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DTSTART;TZID=America/Los_Angeles:20251103T160000
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DTSTAMP:20260417T164955
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SUMMARY:Statistics Seminar: Topological Clustering: from Multilayer Networks to Climate Resiliency and Beyond
DESCRIPTION:Presenter: Professor Yulia R. Gel\, Virginia Tech \nDescription: Multilayer networks continue to gain significant attention in many areas of study\, particularly\, due to their high utility in modeling interdependent systems such as critical infrastructures\, human brain connectome\, and socio-environmental ecosystems. However\, clustering of multilayer networks\, especially\, using the information on higher order interactions of the system entities\, yet remains in its infancy. We discuss a new topological approach for multilayer network clustering\, based on the rationale to group nodes not using the pairwise connectivity patterns or relationships between observations recorded at two individual nodes\, but based on how similar in shape their local neighborhoods are at various resolution scales.  We quantify shapes of local node neighborhoods using persistence diagrams and then consider either single linkage or k-means forms of topological clustering\, which allows us to systematically account for the important heterogeneous higher-order properties of node interactions within and in-between network layers and to integrate information from the node neighbors. In case of topological k-means\, we also show that casting it into an empirical risk minimization framework using reproducing kernel Hilbert spaces allows us to derive clustering stability guarantees\, similarly to the Euclidean k-means\, i.e.\, property that most existing topological clustering methods lack. We illustrate our topological clustering methods in application to assessing climate-induced risks in insurance and COVID-19 biosurveillance. \nBio: Yulia R. Gel is a Professor in the Department of Statistics at Virginia Tech. Her research interests focus on mathematical and statistical foundations of data science\, topological and geometric methods in artificial intelligence and machine learning\, risk analytics\, and graph learning\, with applications to assessing resilience of complex systems\, digital twins\, and early warning mechanisms. She holds a Ph.D in Mathematics\, followed by a postdoctoral position in Statistics at the University of Washington. Prior to joining Virginia Tech\, she was a tenured faculty member at the University of Waterloo\, Canada and University of Texas at Dallas. She also held visiting positions at Johns Hopkins University\, University of California\, Berkeley\, and the Isaac Newton Institute for Mathematical Sciences\, Cambridge University\, UK. In her recent stint (2021-2025) as Program Director in National Science Foundation (NSF) at the Division of Mathematical Sciences (DMS) and Directorate for Technology\, Innovation and Partnerships (TIP)\, she has served as a cognizant officer for various inter-agency interdisciplinary research programs at the interface of mathematical sciences and artificial intelligence\, including the NSF-FDA-NIH Foundations for Digital Twins as Catalyzers of Biomedical Technological Innovation (FDT-BioTech) and the NSF-NIH Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH). She has authored more than 150 publications in top statistical\, data mining and machine learning venues such as NeurIPS\, ICML\, ICLR\, AAAI\, KDD\, IJCAI\, and PNAS and served as senior area chair for ICML and NeurIPS. Her research has been continuously supported by ONR\, NASA\, and NSF. She is a Fellow of the American Statistical Association (ASA)\, recipient of the NSF2023 Director’s Award\, NSF STARS Awards\, and has multiple Best Paper Awards from the ASA Section on Statistics for Defense and National Security. \nHosted by: Professor Paul Parker
URL:https://events.ucsc.edu/event/statistics-seminar-topological-clustering-from-multilayer-networks-to-climate-resiliency-and-beyond/
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251027T160000
DTEND;TZID=America/Los_Angeles:20251027T170000
DTSTAMP:20260417T164955
CREATED:20251002T215037Z
LAST-MODIFIED:20251023T214046Z
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SUMMARY:Statistics Seminar: Sampling Depth Trade-Off in Function Estimation Under a Two-Level Design
DESCRIPTION:Presenter: Akira Horiguchi\, Visiting Assistant Professor\, University of California\, Davis \nDescription: Many modern statistical applications involve a two-level sampling scheme that first samples subjects from a population and then samples observations on each subject. These schemes often are designed to learn both the population-level functional structures shared by the subjects and the functional characteristics specific to individual subjects. Common wisdom suggests that learning population-level structures benefits from sampling more subjects whereas learning subject-specific structures benefits from deeper sampling within each subject. Oftentimes these two objectives compete for limited sampling resources\, which raises the question of how to optimally sample at the two levels. We quantify such sampling-depth trade-offs by establishing the L_2 minimax risk rates for learning the population-level and subject-specific structures under a hierarchical Gaussian process model framework where we consider a Bayesian and a frequentist perspective on the unknown population-level structure. These rates provide general lessons for designing two-level sampling schemes given a fixed sampling budget. Interestingly\, they show that subject-specific learning occasionally benefits more by sampling more subjects than by deeper within-subject sampling. We show that the corresponding minimax rates can be readily achieved in practice through simple adaptive estimators without assuming prior knowledge on the underlying variability at the two sampling levels. We validate our theory and illustrate the sampling trade-off in practice through both simulation experiments and two real datasets. While we carry out all the theoretical analysis in the context of Gaussian process models for analytical tractability\, the results provide insights on effective two-level sampling designs more broadly. \nBio: Akira Horiguchi is a Visiting Assistant Professor in the Department of Statistics at the University of California\, Davis. He was a Postdoctoral Associate in the Department of Statistical Science at Duke University\, advised by Professors Li Ma and Cliburn Chan. He completed his Ph.D. in Statistics at The Ohio State University\, advised by Professors Matthew T. Pratola and Thomas J. Santner. His research interests include improving nonparametric inference for flow cytometry data\, developing sensitivity analysis tools for regression trees\, and developing tree-based methods for tensor regression. \nHosted by: Professor Paul Parker
URL:https://events.ucsc.edu/event/statistics-seminar-sampling-depth-trade-off-in-function-estimation-under-a-two-level-design/
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251027T104000
DTEND;TZID=America/Los_Angeles:20251027T114500
DTSTAMP:20260417T164955
CREATED:20251020T180828Z
LAST-MODIFIED:20251022T183100Z
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SUMMARY:ECE 290 Seminar: Performance Bounds and Bottlenecks for Neuromorphic ML Accelerators
DESCRIPTION:Presenter: Jason Yik\, PhD Candidate\, Harvard SEAS \nDescription: Recent research on neuromorphic accelerators has investigated their efficiency and performance benefits for machine learning (ML) inference at the edge. This talk will focus on the performance implications of the fully-on-chip\, manycore-distributed memory architecture used by current neuromorphic accelerators. In conventional architectures\, the roofline model is a well-known performance model for denoting performance bounds and bottlenecks. For neuromorphics\, we show that bounds create a different shape\, a floorline\, and we demonstrate how to optimize ML deployment using the floorline as a performance guide. \nBio: Jason Yik is a PhD candidate at Harvard SEAS\, with a research focus in neuromorphic computing architectures. His prior work includes designing benchmark frameworks and tools for neuromorphic research\, and modeling and optimizing neuromorphic system performance. Currently\, he is an intern with the ASIC architecture team at Cerebras Systems. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1 \nRoom: E2-192
URL:https://events.ucsc.edu/event/ece-290-seminar-performance-bounds-and-bottlenecks-for-neuromorphic-ml-accelerators/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251021T120000
DTEND;TZID=America/Los_Angeles:20251021T130000
DTSTAMP:20260417T164955
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SUMMARY:CITRIS Aviation Prize Information Session
DESCRIPTION:Join us for this virtual info session on the 2025–26 CITRIS Aviation Prize\, an exciting multi-campus student competition inviting teams to design innovative solutions for the future of air mobility across the University of California. \nThe session will cover this year’s competition guidelines\, key dates and requirements\, and available resources. Attendees will also have the opportunity for Q&A with members of the CITRIS Aviation Leadership Committee\, composed of aviation research faculty from UC Berkeley\, UC Davis\, UC Merced\, and UC Santa Cruz. \nRegister here to attend. \nFor any questions\, contact aviationprize@citris-uc.org. \n  \nDate: Tuesday\, October 21 \nTime: 12:00 pm – 1:00 pm \nLocation: Zoom (register to attend).
URL:https://events.ucsc.edu/event/citris-aviation-prize-info/
CATEGORIES:Meetings & Conferences
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DTSTART;TZID=America/Los_Angeles:20251015T110000
DTEND;TZID=America/Los_Angeles:20251015T110000
DTSTAMP:20260417T164955
CREATED:20250924T212046Z
LAST-MODIFIED:20250924T212046Z
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SUMMARY:2025 Fall STEM Career & Internship Fair
DESCRIPTION:Here is a chance to meet tech recruiters in person! \nIf you are interested in pursuing a career in science\, technology\, engineering\, mathematics or research\, then take advantage of this opportunity to meet recruiters from companies looking to fill various positions (both technical and non-technical). Learn more about internships and full-time career opportunities. Undergraduate students\, graduate students\, and recent alumni are all welcome to attend! \nPLEASE NOTE: You are encouraged to check in at the student registration table in order to participate in the career fair. Bring your student ID. \nWant more support? \n\nVisit a peer coach during drop-in hours\nSchedule a career coaching appointment with a Career Engagement Specialist\nFor PhD students looking to pursue careers in industry\, explore Beyond the Professoriate\n	(Scroll over "Login to Platform" at the top navigation bar and click "Through your institution")\nGet career tips on demand from our Career Success YouTube video library\nStay in the loop by following Career Success on Instagram\n\nYou will receive registration and additional information in your email from Career Success via Handshake. Please make sure to check your junk/spam folder if you are not receiving any communication.\n  \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 fair date.
URL:https://events.ucsc.edu/event/2025-fall-stem-career-internship-fair/
LOCATION:Stevenson Event Center\, Stevenson Service Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences
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