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DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260325T181208Z
CREATED:20260204T222651Z
LAST-MODIFIED:20260325T181208Z
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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/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260318T171956Z
CREATED:20260318T171956Z
LAST-MODIFIED:20260318T171956Z
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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/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260408T120000
DTEND;TZID=America/Los_Angeles:20260408T130000
DTSTAMP:20260306T221053Z
CREATED:20260306T221053Z
LAST-MODIFIED:20260306T221053Z
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SUMMARY:Research Lunch & Learn: SBIR/STTR
DESCRIPTION:Join us on April 8\, 2026\, 12-1 p.m. for a session led by Benjamin Legum\, Director\, New Venture Development. Small Business Innovation Research/Small Business Technology Transfer (SBIR/STTR) grants are offered by most federal agencies to foster the translation of high-tech solutions to commercial applications. This session provides a comprehensive introduction to the federal SBIR/STTR programs that offer non-dilutive funding to help small businesses develop and commercialize high-impact technologies. Participants will learn the fundamental differences between the two programs\, including eligibility requirements\, the three-phase funding structure\, and best practices for identifying agency-specific opportunities that align with their innovative research. \nJoin: Zoom link for this session.
URL:https://events.ucsc.edu/event/research-lunch-learn-sbir-sttr/
LOCATION:https://events.ucsc.edu/event/research-lunch-learn-sbir-sttr/
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260413T160000
DTEND;TZID=America/Los_Angeles:20260413T170000
DTSTAMP:20260312T223836Z
CREATED:20260312T223749Z
LAST-MODIFIED:20260312T223836Z
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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://events.ucsc.edu/event/statistics-seminar-calibration-weighting-style-diagnostics-for-nonlinear-bayesian-hierarchical-models/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260415T120000
DTEND;TZID=America/Los_Angeles:20260415T130000
DTSTAMP:20260403T222913Z
CREATED:20260403T221951Z
LAST-MODIFIED:20260403T222913Z
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SUMMARY:The Emergence of Maritime Archaeology in the Republic of Benin: Research\, Challenges\, and Ongoing Initiatives
DESCRIPTION:Presentation Abstract:  The Republic of Benin has a rich maritime history shaped by human interactions along its coast. However\, these coastal areas remain understudied in terms of archaeological research. Over the past five years\, research has explored the potential of both land and submerged archaeological sites to understand long-term occupation and material evidence of Atlantic-era exchanges. This presentation traces the development of maritime archaeology in Benin through ongoing research. Grounded in a Maritime Cultural Landscape framework\, it combines terrestrial survey data\, underwater investigations\, oral traditions\, and historical archives to reconstruct past human interactions along the coast. \nAbout the Presenter: Affolabi Angelo Ayedoun is a PhD Student in the Department of Anthropology at UCSC. His research seeks to illuminate the precolonial history of coastal Benin by analyzing patterns of occupation and cultural interaction during the second millennium AD. It focuses on the Grand-Popo region\, an area of early settlement and a key site of initial colonial contact in present-day Benin.
URL:https://events.ucsc.edu/event/the-emergence-of-maritime-archaeology-in-the-republic-of-benin-research-challenges-and-ongoing-initiatives/
LOCATION:Social Sciences 1\, Social Sciences 1\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260415T173000
DTEND;TZID=America/Los_Angeles:20260415T203000
DTSTAMP:20260402T171331Z
CREATED:20260325T220453Z
LAST-MODIFIED:20260402T171331Z
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SUMMARY:Kraw Lecture: At the Forefront of AI: Innovation and Discovery
DESCRIPTION:Artificial intelligence is transforming how we understand and solve the world’s most complex challenges—while at the same time causing new challenges and concerns. We invite you to join us for a special UC Santa Cruz Kraw Lecture showcasing the faculty whose groundbreaking research in artificial intelligence is transforming science\, technology\, and society. From advances in autonomous systems and natural language processing to the development of sustainable and responsible AI\, this conversation will highlight the innovative work taking place across disciplines and the real-world impact it is poised to have. \nModerated by special guest Ahmad Thomas\, CEO of the Silicon Valley Leadership Group (SVLG)\, this dynamic discussion will bring together leading researchers to explore how these technologies are shaping the future—accelerating discovery\, addressing complex global challenges\, and opening new frontiers for collaboration. Gain insight into the ideas\, discoveries\, and collaborations shaping the next generation of artificial intelligence research and hear from the leaders advancing this work.\n \n\n\nIn-Person Reception: 5:30 p.m.\nLecture: 6:15 p.m.\n\nRegister Now
URL:https://events.ucsc.edu/event/kraw-lecture-at-the-forefront-of-ai-innovation-and-discovery/
LOCATION:The Quad Conference Center\, 2400 Sand Hill Rd\, Menlo Park\, CA\, 94025\, United States
CATEGORIES:Lectures & Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260416T083000
DTEND;TZID=America/Los_Angeles:20260416T170000
DTSTAMP:20260325T173958Z
CREATED:20260324T205608Z
LAST-MODIFIED:20260325T173958Z
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SUMMARY:25th MARINe Annual Meeting - Public Day
DESCRIPTION:  \nPublic Day – April 16 (All are welcome)\nTime: 8:30 AM – 5:00 PM\nLocation: Jack Baskin E2-180\, UCSC Main Campus\nRegistration: $60 (paid upon arrival; cash only) \nWe hope you can join us for the 25th MARINe Annual Meeting at UC Santa Cruz\, hosted by the Raimondi Lab and Multi-Agency Rocky Intertidal Network (MARINe). \nFrom intertidal ecology to cutting-edge coastal research (and plenty of tidepool appreciation)\, this is your chance to connect\, learn\, and explore what’s happening along our shores. \nThis day is open to practitioners\, partners\, and the broader community\, and will focus on timely topics in rocky intertidal systems. \n  \n👉 Register here (by April 1):\n MARINe Meeting 2026 PUBLIC DAY Registration Form \nAgenda:   DRAFT meeting agenda here \nKey Details\nParking: \n\nParking will be included all day\, but you must check in from 7:30-9:30am at the Core West Parking Structure. Please check in with the parking attendants on the SECOND FLOOR to receive included parking. You may park on any level of the structure.\n\nPlease note the parking attendants will have a list of registrants and we will be checking you in at the door as well.\n\n\nIf you miss this time window\, you must pay for your own parking in one of the many UCSC lots using the Parkmobile app for daily ($22) or hourly rates ($5/hr).\nFood:\nLight breakfast\, lunch (tacos)\, and snacks will be provided. Coffee\, tea\, and drinks included. Please bring your own cup if possible. \nOptional Social Hour:\n6–8 PM at Abbott Square (not included in registration) \n\nIf you have any questions\, please contact Lexi Necarsulmer (anecarsu@ucsc.edu). \nWe hope to see you there!
URL:https://events.ucsc.edu/event/25th-marine-annual-meeting-public-day/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Conference
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260331T180549Z
CREATED:20260331T180549Z
LAST-MODIFIED:20260331T180549Z
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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://events.ucsc.edu/event/am-seminar-variational-inference-and-density-estimation-with-non-negative-tensor-train/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260331T181211Z
CREATED:20260331T181211Z
LAST-MODIFIED:20260331T181211Z
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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://events.ucsc.edu/event/statistics-seminar-hierarchical-clustering-with-confidence/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260430T160000
DTEND;TZID=America/Los_Angeles:20260430T190000
DTSTAMP:20260313T232249Z
CREATED:20260313T232249Z
LAST-MODIFIED:20260313T232249Z
UID:10009418-1777564800-1777575600@events.ucsc.edu
SUMMARY:Planetary Health and Innovation Panel
DESCRIPTION:Join us for an interactive conversation featuring visionary entrepreneurs\, investors\, and ecosystem experts at the forefront of global sustainability. This panel explores the intersection of environmental stewardship and cutting-edge solutions to drive meaningful impact. Following the discussion\, please stay for a networking reception to connect with fellow attendees and industry leaders. It is a premier opportunity to exchange ideas and forge new partnerships within the planetary health space.
URL:https://events.ucsc.edu/event/planetary-health-and-innovation-panel/
LOCATION:Seymour Marine Discovery Center\, 100 McAllister Way\, Santa Cruz\, CA\, 95060
CATEGORIES:Lectures & Presentations
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