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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://live-events-ucsc.pantheonsite.io/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://live-events-ucsc.pantheonsite.io/event/statistics-seminar-some-recent-results-on-transfer-learning/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260408T132500
DTEND;TZID=America/Los_Angeles:20260408T143000
DTSTAMP:20260424T192953Z
CREATED:20260424T192953Z
LAST-MODIFIED:20260424T192953Z
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SUMMARY:Rethinking Stemflow: Distribution\, Infiltration\, and Isotopic Insights in Forests
DESCRIPTION:Juan Andres Pinos from the University of Nevada\, Las Vegas\nIn Person Location: ISB 221 \nZoom Link \nStemflow represents a spatially concentrated and chemically enriched flux of intercepted precipitation that delivers water and solutes to near‐stem soils. Conventional approaches neglect potential heterogeneity in stemflow redistribution aboveground and its subsurface infiltration pathways\, introducing bias into forest water balance modeling. Furthermore\, stemflow is almost always overlooked in isotope-based hydrological models\, and consequently\, its isotopic signature remains poorly characterized. Accounting for the stemflow isotopic signal may reduce uncertainty estimates of near-stem water sources and flow pathways. To address these gaps\, we combined field experimentation with stable water isotope tracing to investigate (i) the circumferential distribution of stemflow along tree trunks\, (ii) its preferential infiltration pathways\, and (iii) isotopic modification during canopy interception and redistribution. Our results demonstrate that stemflow is non-uniformly distributed around the trunk\, with patterns influenced by tree lean and canopy structure. Upon reaching the soil\, stemflow preferentially infiltrates along coarse roots and macropores\, extending both vertically and laterally within the soil matrix. Additionally\, stemflow exhibits isotopic enrichment relative to open rainfall\, reflecting canopy retention and evaporative fractionation during interception. These findings challenge assumptions of uniform stemflow distribution\, refine mechanistic models of stemflow infiltration\, and highlight isotopic modification. Together\, they advance understanding of forest–water interactions and their implications for landscape hydrology.
URL:https://live-events-ucsc.pantheonsite.io/event/rethinking-stemflow-distribution-infiltration-and-isotopic-insights-in-forests/
LOCATION:CA
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260410T132000
DTEND;TZID=America/Los_Angeles:20260410T142500
DTSTAMP:20260410T190015Z
CREATED:20260407T233816Z
LAST-MODIFIED:20260410T190015Z
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SUMMARY:BME 80G Seminar: Sara Ackerman - Doing Ethics From The Inside: Collaboration\, Critique\, and Contradiction in Team Science
DESCRIPTION:Presenter: Sara Ackerman\, Medical Anthropologist and Professor of Social and Behavioral Sciences\, University of California\, San Francisco \nDescription: Team science has been widely promoted as a collaborative\, cross-disciplinary approach to addressing key scientific questions\, yet power differences and epistemic hierarchies persist. This talk explores "embedded ethics"—a model in which social scientists and ethicists work directly with scientific research teams. Drawing on findings from an empirical ethics project embedded in a multi-year clinical genomics study\, I demonstrate how qualitative methods and participatory design can shift the researcher-participant dynamic toward greater reciprocity and attention to enrolled families’ experiences. At the same time\, ethicists and social scientists can find themselves in an uncomfortable and even paradoxical position\, expected to facilitate project goals—such as recruitment of historically underrepresented groups—while simultaneously critically assessing the very categories of difference and measures being used. In the future\, team science collaborations can result in more just and broadly beneficial science if social science\, humanities and community partners are able to meaningfully contribute to the research agenda itself. \nBio: Sara Ackerman\, PhD\, MPH\, is a medical anthropologist and Professor of Social and Behavioral Sciences at the University of California\, San Francisco. Her research draws on ethnographic methods and public engagement to examine how genomics\, artificial intelligence and other emerging medical technologies affect the lives of patients and caregivers and shape conceptions of health\, illness and the public good. Sara teaches courses on community-engaged research\, qualitative methods\, and research ethics at UCSF. As Director of the Bioethics and Regulatory Support Program for UCSF’s Clinical and Translational Science Institute\, she is working to increase patient and public participation in decisions about the use of AI in clinical care and the sharing of patients’ clinical data for research. \nHosted by: Professor Karen Miga\, BME Department
URL:https://live-events-ucsc.pantheonsite.io/event/bme-80g-seminar-sara-ackerman-doing-ethics-from-the-inside-collaboration-critique-and-contradiction-in-team-science/
LOCATION:Jack Baskin Auditorium\, 191 Baskin Cir\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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