational and Mathematical Engineering (ICME) at Stanford. Before joining Stanford\, she was an Assistant Professor at Princeton University. She received an undergraduate degree (2013) in Physics from National Taiwan University and a PhD (2018) in Mechanical and Aerospace Engineering from Princeton University. She completed her postdoctoral research at Columbia University where she received the Lamont Postdoctoral Fellowship. Her current research focuses on enhancing the representation of machine-learning models to tackle multiscale problems. She was the recipient of the 2023 Google Research Scholar Award\, the 2024 Sloan Research Fellowship\, and the 2025 NSF CAREER Award. \nHosted by: Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-solution-discovery-in-fluids-with-high-precision-using-neural-networks/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T080000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260225T190019Z
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009358-1773043200-1773075600@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/1/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260225T181221Z
CREATED:20260225T181221Z
LAST-MODIFIED:20260225T181221Z
UID:10009355-1772467200-1772470800@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: The Evolving Landscape of AI for Science and Engineering: Bridging Simulation\, Experiment\, and Multi-scale Dynamics
DESCRIPTION:Presenter: Aditi Krishnapriyan\, Assistant Professor\, UC Berkeley \nDescription: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk\, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This includes investigations into understanding if neural networks can autonomously discover fundamental physical relationships from data\, and demonstrating how more flexible machine learning modeling design choices enable capturing physical dynamics across multiple scales. I will also explore how generative modeling approaches rooted in statistical physics can be applied to accelerate the sampling of dynamic pathways\, and as a framework to align and bridge the gap between simulated data and experimental observations. \nBio: Aditi Krishnapriyan is an Assistant Professor at UC Berkeley where she is part of Chemical and Biomolecular Engineering\, Electrical Engineering and Computer Sciences\, and Berkeley AI Research; as well as a faculty scientist in the Applied Mathematics division at Lawrence Berkeley National Laboratory. She holds a PhD from Stanford University\, supported by the DOE Computational Science Graduate Fellowship\, was the Luis W. Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory\, and is a recipient of the Department of Energy Early Career Award and RCSA Scialog. Her research focuses on developing physics-inspired machine learning methods that bridge machine learning with physical science applications to capture phenomena across multiple length and timescales. \nHosted by: Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-the-evolving-landscape-of-ai-for-science-and-engineering-bridging-simulation-experiment-and-multi-scale-dynamics/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260202T195322Z
CREATED:20260202T195322Z
LAST-MODIFIED:20260202T195322Z
UID:10009146-1772467200-1772470800@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean
DESCRIPTION:Presenter: Francois Ribalet\, Research Associate Professor\, School of Oceanography\, University of Washington \nDescription: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow\, a custom-built automated flow cytometer deployed on over 100 research cruises\, his team has collected nearly 850 billion cell measurements across global oceans. Matrix population models applied to these data reveal how temperature affects phytoplankton division rates and biomass. The research shows that Prochlorococcus\, the ocean’s most abundant photosynthetic organism\, experiences sharp declines in growth above 28°C. Climate projections incorporating these metabolic constraints predict a 40-60% decrease in Prochlorococcus production in tropical regions by 2100\, with Synechococcus partially compensating through a 20-40% increase. These shifts between dominant phytoplankton groups will likely disrupt ocean food webs and carbon cycling\, raising questions about whether tropical ecosystems can adapt to warming oceans. \n\n\n\n\n\n\n\n\n\nBio: François Ribalet is a research associate professor at the University of Washington studying phytoplankton and their role in ocean food webs and carbon cycling. He combines field observations with statistical models to understand how environmental changes affect the growth and community dynamics of these microscopic organisms. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-decoding-phytoplankton-responses-to-a-changing-ocean/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T150000
DTEND;TZID=America/Los_Angeles:20260225T170000
DTSTAMP:20260218T010402Z
CREATED:20260211T203445Z
LAST-MODIFIED:20260218T010402Z
UID:10009206-1772031600-1772038800@live-events-ucsc.pantheonsite.io
SUMMARY:February 25\, 2026 | Works-in-Progress with Geoffrey Bowker
DESCRIPTION:Wednesday\, February 25\, 2026 \n3:00 – 5:00 PM \nHumanities 1\, Room 210 or Zoom (Registration) \nJoin SJRC scholars in Humanities 1\, room 210 or on Zoom for an open discussion of works-in-progress! This is a wonderful chance to engage with one another’s ideas\, and support our own internal work. \nAt this session\, we will hear from Geoffrey Bowker\, Emeritus Professor in Irvine and Science & Justice Advisor about works-in-progress and ongoing work on the death of infrastructure\, AI\, and underwater network cables and his collaborative comic book on Actor Network Theory. SJRC members Warren Sack and Dimitris Papadopolous will act as “warm up” discussants. \nContact Colleen Stone (colleen@ucsc.edu) or Maria Puig de la Bellacasa (puig@ucsc.edu) for the readings\, including a new comic book on the graveyard of machines! \nRegister for Zoom here. \nGeoffrey C. Bowker is Emeritus Professor at the School of Information and Computer Science\, University of California at Irvine\, where he directed a laboratory for Values in the Design of Information Systems and Technology. He was also Professor of and Senior Scholar in Cyberscholarship at the University of Pittsburgh School\, and Executive Director\, Center for Science\, Technology and Society\, Santa Clara. He was awarded the prestigious 4S Bernal Prize in 2024 for his distinguished\, career-long contributions to the field of Science and Technology Studies (STS). His book Memory Practices in the Sciences (MIT Press 2008) won the 2007 Ludwig Fleck Prize of the Society for Social Studies of Science\,  and was awarded “Best Information Science Book” by the American Society for Information Science and Technology (ASIS&T). \nCo-sponsored by earthecologies x technoscience conversations\, History of Consciousness
URL:https://live-events-ucsc.pantheonsite.io/event/february-25-2026-works-in-progress-with-geoffrey-bowker/
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260126T202042Z
CREATED:20260126T202042Z
LAST-MODIFIED:20260126T202042Z
UID:10009108-1771862400-1771866000@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Rotated Mean-Field Variational Inference and Iterative Gaussianization
DESCRIPTION:Presenter: Sifan Liu\, Assistant Professor\, Department of Statistical Science\, Duke University \nDescription:Mean-field variational inference (MFVI) approximates a target distribution with a product distribution in the standard coordinate system\, offering a scalable approach to Bayesian inference but often severely underestimating uncertainty due to neglected dependence. We show that MFVI can be greatly improved when performed along carefully chosen principal component axes rather than the standard coordinates. The principal components are obtained from a cross-covariance matrix of the target’s score function and identify orthogonal directions that capture the dominant discrepancies between the target distribution and a Gaussian reference. Performing MFVI in a rotated system defines a rotation followed by a coordinatewise transformation that moves the target closer to Gaussian. Iterating this procedure yields a sequence of transformations that progressively Gaussianize the target. The resulting algorithm provides a computationally efficient construction of normalizing flows\, requiring only MFVI sub-problems and avoiding large-scale optimization. In posterior sampling tasks\, we demonstrate that the proposed method greatly outperforms standard MFVI while achieving accuracy comparable to normalizing flows at a much lower computational cost. \nBio: Sifan Liu is an Assistant Professor in the Department of Statistical Science at Duke University. She was previously a research scientist at the Flatiron Institute and received her Ph.D. in Statistics from Stanford University. Her research interests include sampling\, generative modeling\, and selective inference. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-rotated-mean-field-variational-inference-and-iterative-gaussianization/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option-1-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260219T193254Z
CREATED:20260114T175234Z
LAST-MODIFIED:20260219T193254Z
UID:10008383-1771862400-1771866000@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Multiscale Modeling of Cellular Membranes and Oncogenic Proteins
DESCRIPTION:Presenter: Liam Stanton\, Professor\, San Jose State University \nDescription: In this talk\, I will present a multiscale model for cellular membranes\, which is trained on molecular dynamics simulations. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This new framework has enabled simulations that can access length-scales on the order of microns and time-scales on the order of seconds\, all while maintaining near fidelity to the underlying molecular interactions. Such scales are significant for accessing biological processes associated with signaling pathways within cells and experimentally relevant regimes. As applications\, we consider the cellular interactions of two membrane proteins of biological interest: G protein-coupled receptors (GPCRs) and RAS-RAF complexes\, the latter being implicated in roughly 30% of human cancers. \nBio: Dr. Stanton received his PhD in Applied Mathematics from Northwestern University in 2009. He went on to do a postdoc at Lawrence Livermore National Laboratory (LLNL)\, where he later became a staff scientist at the Center for Applied Scientific Computing. In 2018\, he joined the faculty at San Jose State University in the Department of Mathematics and Statistics\, where he is now an associate professor and a recent recipient of the Dean’s Scholar Award in Research Excellence. Dr. Stanton’s current research interests are in the multiscale modeling of non-equilibrium\, many-body systems. In particular\, he focuses on areas such as fusion energy\, biophysical systems and statistical mechanics. \nHosted by: Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-multiscale-modeling-of-cellular-membranes-and-oncogenic-proteins/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/Liam-Stanton-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T160000
DTEND;TZID=America/Los_Angeles:20260209T170000
DTSTAMP:20260114T182750Z
CREATED:20260114T182449Z
LAST-MODIFIED:20260114T182750Z
UID:10008393-1770652800-1770656400@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Data Driven Modeling for Scientific Discovery and Digital Twins
DESCRIPTION:Presenter: Dongbin Xiu\, Professor\, Ohio State University \nDescription:We present a data-driven modeling framework for scientific discovery\, termed Flow Map Learning (FML). This framework enables the construction of accurate predictive models for complex systems that are not amenable to traditional modeling approaches. By leveraging data and the expressiveness of deep neural networks (DNNs)\, FML facilitates long-term system modeling and prediction even when governing equations are unavailable. FML is particularly powerful in the context of Digital Twins\, an emerging concept in digital transformation. With sufficient offline learning\, FML enables the construction of simulation models for key quantities of interest (QoIs) in complex Digital Twins\, when direct mathematical modeling of the QoIs is infeasible. During the online execution of a Digital Twin\, the learned FML model can simulate the QoIs without reverting to the computationally intensive Digital Twin simulation model. As a result\, FML serves as an enabling methodology for real-time control and optimization for complex systems. \nBio: Dongbin Xiu received his Ph.D degree from the Division of Applied Mathematics of Brown University in 2004. He joined the Department of Mathematics of Purdue University in 2005 and moved to the University of Utah in 2013. In 2016\, He joined The Ohio State University as Professor of Mathematics and Ohio Eminent Scholar. He received NSF CAREER award in 2007 and was elected to SIAM Fellow in 2023. He is currently the Editor-in-Chief of the Journal of Computational Physics and the founding Editor-in-Chief of Journal of Machine Learning for Modeling and Computing (JMLMC). His current research focuses on developing efficient numerical methods for scientific machine learning\, data driven discovery and digital twins. \nHosted by: Daniele Venturi\, Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-data-driven-modeling-for-scientific-discovery-and-digital-twins/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/option-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260204T120000
DTEND;TZID=America/Los_Angeles:20260204T130000
DTSTAMP:20260128T170858Z
CREATED:20260128T170858Z
LAST-MODIFIED:20260128T170858Z
UID:10009124-1770206400-1770210000@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era
DESCRIPTION:Presenter: Qi Xu\, Postdoctoral Researcher\, Department of Statistics & Data Science\, Carnegie Mellon University \nDescription: Multi-modality data are increasingly common across science medicine and technology\, such as imaging\, text\, sensors\, and genomics. These modalities are often high dimensional or unstructured and naturally exhibit blockwise (nonmonotone) missingness where different samples observe different subsets of modalities. Such missingness creates a major obstacle for statistical analyses since classical methods either discard large portions of data or rely on strong modeling assumptions. Recent advances in AI make it possible to generate or predict unobserved modalities from observed ones\, opening new opportunities for data integration. In this talk\, I will focus on statistical inference for blockwise-missing multi-modality data\, while rigorously incorporating modern AI tools. Rooted in semiparametric theory\, there is a long-term open problem that theoretically optimal estimating function under non-monotone missingness is computationally intractable\, even under the missing completely at random mechanism. I introduce a tractable approximation to the optimal estimating equation through a novel Restricted ANOVA hierarchY or RAY decomposition and its almost-eigen-operator property. This leads to a new class of estimators that leverage predictive or generative AI models to borrow information across datasets while remaining unbiased and asymptotically normal. Motivated by the property of the RAY estimator\, we extend the RAY estimator to a class of unbiased\, consistent\, and computationally tractable estimators. The most efficient estimator in this class is then derived\, named as Adaptive RAY estimator\, which optimally integrating all available data and prediction from AI. Simulation studies and a single cell multi-omics application demonstrate that the proposed framework enables stable and efficient inference for complex multi modality data in the AI era. This is a joint work with Lorenzo Testa\, Jing Lei and Kathryn Roeder\, and the paper is available on arXiv: https://arxiv.org/abs/2509.24158 \nBio: Qi Xu is a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University. His research interests lie broadly in statistics and machine learning\, especially in data integration and AI for statistics\, with their applications in genomics and mobile health. He received his Ph.D. from the Department of Statistics at University of California\, Irvine\, and the Master degree from University of Illinois Urbana Champaign\, and the Bachelor degree (with honors) from Tongji University. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-statistical-inference-for-multi-modality-data-in-the-ai-era/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/Screenshot-2026-01-28-at-9.08.20-AM.png
LOCATION:https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260202T160000
DTEND;TZID=America/Los_Angeles:20260202T170000
DTSTAMP:20260128T184233Z
CREATED:20260128T184233Z
LAST-MODIFIED:20260128T184233Z
UID:10009126-1770048000-1770051600@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Are Graph Learning Methods Actually Learning?
DESCRIPTION:Presenter: Seshadhri Comandur\, Professor of Computer Science\, UCSC \nDescription: There has been a lot of literature on graph machine learning over the past few years\, and a bewildering array of new methods. This talk is based on a series of results making a provocative argument. Maybe many graph machine learning methods are not really that effective\, and the progress we are seeing is an artifact of experimental design and measurement. I will talk about some results showing that low-dimensional embeddings with dot product similarity (arguably the most common graph ML technique) cannot capture salient aspects of real-world graphs. Follow-up work demonstrates that simple benchmarks seem to outperform fancier methods\, and that there are significant shortcomings in existing accuracy measurement. \nBio: C. Seshadhri (Sesh) is a professor of Computer Science at the University of California\, Santa Cruz and an Amazon scholar. Prior to joining UCSC\, he was a researcher at Sandia National Labs\, Livermore in the Information Security Sciences department\, during 2010-2014. His primary interest is the theoretical study of algorithms\, especially those with a mix of graphs and randomization. By and large\, Sesh works at the boundary of theoretical computer science (TCS) and data mining. His work spans many areas: sublinear algorithms\, graph algorithms\, graph modeling\, scalable computation\, and data mining. In the theory world\, his work has resolved numerous open problems in monotonicity testing and graph property testing. A number of his papers in the interface of TCS and applied algorithms have received paper awards at KDD\, WWW\, ICDM\, SDM\, and WSDM. He received the 2019 SDM/IBM Early Career Award for Excellence in Data Analytics. Sesh got his Ph.D from Princeton University and spent two years as a postdoc in IBM Almaden Labs. \nHosted by: Ashesh Chattopadhyay\, Applied Mathematics Department
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-are-graph-learning-methods-actually-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/sesh.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260202T120000
DTEND;TZID=America/Los_Angeles:20260202T130000
DTSTAMP:20260128T171007Z
CREATED:20260122T191932Z
LAST-MODIFIED:20260128T171007Z
UID:10009093-1770033600-1770037200@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective
DESCRIPTION:Presenter: Jiaqi Li\, William H. Kruskal Instructor\, University of Chicago \nDescription:Modern machine learning (ML) algorithms achieve remarkable empirical success\, yet providing rigorous statistical guarantees remains a major challenge\, particularly in distributional theory and online inference methods. In this talk\, we will introduce a novel framework to provide mathematical foundations for ML by bringing powerful tools in nonlinear time series. First\, we focus on the stochastic gradient descent (SGD) with constant learning rates. By interpreting the SGD sequence as a nonlinear AR(1) process\, we can establish the geometric moment contraction (GMC) for SGD regardless of initializations. By this GMC property\, we can derive refined asymptotic theory of SGD and its averaging variant\, including general moment convergence\, quenched central limit theorems\, quenched invariance principles\, and sharp Berry- Esseen bounds. Then\, we extend this theoretical framework to SGD with dropout regularization\, a widely used but theoretically underexplored technique in deep learning. By establishing GMC under explicit learning-rate and dimensional scaling regimes\, we obtain asymptotic normality and invariance principles for dropout SGD and its averaged version. These results enable online inference\, for which we introduce a fully recursive estimator of the long-run covariance matrix appearing in the limiting distributions. The proposed online confidence intervals with asymptotically correct coverage can be generalized to many other ML algorithms. Overall\, viewing online learning algorithms as nonlinear time series provides a powerful toolkit for deriving statistical guarantees in modern ML\, with implications for high-dimensional stochastic optimization and real-time uncertainty quantification. \nBio:Jiaqi Li is a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago. She obtained her PhD in Statistics from Washington University in St. Louis in 2024. Her research focuses on developing theoretical guarantees and statistical inference methods for machine learning algorithms. She also works on time series data\, especially in the high- dimensional settings with complex temporal and cross-sectional dependency structures. She also\ncollaborates with neuroscientists on applications in fMRI and EEG data. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-mathematical-foundations-for-machine-learning-from-a-nonlinear-time-series-perspective/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option-1-1.jpg
LOCATION:https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260128T120000
DTEND;TZID=America/Los_Angeles:20260128T130000
DTSTAMP:20260128T171042Z
CREATED:20260121T235125Z
LAST-MODIFIED:20260128T171042Z
UID:10009090-1769601600-1769605200@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar:  Inferring Unobserved Trajectories from Multiple Temporal Snapshots
DESCRIPTION:Presenter: Yunyi Shen\, Ph.D. Candidate\, Department of Electrical Engineering and Computer Science\, Massachusetts Institute of Technology \n\nDescription: 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. \nBio: 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. \nHosted by: Statistics Department  \nZoom link: https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-inferring-unobserved-trajectories-from-multiple-temporal-snapshots/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option2-1.jpg
LOCATION:https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T160000
DTEND;TZID=America/Los_Angeles:20260126T170000
DTSTAMP:20260120T184604Z
CREATED:20260120T184336Z
LAST-MODIFIED:20260120T184604Z
UID:10008394-1769443200-1769446800@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option2.jpg
LOCATION: https://ucsc.zoom.us/j/97450297092?pwd=Bp4GIgR8dAuBeCd1Sz9vXo8unkYWQW.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T120000
DTEND;TZID=America/Los_Angeles:20260126T130000
DTSTAMP:20260121T182735Z
CREATED:20260121T182735Z
LAST-MODIFIED:20260121T182735Z
UID:10009084-1769428800-1769432400@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/statistics-seminar-boosting-biomedical-imaging-analysis-via-distributed-functional-regression-and-synthetic-surrogates/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/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:20260123T120000
DTEND;TZID=America/Los_Angeles:20260123T130000
DTSTAMP:20260122T174111Z
CREATED:20260120T214846Z
LAST-MODIFIED:20260122T174111Z
UID:10008680-1769169600-1769173200@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Heterogeneous Statistical Transfer Learning
DESCRIPTION:Presenter: Subhadeep Paul\, Associate Professor\, Ohio State University \nDescription: In the first part of the talk\, we consider the problem of Transfer Learning (TL) under heterogeneity from a source to a new target domain for high-dimensional regression with differing feature sets. Most homogeneous TL methods assume that target and source domains share the same feature space\, which limits their practical applicability. In applications\, the target and source features are frequently different due to the inability to measure certain variables in data-poor target environments. Conversely\, existing heterogeneous TL methods do not provide statistical error guarantees\, limiting their utility for scientific discovery.  Our method first learns a feature map between the missing and observed features\, leveraging the vast source data\, and then imputes the missing features in the target. Using the combined matched and imputed features\, we then perform a two-step transfer learning for penalized regression. We develop upper bounds on estimation and prediction errors\, assuming that the source and target parameters differ sparsely but without assuming sparsity in the target model. We obtain results for both when the feature map is linear and when it is nonparametrically specified as unknown functions.  Our results elucidate how estimation and prediction errors of HTL depend on the model’s complexity\, sample size\, the quality and differences in feature maps\, and differences in the models across domains. In the second part of the talk\, going beyond linear models\, I will discuss a transfer learning method for nonparametric regression using a random forest. The unknown source and target regression functions are assumed to differ for a small number of features. Our method obtains residuals from a source domain-trained Centered RF (CRF) in the target domain\, then fits another CRF to these residuals with feature splitting probabilities proportional to feature-residual distance covariance. We derive an upper bound on the mean square error rate of the procedure that theoretically brings out the benefits of transfer learning in random forests. Our results explain why shallower trees in the residual random forest in the target domain provide implicit regularization. \nBio:Subhadeep Paul is an Associate Professor in the Department of Statistics at The Ohio State University. He is also a faculty fellow and previously served as a co-director of the foundations of data science and AI community at the Translational Data Analytics Institute at Ohio State. He received his PhD in Statistics from the University of Illinois at Urbana-Champaign in 2017. His research focuses on statistical analysis of complex network-linked data and transfer and federated statistical learning. His research has been funded by two NSF grants from the algorithms of threat detection and mathematics of digital twins programs. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-heterogeneous-statistical-transfer-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/option-3-1.png
LOCATION:https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260122T014000
DTEND;TZID=America/Los_Angeles:20260122T014000
DTSTAMP:20260108T184752Z
CREATED:20251211T230012Z
LAST-MODIFIED:20260108T184752Z
UID:10005828-1769046000-1769046000@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series Presents: Guo Xu
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, January 22\, 2026\nTime: 1:40 – 3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Guo Xu\nTitle: Associate Professor of Economics \nAffiliation: University of California\, Berkeley  \nHost: Ajay Shenoy \n  \nSeminar title: Personnel is Policy: Delegation and Political Misalignment in the Rulemaking Process\n\nABSTRACT: We combine comprehensive data on the U.S. federal rulemaking process with individuallevel personnel and voter registration records to study the consequences of partisan misalignment between regulators and the president. We present three main results. First\, even important pieces of new regulation are frequently delegated to bureaucrats who are politically misaligned. Second\, rules that are overseen by misaligned regulators take systematically longer to complete\, are more verbose\, generate more negative feedback from the public\, and are more likely to be challenged in court. Third\, in assigning regulators to rules\, agency leaders often face a sharp tradeoff between political alignment and expertise. Agency frictions notwithstanding\, they tend to resolve this tradeoff in favor of expertise.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-guo-xu/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260120T134000
DTEND;TZID=America/Los_Angeles:20260120T150000
DTSTAMP:20260108T184635Z
CREATED:20251211T224823Z
LAST-MODIFIED:20260108T184635Z
UID:10005827-1768916400-1768921200@live-events-ucsc.pantheonsite.io
SUMMARY:Behavioral\, Econometrics and Theory Seminar Series Presents: Roberto Corrao
DESCRIPTION:Economics Behavioral\, Econometrics\, & Theory Seminar\nDate: Tuesday\, January 20\, 2026\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Roberto Corrao\nTitle:  Assistant Professor of Economics \nAffiliation:  Stanford University\nHost: Gerelt Tserenjigmid\n \nSeminar title: Contractibility Design\n \nABSTRACT: \nWe introduce a model of incentive contracting in which the principal\, in addition to\nwriting contracts\, must engage in contractibility design: creating an evidence structure\nthat allows them to prove when the agent has breached the contract. Designing an\nevidence structure entails both (i) front-end costs borne ex ante\, such as those of\ndrafting contracts\, and (ii) back-end costs borne ex post\, such as those of generating\nevidence. We find that\, under even small front-end costs\, optimal contracts are coarse\,\nspecifying finitely many contingencies out of a continuum of possibilities. In contrast\,\nunder even large back-end costs\, optimal contracts are complete. Applied to the design\nof procurement contracts\, our results rationalize: (i) the discreteness of contracts\, (ii)\nthe presence of similarly vague contracts in low-stakes and high-stakes settings\, and\n(iii) the discontinuous adjustment of contracts to changes in the economic environment.
URL:https://live-events-ucsc.pantheonsite.io/event/behavioral-econometrics-and-theory-seminar-series-presents-roberto-corrao/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260115T014000
DTEND;TZID=America/Los_Angeles:20260115T014000
DTSTAMP:20251219T220029Z
CREATED:20251211T212236Z
LAST-MODIFIED:20251219T220029Z
UID:10005825-1768441200-1768441200@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series Presents: Olivia Bordeu
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, January 15\, 2026\nTime: 1:40 – 3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Olivia Bordeu \nTitle: Assistant Professor of Economics \nAffiliation: University of California\, Berkeley  \nHost: Jeremy West \nSeminar title: Bank Branches and the Allocation of Capital across Cities\n\nABSTRACT: We study how banking market structure and branch networks shape the spatial mobility of capital. Using administrative loan-level data from Chile\, we show that bank-level deposit shocks lead receiving banks to increase lending and lower interest rates relative to other banks. Interest rate reductions are concentrated in cities where the bank has a small market share\, consistent with local market power. We develop and estimate a quantitative spatial model with multi-city banks\, oligopolistic local credit markets\, and frictions in interbank lending. These channels lead to spatial dispersion in interest rates and the marginal productivity of physical capital\, reducing GDP. Interbank frictions reduce steady-state GDP by 0.04%\, while spatial variation in loan markups reduces GDP by 0.5%. Bank mergers improve financial integration between cities but reduce competition\, generating heterogeneous welfare effects that depend on the merging banks’ geographic overlap.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-olivia-bordeu/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260113T134000
DTEND;TZID=America/Los_Angeles:20260113T150000
DTSTAMP:20251219T220255Z
CREATED:20251211T224403Z
LAST-MODIFIED:20251219T220255Z
UID:10005826-1768311600-1768316400@live-events-ucsc.pantheonsite.io
SUMMARY:Macroeconomics & International Finance Seminar Series Presents: Dean Corbae
DESCRIPTION:Macroeconomics and International Finance Seminar\nDate: Tuesday\, January 13\, 2026\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Dean Corbae\nTitle: William Sellery Trukenbrod Chair in Finance\nAffiliation: University of Wisconsin – Madison\nHost: Grace Gu Steadmon\n \nSeminar title:  A Quantitative Model of Bank Merger Dynamics\n \n\nABSTRACT: \nWe develop a simple model of the bank merger process to study the rise in bank concentration following the deregulation of bank branching in the Riegle-Neal Act of 1994. Motivated by the data where currently 10 (dominant) banks have over 55 percent of the U.S. deposit market share while the remaining over 4000 (fringe) banks cover the rest\, we apply a dominant-fringe framework with a merger stage to model the rise in concentration following the change in regulation making interstate branching possible. First\, we study the effect of the merger wave on competition\, efficiency\, and stability of the banking industry. Then we use our model to understand the interaction between regulatory and monetary policy. Specifically\, how has the bank lending channel of monetary policy been affected by rising concentration; has it amplified or dampened the effectiveness of monetary policy? How might monetary policy itself contribute to mergers and rising concentration?
URL:https://live-events-ucsc.pantheonsite.io/event/macroeconomics-international-finance-seminar-series-presents-dean-corbae/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20260112T164010Z
CREATED:20260112T164010Z
LAST-MODIFIED:20260112T164010Z
UID:10008343-1768233600-1768237200@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/am-seminar-science-in-the-age-of-foundation-models/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/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:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20251219T164251Z
CREATED:20251219T164251Z
LAST-MODIFIED:20251219T164251Z
UID:10007701-1768233600-1768237200@live-events-ucsc.pantheonsite.io
SUMMARY:Kathleen Schmidt: Sequential Experimental Design for Materials Strength Model Calibration
DESCRIPTION:Presenter: Katie Schmidt\, UQ & Optimization Group Leader\, Lawrence Livermore National Laboratory \nDescription: Due to the time and expense associated with physical experiments\, there is significant interest in optimal selection of the conditions for future experiments. Selection based on reduction in parameter uncertainty provides a natural path forward. We consider this type of optimal sequential design in the context of Bayesian calibration of materials strength models with the strength model characterizing the evolving resistance of a material to permanent strain. This problem is particularly challenging because different types of experiments and associated diagnostics are employed across strain rate regimes. For lower-strain-rate experiments\, stress-strain curves can be measured directly. For higher-strain-rate experiments\, strength must be inferred (e.g.\, from the deformation of a cylinder of material in a Taylor cylinder experiment). We employ data fusion in our sequential design methodology to incorporate these multiple experimental modalities. \nLLNL-ABS-835231 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. \nBio: Katie Schmidt is the UQ & Optimization Group Leader at Lawrence Livermore National Laboratory. She joined LLNL in 2016 after earning a PhD in Applied Mathematics from North Carolina State University. During her time at the lab\, Katie has been involved in a variety of uncertainty quantification problems related to national security as well as outreach and education through LLNL’s Data Science Institute. Her research interests include mixed-effects models\, Bayesian inference\, sequential design\, and sensitivity analysis. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/kathleen-schmidt-sequential-experimental-design-for-materials-strength-model-calibration/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/12/ph.d.-presentation-graphic-option-1-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260105T160000
DTEND;TZID=America/Los_Angeles:20260105T170000
DTSTAMP:20251218T002005Z
CREATED:20251217T182411Z
LAST-MODIFIED:20251218T002005Z
UID:10005858-1767628800-1767632400@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/am-seminar/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/12/txvu.jpg
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