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DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260406T215333
CREATED:20260318T171956Z
LAST-MODIFIED:20260318T171956Z
UID:10011340-1775491200-1775494800@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-some-recent-results-on-transfer-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/03/ph.d.-presentation-graphic-option-1.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260406T215333
CREATED:20260204T222651Z
LAST-MODIFIED:20260325T181208Z
UID:10009162-1775491200-1775494800@events.ucsc.edu
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://events.ucsc.edu/event/am-seminar-the-thinking-eye-ai-that-sees-reads-and-reasons-in-medicine/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260406T215333
CREATED:20260325T182049Z
LAST-MODIFIED:20260325T182049Z
UID:10011767-1774886400-1774890000@events.ucsc.edu
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://events.ucsc.edu/event/am-seminar-flexible-filaments-and-swimming-cups-just-go-with-the-flow/
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260315T000000
DTEND;TZID=America/Los_Angeles:20260315T235959
DTSTAMP:20260406T215333
CREATED:20251118T004258Z
LAST-MODIFIED:20251118T004433Z
UID:10005173-1773532800-1773619199@events.ucsc.edu
SUMMARY:Summer Live in the Schedule of Classes
DESCRIPTION:The Summer Session Schedule of Classes goes live today. Explore course descriptions\, prerequisites\, and meeting times to start planning early for summer enrollment. Email summer@ucsc.edu with questions or call 831-459-5373.
URL:https://events.ucsc.edu/event/summer-live-in-the-schedule-of-classes/2026-03-15/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260406T215333
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009357-1773072000-1773075600@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/2/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260406T215333
CREATED:20260217T230434Z
LAST-MODIFIED:20260217T230434Z
UID:10009244-1773072000-1773075600@events.ucsc.edu
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://events.ucsc.edu/event/am-seminar-solution-discovery-in-fluids-with-high-precision-using-neural-networks/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260406T215333
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009358-1773043200-1773075600@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/1/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260406T215333
CREATED:20260225T181221Z
LAST-MODIFIED:20260225T181221Z
UID:10009355-1772467200-1772470800@events.ucsc.edu
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://events.ucsc.edu/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://events.ucsc.edu/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:20260406T215333
CREATED:20260202T195322Z
LAST-MODIFIED:20260202T195322Z
UID:10009146-1772467200-1772470800@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-decoding-phytoplankton-responses-to-a-changing-ocean/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260227T094500
DTEND;TZID=America/Los_Angeles:20260227T160000
DTSTAMP:20260406T215333
CREATED:20260126T234626Z
LAST-MODIFIED:20260225T003412Z
UID:10009117-1772185500-1772208000@events.ucsc.edu
SUMMARY:Semiconductor Career Summit - From Campus to Silicon Valley
DESCRIPTION:A SEMI Professional Development Seminar organized by the SEMI Silicon Valley Chapter – Connecting College Students to the Semiconductor Industry. Learn about career opportunities in high tech and acquire valuable\, practical information that will help you choose career directions and plan for your success. \nCome learn about careers in the semiconductor industry at the SEMI Professional Development Seminar hosted by UC Santa Cruz. \n\nListen to professionals in the industry talk about their roles and find out how to prepare for jobs in the Semiconductors Industry.\nDiscover semiconductor job opportunities you didn’t know existed (internship and entry-level) and how you can prepare for them through job searches\, interviews\, resumes\, and more.\nMeet with professionals and executives during our speed mentoring\, mock interview\, and networking sessions.\n\nAll majors are welcome! Students with a background in Engineering\, Computer Science\, Chemistry\, Physics\, Math\, Data Science\, and Business are strongly encouraged to attend. \n\nEnjoy free food\, free swag\, and giveaways.\nStudents can come and go.\n\nEVENT is FREE but registration is required. Register by Feb 20th to secure a lunch.  \nEvent is organized by SEMI in collaboration with Career Success\, Baskin Engineering and the Innovation & Business Engagement Hub. \nYou Belong Here: The programs and services described here are open to all\, consistent with state and federal law\, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service\, faculty program\, or community event—is designed to be accessible\, inclusive\, and respectful of all identities. \nTo learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications. \nQuestions? Send to csuccess@ucsc.edu or visit Career Success at Hahn 125 East Entrance\nNeed accessibility support? Let us know at slugtalent@ucsc.edu at least two weeks prior to the event date.
URL:https://events.ucsc.edu/event/semiconductor-career-summit-from-campus-to-silicon-valley/
LOCATION:Stevenson Event Center\, Stevenson Service Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Exhibits,Lectures & Presentations,Meetings & Conferences,Training
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260406T215333
CREATED:20260126T202042Z
LAST-MODIFIED:20260126T202042Z
UID:10009108-1771862400-1771866000@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-rotated-mean-field-variational-inference-and-iterative-gaussianization/
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260406T215333
CREATED:20260114T175234Z
LAST-MODIFIED:20260219T193254Z
UID:10008383-1771862400-1771866000@events.ucsc.edu
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://events.ucsc.edu/event/am-seminar-multiscale-modeling-of-cellular-membranes-and-oncogenic-proteins/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260406T215333
CREATED:20260114T182449Z
LAST-MODIFIED:20260114T182750Z
UID:10008393-1770652800-1770656400@events.ucsc.edu
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://events.ucsc.edu/event/am-seminar-data-driven-modeling-for-scientific-discovery-and-digital-twins/
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260204T120000
DTEND;TZID=America/Los_Angeles:20260204T130000
DTSTAMP:20260406T215333
CREATED:20260128T170858Z
LAST-MODIFIED:20260128T170858Z
UID:10009124-1770206400-1770210000@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-statistical-inference-for-multi-modality-data-in-the-ai-era/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/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:20260406T215333
CREATED:20260128T184233Z
LAST-MODIFIED:20260128T184233Z
UID:10009126-1770048000-1770051600@events.ucsc.edu
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://events.ucsc.edu/event/am-seminar-are-graph-learning-methods-actually-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/01/sesh.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260202T120000
DTEND;TZID=America/Los_Angeles:20260202T130000
DTSTAMP:20260406T215333
CREATED:20260122T191932Z
LAST-MODIFIED:20260128T171007Z
UID:10009093-1770033600-1770037200@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-mathematical-foundations-for-machine-learning-from-a-nonlinear-time-series-perspective/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260406T215333
CREATED:20260121T235125Z
LAST-MODIFIED:20260128T171042Z
UID:10009090-1769601600-1769605200@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-inferring-unobserved-trajectories-from-multiple-temporal-snapshots/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260406T215333
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T120000
DTEND;TZID=America/Los_Angeles:20260126T130000
DTSTAMP:20260406T215333
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:20260123T120000
DTEND;TZID=America/Los_Angeles:20260123T130000
DTSTAMP:20260406T215333
CREATED:20260120T214846Z
LAST-MODIFIED:20260122T174111Z
UID:10008680-1769169600-1769173200@events.ucsc.edu
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://events.ucsc.edu/event/statistics-seminar-heterogeneous-statistical-transfer-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/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:20260112T170000
DTEND;TZID=America/Los_Angeles:20260112T183000
DTSTAMP:20260406T215333
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20260406T215333
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:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20260406T215333
CREATED:20251219T164251Z
LAST-MODIFIED:20251219T164251Z
UID:10007701-1768233600-1768237200@events.ucsc.edu
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://events.ucsc.edu/event/kathleen-schmidt-sequential-experimental-design-for-materials-strength-model-calibration/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260406T215333
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:20251208T093000
DTEND;TZID=America/Los_Angeles:20251208T104500
DTSTAMP:20260406T215333
CREATED:20251117T202808Z
LAST-MODIFIED:20251119T192125Z
UID:10005162-1765186200-1765190700@events.ucsc.edu
SUMMARY:CSE Colloquium: Making Systems Secure with Information Flow
DESCRIPTION:Presenter: Andrew Myers\, Cornell University\n\nAbstract:\nModern civilization depends on complex\, interconnected software systems that must safeguard trustworthy or private data. We have ever-growing mountains of code yet lack principled ways to build large systems that are secure. What is missing is a way to securely build these systems compositionally: module by module and layer by layer. Information flow control\, enforced throughout software and hardware\, offers a plausible way to achieve compositional security\, and is increasingly being used by industry. I describe how my research group has incorporated information-flow security into various languages and systems: hardware architectures resilient to timing and speculation attacks\, smart contracts\, and automatically synthesized cryptographic and distributed protocols. Information flow is inherently compositional and makes possible strong\, provable security guarantees that can be connected to cryptographic security definitions. Importantly\, it also guides developers during the design process\, exposing security-critical decisions up front. \nBio:\nAndrew Myers is the Class of 1912 Professor of Engineering in the Department of Computer Science at Cornell University. He received his Ph.D. in Electrical Engineering and Computer Science from MIT\, advised by Barbara Liskov. His research interests include programming languages\, computer security\, and distributed and persistent programming systems. His work on computer security has focused on practical\, sound\, expressive languages and systems for enforcing information security. Myers is an ACM Fellow and has authored several award-winning papers. He currently serves as the chair of the ACM SIGPLAN Executive Committee. \nHosted By: Professor Mohsen Lesani \nLocation: Engineering 2\, E2-180 \nZoom: https://ucsc.zoom.us/j/97682837116?pwd=WZBzhJY4p7rTZshqglmOs6xBtBasbE.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-making-systems-secure-with-information-flow/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20251203T110000
DTEND;TZID=America/Los_Angeles:20251203T123000
DTSTAMP:20260406T215333
CREATED:20251103T224713Z
LAST-MODIFIED:20251119T191907Z
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SUMMARY:When Less is More: Applications of Type-Based Underapproximate Reasoning
DESCRIPTION:Presenter: Suresh Jagganathan\, Purdue University\n\n\nAbstract:\nUnlike program verifiers\, symbolic execution and property-based testing tools underapproximate program behavior: they aim to report only real bugs (no false positives)\, at the cost of potentially missing some (false negatives). Recent work has sought to place such tools on a more formal footing\, primarily through the development of incorrectness logics that capture a program’s ‘must’ rather than ‘may’ behavior. This talk explores how to transplant these ideas of underapproximation into an expressive refinement type system. Our development enables us to:\n\n(a) Typecheck the completeness of property-based testing (PBT) generators\, ensuring that a well-typed generator produces all values (i.e.\, fully covers) its output type;\n\n(b) Synthesize effectful generators by extending the type system to model underapproximations of sequences of effects rather than just values; and\n\n(c) Guide symbolic execution in effectful functional programs\, prioritizing execution paths capable of falsifying data structure safety properties.\n\nOur results demonstrate that viewing types through the lens of underapproximation offers a principled foundation for designing\, implementing\, and reasoning about program analyzers and test generators\, significantly improving their reliability and practical utility in the process.\n\n\nBio:\nSuresh Jagannathan is the Samuel D. Conte Professor of Computer Science at Purdue University. His interests span functional programming\, program verification\, distributed and concurrent systems\, and trustworthy machine learning. In recent years\, he has spent time as an Amazon Scholar\, a program manager at the Information Innovoation Office (I2O) at DARPA\, and a visiting researcher at the University of Cambridge. He serves an Associate Editor of ACM TOPLAS\, and has served as both General and PC Chair of POPL (ACM Symposium on Programming Languages).\n\n\nHosted by: Professor Mohsen Lesani\n\n\nLocation: E2-180\n*Refreshments such as coffee and pastries will be provided\n\n\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/when-less-is-more-applications-of-type-based-underapproximate-reasoning/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251121T140000
DTEND;TZID=America/Los_Angeles:20251121T160000
DTSTAMP:20260406T215333
CREATED:20251021T182427Z
LAST-MODIFIED:20251022T181942Z
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SUMMARY:Torres\, S. (ECE) - An Integrated Platform for Real-time Monitoring and Support of 3D Tissue Growth
DESCRIPTION:Organoids are three-dimensional tissue cultures that model real organs and serve as valuable tools for studying development\, disease\, and treatment response. Traditional methods\, which rely on manual handling and incubators\, limit consistency and real-time monitoring. To address these issues\, we developed a modular microfluidic platform that integrates automated feeding\, live fluorescence imaging\, and environmental control without the need for a standard incubator. The core of the system is a vertically oriented PDMS-glass chip that enables precise media delivery and continuous imaging of small 3D structures such as organoids. Using fluorescent dyes to mimic molecules\, such as nutrients or drugs\, we tracked their movement through tissue in real time without invasive sensors. This setup maintains metabolic stability and provides detailed insight into molecular transport\, which improves applications in disease modeling\, drug testing\, and personalized medicine. \n  \nEvent Host- Sebastián Torres\, Ph.D. Candidate\, Electrical & Computer Engineering  \nAdvisor: Mircea Teodorescu \n  \nZoom- https://ucsc.zoom.us/j/2333595627?pwd=aWtwL3V2QnFTMkNDSWowZnRNS0xSQT09 \nPasscode- 579836
URL:https://events.ucsc.edu/event/torres-s-ece-an-integrated-platform-for-real-time-monitoring-and-support-of-3d-tissue-growth/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251119T110000
DTEND;TZID=America/Los_Angeles:20251119T121500
DTSTAMP:20260406T215333
CREATED:20251105T220936Z
LAST-MODIFIED:20251118T181912Z
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SUMMARY:CSE Colloquium - Flux: Refinement Types for Verified Rust Systems
DESCRIPTION:Presenter: Ranjit Jhala\, UCSD\n\nAbstract: Rust has risen as a language of choice for new systems code — from OS kernels to hypervisors\, firmware and run-times — as it is memory safe and provides the sort of abstractions needed for efficient low-level systems implementation. We present Flux\, a refinement type checker for Rust that shows how logical refinements can work in tandem with Rust’s ownership mechanisms to yield ergonomic type-based verification of low-level systems code. We then present a case study showing how Flux was used to formally verify process isolation in Tock: a microcontroller OS used in security-critical systems like the Google Security Chip (GSC) and Microsoft’s Pluton security processor. Our verification effort unearthed multiple subtle bugs that broke isolation\, allowing malicious applications to compromise the OS to potentially steal sensitive data or brick or take control of the OS. We describe how Flux helped design and implement a new granular process abstraction that is both simpler\, more efficient\, and yields formally verified security guarantees.\n\nBio:\nRanjit Jhala is a professor of Computer Science and Engineering at the University of California\, San Diego. He works on algorithms and tools that help engineer reliable computer systems. His work draws from and contributes to the areas of Model Checking\, Program Analysis\, and Automated Deduction\, and Type Systems. He helped create several influential and award winning systems including the BLAST software model checker and Liquid Types\, received ACM SIGPLAN’s Robin Milner Young Researcher Award\, and is a Fellow of the ACM.\n\nHosted by: Professor Mohsen Lesani\n\nLocation: Engineering 2\, Room E2-180\n*Refreshments such as coffee and pastries will be provided.\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1
URL:https://events.ucsc.edu/event/cse-colloquium-flux-refinement-types-for-verified-rust-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T121500
DTSTAMP:20260406T215333
CREATED:20251106T173342Z
LAST-MODIFIED:20251106T185851Z
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SUMMARY:CSE Colloquium - Neurosymbolic AI: from research to industry
DESCRIPTION:Presenter: Luis Lamb\, Catholic Institute of Technology\n\nAbstract:\nNeurosymbolic AI brings together the statistical nature of machine learning with the formal reasoning capabilities of symbolic AI. It seeks to offer a balanced approach to contemporary AI technologies\, by combining the ability to learn from data\, with the capacity to reason upon knowledge acquired from an environment. The main criticism of neural machine learning lies in its lack of explainability and semantics\, which are key requirements in safety-critical applications\, yet inherent strengths of logic-based methods. Recently\, several corporations have publicly announced products and technologies grounded in neurosymbolic AI methodologies. This talk provided a concise review of the foundations\, frameworks and tools underlying neurosymbolic AI\, along with illustrative applications. It concludes by highlighting current trends and research directions in the field.\n\nBio:\nLuis Lamb is Professor of Computer Science and Vice President of Research at the Catholic Institute of Technology. His research interests include: Artificial Intelligence\, Neurosymbolic AI\, Innovation Strategies\, and Applied Logics. Lamb has co-authored two research monographs\, including Neural-Symbolic Cognitive Reasoning\, with d’Avila Garcez and Gabbay (Springer 2009). He organized two Dagstuhl Seminars on Neursymbolic AI\, published widely in AI\, and has worked in the area for over 20 years.  Lamb also has extensive experience leading research planning\, strategy\, and university wide research & infrastructure grant applications\, and strategic academic-industry partnerships. He has been a Professor in Brazil and has experience in industry as a former Senior Manager of AI and Machine Learning at Boeing. He holds a PhD in Computer Science from Imperial College London and an MBA from MIT.\n\nHosted by: Professor Mohsen Lesani\n\nLocation: Engineering 2\, E2-180\n\n*Refreshments such as coffee and pastries will be provided.\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-neurosymbolic-ai-from-research-to-industry/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251105T110000
DTEND;TZID=America/Los_Angeles:20251105T121500
DTSTAMP:20260406T215333
CREATED:20251015T215159Z
LAST-MODIFIED:20251022T182643Z
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SUMMARY:CSE Colloquium: Mitigating Data Scarcity via Simulation by Roozbeh Mottaghi
DESCRIPTION:Presenter: Roozbeh Mottaghi\, University of Washington \nAbstract: Data has revolutionized progress across AI fields like natural language processing and computer vision. Yet\, in robotics\, data collection remains a significant challenge: robots must interact with complex\, dynamic environments\, making the process slow\, costly\, and difficult to scale. In this talk\, I will discuss how simulation is transforming the landscape of robotics research by addressing these data bottlenecks. I will introduce Habitat 3.0\, a 3D simulator designed for training and evaluating robotic agents in dynamic environments that include human interactions. Focusing on collaborative human-robot tasks\, I will present PARTNR\, a simulation benchmark designed to rigorously evaluate planning and reasoning in interactive settings. I will share key insights from this benchmark\, revealing both the impressive capabilities of current LLMs and the significant challenges they encounter when faced with the complexities of real-world environments. \nBio: Roozbeh Mottaghi is a Senior Research Scientist Manager at FAIR and an Affiliate Associate Professor in Paul G. Allen School of Computer Science and Engineering at the University of Washington. Prior to joining FAIR\, he was the Research Manager of the Perceptual Reasoning and Interaction Research (PRIOR) group at the Allen Institute for AI (AI2). He obtained his PhD in Computer Science in 2013 from the University of California\, Los Angeles. After PhD\, he joined the Computer Science Department at Stanford University as a post-doctoral researcher. His research mainly focuses on embodied AI\, reasoning via perception\, and learning via interaction\, and his work on large-scale Embodied AI received the Outstanding Paper Award at NeurIPS 2022. \n\n\n\n\n\n\nFaculty Host: Professor Mohsen Lesani \n\nLocation: Engineering 2\, E2-180\n\n*Refreshments such as coffee and pastries will be provided.\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-mitigating-data-scarcity-via-simulation-by-roozbeh-mottaghi/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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