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DTSTART;TZID=America/Los_Angeles:20260403T132000
DTEND;TZID=America/Los_Angeles:20260403T142500
DTSTAMP:20260404T101639
CREATED:20260401T005024Z
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SUMMARY:BME 80G Seminar: To Infinity and Beyond? Ethical\, legal\, and social issues of human research in space”
DESCRIPTION:Presenter: Vaso Rahimzadeh\, Assistant Professor\, Baylor College of Medicine \nDescription: As humans venture farther into outer space\, new scientific discovery awaits including in genomics; but so do new ethical dilemmas.  Who bears the risks (and rewards) of space exploration and how should humanity ethically expand beyond our planet? This session will have students think critically about the ethical\, legal\, and social issues of human genomic research in space and offer frameworks for analyzing them. Students will learn about the contemporary challenges and opportunities of genomic research for the upcoming lunar missions\, and in anticipation of future Mars exploration. \nBio: I am Assistant Professor at the Center for Medical Ethics and Health Policy at Baylor College of Medicine. In my National Institutes of Health-funded research\, I investigate the ethical\, legal\, and social issues of health data sharing on earth and in space. I aim to inform policy and practice in ways that maximize the scientific value of data while respecting the rights and interests of individuals and communities. I director the METEORS program (Mission to Enhance eThics Education\, Outreach\, and Research in Space) and serve on the Bioethics Advisory Panel for the National Aeronautics and Space Administration (NASA). I am a proud UC alum\, earning my BS in Microbial Biology from UC Berkeley in 2012\, and hold a PhD from McGill University with a specialization in biomedical ethics. You can read more about my background and read my work here. \nHosted by: Professor Karen Miga\, BME Department
URL:https://events.ucsc.edu/event/bme-80g-seminar-to-infinity-and-beyond-ethical-legal-and-social-issues-of-human-research-in-space/
LOCATION:Jack Baskin Auditorium\, 191 Baskin Cir\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260404T101639
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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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260404T101639
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:20260413T080000
DTEND;TZID=America/Los_Angeles:20260508T170000
DTSTAMP:20260404T101639
CREATED:20260214T011406Z
LAST-MODIFIED:20260319T220918Z
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SUMMARY:2026 Right Livelihood International Conference
DESCRIPTION:The Right Livelihood International Conference is a four-week global conference exploring how education can strengthen democracy\, collective intelligence\, and just futures. Bringing together Right Livelihood Laureates\, students\, faculty\, and community partners across continents\, the conference combines asynchronous learning with participatory dialogue and collaborative action. Rather than advocating specific outcomes\, the conference positions education as a democratic practice and the Right Livelihood College as a steward of dialogue\, student voice\, and long-term institutional learning. \nRegistration is free and open to the public. Sign up to receive conference updates\, session links\, and participation opportunities.
URL:https://events.ucsc.edu/event/2026-right-livelihood-international-conference/
LOCATION:
CATEGORIES:Film Screening,Lectures & Presentations,Meetings & Conferences,Ph.D. Presentations,Seminars,Social Gathering,Training,Undergraduate,Workshop
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260413T160000
DTEND;TZID=America/Los_Angeles:20260413T170000
DTSTAMP:20260404T101639
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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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260415T103000
DTEND;TZID=America/Los_Angeles:20260415T120000
DTSTAMP:20260404T101639
CREATED:20260331T011648Z
LAST-MODIFIED:20260331T012828Z
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SUMMARY:FINS: Fisheries Insights Narratives and Stories seminar series featuring Dr. George Leonard
DESCRIPTION:Please join us for the second talk in the FINS: Fisheries Insights Narratives and Stories seminar series featuring Adjunct Professor Dr. George Leonard. His talk\, “Lessons learned from my time at the science-policy interface” will discuss his 35 years of experience researching\, communicating\, and advocating for a vibrant and healthy ocean upon which all of us depend. He has been at the forefront of ocean science-policy interface at major nonprofits (Monterey Bay Aquarium and Ocean Conservancy)\, working on practical ocean solutions to some of the ocean’s greatest environmental challenges. He initiated\, developed\, and led a host of conservation programs during his time at Ocean Conservancy including offshore aquaculture\, plastics pollution\, ocean acidification\, climate change\, mesopelagic fisheries\, and deep-sea mining. During his early career at Monterey Bay Aquarium\, he developed the scientific foundation for the nascent sustainable seafood movement \nFINS: Fisheries Insights Narratives and Stories Seminar Series \nDr. George Leonard\, Adjunct Professor\, Coastal Science and Policy UCSC \nTitle: Lessons learned from my time at the science-policy interface \nWhen: Wednesday\, April 15th from 10:30am-12pm \nWhere: Ocean Health Building Rm 118\, 115 McAllister Way\, Santa Cruz\, CA 95060 and on Zoom \nAgenda: \n\n10:30 am – 11:00 am – Professional Networking Session (in person only – light snacks and refreshments provided)\n11 am to 12 pm – presentation followed by Q & A\n12 pm – 1pm – student lunch with the speaker in OHB courtyard → sign up here\n\nZoom Meeting Registration: https://ucsc.zoom.us/meeting/register/NwH0_qUbSeuIm3A76DY-Dg \n 
URL:https://events.ucsc.edu/event/fins-fisheries-insights-narratives-and-stories-seminar-series-featuring-dr-george-leonard/
LOCATION:Ocean Health Building\, McAllister Way\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars,Social Gathering
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260404T101639
CREATED: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:Jack Baskin Engineering Building\, 372
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260404T101639
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:Jack Baskin Engineering Building\, 156
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260422T110000
DTEND;TZID=America/Los_Angeles:20260422T121500
DTSTAMP:20260404T101639
CREATED:20260331T171056Z
LAST-MODIFIED:20260401T165930Z
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SUMMARY:CSE Colloquium - Robust Machine Learning for Biomedical Data: Efficiency\, Reliability\, and Generalizability
DESCRIPTION:Presenter\nChenyu You\, Stony Brook University \nAbstract\nIn the rapidly growing area of machine learning\, there is profound promise in crafting intelligent\, data-driven methods for diverse real-world applications. Yet\, in safety-critical domains like healthcare\, some fundamental challenges remain: (1) The insufficiency of raw biomedical data emphasizes the need for data-efficient and robust learning approaches. (2) The imperative of safety and stability necessitates a cohesive framework that unifies learning with theoretical guarantees. (3) The inherent heterogeneity and distribution shifts in real-world clinical data call for robust and generalizable learning methods. To address these challenges\, there are several major directions I have explored: (i) (Robust) Machine Learning for Imperfect Medical Data: The development of machine learning models\, particularly in the context of label scarcity\, increasingly necessitates the collection of substantial annotated medical data. Moreover\, medical data often display a long-tailed class distribution\, which consequently results in notable imbalance issues. To this end\, there are several growing interests in training machine learning models jointly across imbalanced class distributions and limited annotations. I have developed novel\, efficient\, statistically consistent algorithms to improve empirical performance for biomedical image analysis. (ii) Learning with Theoretical Guarantees: As machine learning methods have become ubiquitous in clinical decision-making\, their reliability and interpretability have become important. This is particularly crucial in the field of biomedical image analysis\, where decision outcomes can have profound implications. I have developed novel machine learning algorithms that enable provably accurate anatomical modeling with theoretical guarantees. (iii) Generalize across Diverse Biomedical Data: The development of medical foundation models often requires massive and diverse biomedical data. To this end\, I have developed various foundation models for biomedical imaging data and explored novel applications of these models. I have also developed novel medical AI Agents that lead to the scalable and accurate predictive modeling\, particularly for distribution shift problems. \nSpeaker Bio\nChenyu You is an Assistant Professor in the Department of Applied Mathematics & Statistics and Department of Computer Science at Stony Brook University. He is also the core faculty member of the CVLab\, AI institute\, and affiliated with the Institute for Advanced Computational Science. His research focuses on both fundamental and applied problems in computer vision and machine learning\, often with a focus on generalization\, and making machine learning more reliable. Our applied research includes applications to healthcare\, biomedical imaging\, and cognitive neuroscience. He received his Ph.D. in 2024 from Yale University under the advisement of James S. Duncan\, his M.S. in 2019 from Stanford University under the advisement of Daniel Rubin\, and his B.S. in 2017 from Rensselaer Polytechnic Institute under the advisement of Ge Wang\, all in electrical engineering. He has also spent wonderful time at Facebook AI Research (FAIR)\, as well as Google Research. He serves on the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI)\, and the SUNY AI Symposium Planning Committee\, and as associate editors for IEEE Transactions on Medical Imaging\, Medical Image Analysis\, IEEE Transactions on Neural Networks and Learning Systems\, Pattern Recognition\, and Transactions on Machine Learning Research. He has received AAAI’26 New Faculty Highlights\, CPAL’26 Rising Stars Award\, Tinker Research Grant Award\, Lambda Research Grant Award\, ICML’25 Oral Presentation Award\, EMBC’25 Top Paper Award\, MICCAI’25 NIH Registration Grant Award\, IEEE TMI’25 Distinguished Associate Editor Certificate of Excellence Award\, and Yale George P. O’Leary Graduate Fellowship\, and has been ranked as the World’s Top 2% most-cited scientists by Stanford University since 2024\, is a member of the Sigma Xi scientific research society\, and received the Excellence in Teaching Award for Spring and Fall 2025. For more information\, please check his website: https://chenyuyou.me/. \nHosted by: Professor Yuyin Zhou \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-robust-machine-learning-for-biomedical-data-efficiency-reliability-and-generalizability/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260429T110000
DTEND;TZID=America/Los_Angeles:20260429T121500
DTSTAMP:20260404T101639
CREATED:20260402T185047Z
LAST-MODIFIED:20260402T185047Z
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SUMMARY:CSE Colloquium - Towards Safe and Resilient Large-scale Distributed Programming
DESCRIPTION:Presenter: Philipp Haller\, KTH Royal Institute of Technology \nAbstract: \nDistributed programming is notoriously difficult. Not only are distributed systems concurrent\, they pose additional challenges including data consistency and fault tolerance. At the same time\, the share of software systems that are necessarily distributed systems is growing rapidly. As a result\, too many software developers are asked to become distributed systems experts. Thus\, tools and techniques for ensuring the correctness of distributed systems are urgently needed in order to leave this unsustainable trajectory. This talk presents research results towards the design and implementation of programming systems that support emerging applications and workloads; provide reliability and trust; and embrace simplicity and accessibility. Concretely\, the presented work focuses on two directions. \nThe first direction explores a distributed programming model that provides consistency while enabling high availability for workloads operating on join-semilattices without sacrificing partition tolerance. We propose a new consistency protocol\, called observable atomic consistency protocol (OACP)\, which leverages on-demand coordination to support both coordination-free operations as well as totally-ordered operations on replicated data types. We present a formal\, mechanized model of OACP in rewriting logic and verify key correctness properties using the model checking tool Maude. Furthermore\, we present the evaluation of a prototype implementation of OACP based on Akka\, a widely-used actor-based middleware. The second direction explores a programming system that aims to reconcile the scalability and fault tolerance of stream processing systems with the flexibility of the actor concurrency model. The programming system ensures a failure-transparency property\, effectively masking failures through transparent recovery. Our work is the first to formalize failure transparency using a small-step operational semantics\, and to provide proofs of failure transparency for stateful dataflow streaming and a fault-tolerant actor-based programming model. \nBio: \nPhilipp Haller is an Associate Professor in the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology in Stockholm\, Sweden. His main research interests are in the design and implementation of programming languages\, type systems\, concurrency\, and distributed programming. He was part of the team that received the 2019 ACM SIGPLAN Programming Languages Software Award for the development of the Scala programming language. Prior to KTH\, he was an early employee at Akka (previously Lightbend\, Inc.)\, a start-up company developing and supporting Scala as well as frameworks for large-scale distributed programming. Prior to Akka\, he was a post-doctoral fellow at Stanford University\, USA\, and at EPFL\, Switzerland. In 2010 he received his PhD in computer science from EPFL\, including a nomination for the 2010 EPFL Doctorate Award. In 2006 he received his Dipl.-Inform. degree from Karlsruhe Institute of Technology (previously University of Karlsruhe)\, Germany. \nHosted by: Professor Mohsen Lesani \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-towards-safe-and-resilient-large-scale-distributed-programming/
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:20260504T160000
DTEND;TZID=America/Los_Angeles:20260504T170000
DTSTAMP:20260404T101639
CREATED:20260312T222740Z
LAST-MODIFIED:20260312T222740Z
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SUMMARY:Statistics Seminar: Advancing Statistical Rigor in Single-Cell and Spatial Omics Using In Silico Control Data
DESCRIPTION:Presenter: Guan’ao Yan\, Assistant Professor\, Michigan State University \nDescription: Single-cell and spatial transcriptomics technologies now let us map cellular diversity and tissue organization at high resolution\, but the computational methods built to analyze these data are difficult to evaluate in a rigorous\, reproducible way. Two key barriers are the lack of realistic synthetic data with known ground truth and the ambiguity in how we define biologically meaningful spatial patterns. This talk will introduce two simulation frameworks—scReadSim for single-cell RNA-seq and ATAC-seq data\, and scIsoSim for isoform-level expression and splicing—that generate realistic sequencing reads while preserving user-specified truth. These tools enable fair\, controlled benchmarking of quantification and splicing methods across experimental protocols. The talk will also present a systematic review of 34 methods for detecting spatially variable genes (SVGs) in spatial transcriptomics data\, proposing a new categorization of SVGs and outlining how future benchmarks should be designed. Overall\, the goal is to improve statistical rigor\, interpretability\, and comparability in single-cell and spatial omics analysis. \nBio: Guan’ao Yan is an Assistant Professor of Computational Mathematics\, Science & Engineering at Michigan State University. He received his Ph.D. in Statistics from UCLA. His research focuses on statistical and computational methods for modern statistical genomics\, particularly single-cell and spatial omics\, with an emphasis on rigorous benchmarking\, interpretability\, and biomedical discovery. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-advancing-statistical-rigor-in-single-cell-and-spatial-omics-using-in-silico-control-data/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260513T110000
DTEND;TZID=America/Los_Angeles:20260513T121500
DTSTAMP:20260404T101639
CREATED:20260330T203158Z
LAST-MODIFIED:20260330T203417Z
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SUMMARY:CSE Colloquium - The EU’s Cybersecurity Framework: what it is\, what it means
DESCRIPTION:Presenter: Chris Jay Hoofnagle\, Frederik Zuiderveen Borgesius\, Lothar Determann\, Pieter T.J. Wolters \nAbstract: \nThe European Union has enacted a comprehensive cybersecurity framework (the “Framework”) that imposes far-reaching obligations on developers of standalone software and connected products. This Article describes the European legislative approach before turning to a description of the Framework. Anchored by the Cyber Resilience Act and the Cybersecurity Act\, and reinforced by a constellation of sector-specific measures\, the Framework effectively creates a California-like-products-liability regime for software. It mandates extensive security-by-design obligations\, imposes stringent conformity assessment and incident-reporting duties\, and shifts substantial compliance burdens onto manufacturers\, importers\, and distributors. It even treats emotional wrongs caused by software as injurious. The Framework will take full effect in December 2027\, meaning that companies must integrate its requirements into their current product cycles. \nBio: Chris Hoofnagle is professor of law in residence at the University of California\, Berkeley\, where he teaches tort law and cybersecurity. \nHosted by: Professor Alvaro Cardenas \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-the-eus-cybersecurity-framework-what-it-is-what-it-means/
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:20260515T130000
DTEND;TZID=America/Los_Angeles:20260515T160000
DTSTAMP:20260404T101639
CREATED:20260402T204708Z
LAST-MODIFIED:20260402T204708Z
UID:10011844-1778850000-1778860800@events.ucsc.edu
SUMMARY:UCSC Graduate Research Symposium
DESCRIPTION:Friday\, May 15\, 1:00-4:00 PM (PDT) \nMcHenry Library | Information Commons South on the Main Floor \nWe are delighted to invite you to the 22nd Annual Graduate Research Symposium!\nThis event celebrates and highlights the work of UCSC graduate students in all academic divisions. Any enrolled graduate student is welcome to present either a poster\, talk\, or mixed media presentation. The event is free and open to the public. Please see the Graduate Division website for more information. \n 
URL:https://events.ucsc.edu/event/ucsc-graduate-research-symposium/
LOCATION:McHenry Library\, 1156 High St\, Santa Cruz\, CA\, 95064
CATEGORIES:Exhibits,Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T110000
DTEND;TZID=America/Los_Angeles:20260527T123000
DTSTAMP:20260404T101639
CREATED:20260330T203942Z
LAST-MODIFIED:20260330T203942Z
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SUMMARY:CSE Colloquium - Learning to Image: Computational Microscopy for Dynamic Systems
DESCRIPTION:Presenter: Laura Waller\, UC Berkeley \nAbstract: \nComputational imaging jointly designs hardware and algorithms to push beyond the classical limits of imaging\, enabling measurement of new quantities (e.g. 3D\, phase\, and super-resolution) with simple\, inexpensive hardware. These approaches have already transformed consumer photography; our goal is to achieve a similar transformation in scientific microscopy. \nIn this talk\, I will show how end-to-end learning is reshaping the design of imaging systems\, from programmable illumination with LED arrays to compact\, lensless cameras built from Scotch tape. By combining physical models with neural networks\, we can jointly learn how to capture data\, reconstruct images\, and self-calibrate systems that would otherwise be too complex to model. However\, many computational methods rely on multiple measurements\, limiting their use for live\, dynamic samples. I will introduce new space-time algorithms based on implicit neural representations (INRs) that jointly recover structure and motion\, correct artifacts\, and enable high-resolution imaging in regimes where traditional approaches fail. \nBio: \nLaura Waller is the Charles A. Desoer Professor of Electrical Engineering and Computer Sciences at UC Berkeley. She received B.S.\, M.Eng. and Ph.D. degrees from the Massachusetts Institute of Technology in 2004\, 2005 and 2010. After that\, she was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012. She is a Packard Fellow for Science & Engineering\, Moore Foundation Data-driven Investigator\, OSA Fellow\, and Chan-Zuckerberg Biohub Investigator. She has received the Carol D. Soc Distinguished Graduate Mentoring Award\, OSA Adolph Lomb Medal\, the SPIE Early Career Award and the Max Planck-Humboldt Medal. \nHosted by: Professor Alvaro Cardenas \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-learning-to-image-computational-microscopy-for-dynamic-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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