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DTSTART;TZID=America/Los_Angeles:20260126T104000
DTEND;TZID=America/Los_Angeles:20260126T114500
DTSTAMP:20260417T231620
CREATED:20260112T223834Z
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SUMMARY:ECE Seminar: Tactile sensing: At the boundary between mechanical and computational intelligence in robotic grippers
DESCRIPTION:Presenter: Dr. Hannah Stuart\, Associate Professor in the Department of Mechanical Engineering\, University of California at Berkeley \nDescription: Robot grippers typically include mechanical intelligence (e.g.\, underactuation\, compliance) or computational intelligence (e.g.\, fully actuated with a wide array of sensors). Next generation grippers and hands will require both intelligences to work in concert across applications with resilience and dexterity. This talk will introduce the concept of mechanical and computational intelligence co-design through example case studies that focus on the particular importance of embodied sensitivity as a feature of the co-design process. For example\, the most recent work on the Smart Suction Cup\, conducted largely by Dr. Jungpyo Lee\, demonstrates how design decisions like the number of sensitive chambers influences the resultant robot arm controller as well as physical compliance and manufacturing feasibility and cost. \nBio: Dr. Hannah Stuart is an Associate Professor in the Department of Mechanical Engineering at the University of California at Berkeley. She received her BS in Mechanical Engineering at the George Washington University in 2011\, and her MS and PhD in Mechanical Engineering at Stanford University in 2013 and 2018\, respectively. Her research focuses on understanding the mechanics of physical interaction in order to better design systems for dexterous manipulation. Applications range from remote robotics to assistive orthotics. Recent awards include the NSF CAREER grant\, NASA Early Career Faculty grant\, Hellman Fellows Fund grant\, and Johnson & Johnson Women in STEM2D grant. She is a Senior Member of IEEE. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-seminar-tactile-sensing-at-the-boundary-between-mechanical-and-computational-intelligence-in-robotic-grippers/
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:20260126T120000
DTEND;TZID=America/Los_Angeles:20260126T130000
DTSTAMP:20260417T231620
CREATED:20260121T182735Z
LAST-MODIFIED:20260121T182735Z
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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/
LOCATION:https://ucsc.zoom.us/j/92479478035?pwd=S6b9SNtCorApA04sISbDwWqaF3wyPZ.1
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260126T123000
DTEND;TZID=America/Los_Angeles:20260126T133000
DTSTAMP:20260417T231620
CREATED:20260113T202943Z
LAST-MODIFIED:20260113T202943Z
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SUMMARY:CM Seminar - "Revealing Hidden Stories: Co-Designing the Thámien Ohlone Augmented Reality Tour"
DESCRIPTION:Presented by: Kai Lukoff \nDescription: \nThe Santa Clara University campus is adorned with symbols and monuments\, including a Spanish Mission Church\, that highlight its Catholic heritage. However\, the presence and history of the Ohlone Native Americans\, who have inhabited this land for thousands of years and continue to live in the region\, receive little to no recognition. How can we utilize augmented reality (AR) to share these hidden stories? \nIn collaboration with the Muwekma Ohlone Tribe\, our interdisciplinary team developed the Thámien Ohlone AR tour. This tour reveals hidden stories\, encourages visitors to engage in critical reflection\, and inspires visions of a more just future and received the Best Movie Award at CHI 2024\, the leading conference in the field of human-computer interaction. This talk will share insights on co-designing location-based AR experiences for social impact and explore the potential of AR in preserving cultural heritage. \nBio: Kai Lukoff is an assistant professor in the Department of Computer Science & Engineering at Santa Clara University. He leads the Human-Computer Interaction Lab\, focusing on technologies with social impact. His recent work focuses on co-design methods for location-based augmented reality. His research has been featured in prominent conferences such as CHI\, CSCW\, IMWUT\, and DIS\, and he was honored with the 2023 Outstanding Dissertation Award from ACM SIGCHI. \n  \nHosted by: Professor Sri Kurniawan \nWhen: Monday\, January 26\, 2026 from 12:30PM to 1:30PM \nLocation:  \nIN-PERSON @ UCSC Main Campus\, E2-280. \nViewing room @ SVC 3212. \nLUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend. \nZoom info: \nhttps://ucsc.zoom.us/j/95105219890?pwd=PXG6uexrh6P0Ry06aRkxfdTsLhaNhK.1\nMeeting ID: 951 0521 9890\nPasscode: 160917
URL:https://events.ucsc.edu/event/cm-seminar-revealing-hidden-stories-co-designing-the-thamien-ohlone-augmented-reality-tour/
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:20260126T160000
DTEND;TZID=America/Los_Angeles:20260126T170000
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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/
LOCATION: https://ucsc.zoom.us/j/97450297092?pwd=Bp4GIgR8dAuBeCd1Sz9vXo8unkYWQW.1
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260128T110000
DTEND;TZID=America/Los_Angeles:20260128T121500
DTSTAMP:20260417T231620
CREATED:20260120T191337Z
LAST-MODIFIED:20260120T191337Z
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SUMMARY:CSE Colloquium - Towards Relational Foundation Models: Zero-Shot Forecasting over Relational Databases
DESCRIPTION:Presenter: Charilaos I. Kanatsoulis\, Stanford University \nAbstract: Foundation models have transformed unstructured domains such as language and vision\, yet relational datasets\, where most enterprise knowledge lives\, still rely on brittle\, task-specific ML pipelines. I will begin by introducing Relational Deep Learning (RDL)\, a general framework for learning directly from heterogeneous multi-table data\, capturing structure across entities\, attributes\, and relationships without handcrafted schemas or features. \nBuilding on this paradigm\, I will present the Relational Transformer (RT)\, a schema-invariant model pretrained across diverse relational databases that performs structural learning with in-context information and transfers zero-shot to new databases and predictive tasks. By modeling both inter- and intra-table dependencies and reframing prediction as pattern recognition inside a unified latent relational space\, RT represents a concrete step toward relational foundation models that can be prompted\, reused\, and generalized for new problems. \nBio: Charilaos I. Kanatsoulis is a Research Scientist in the Department of Computer Science at Stanford University. He previously was a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania and received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota\, Twin Cities. His research lies at the intersection of machine learning and signal processing\, with a focus on Transformer and foundation model design for structured data\, graph representation learning\, tensor analysis\, and explainable AI. His work has been recognized with the Best Paper Award at the KDD Temporal Graph Learning Workshop (2025) and the Best Student Paper Award at IEEE CAMSAP (2023). He co-instructs CS246 and CS224W at Stanford and previously taught ESE 5140 at Penn. He has organized several community events\, including the Graph Signal Processing short course at IEEE ICASSP 2023\, the Stanford Graph Learning Workshop (2024–2025)\, the Relational Deep Learning tutorial at ACM KDD 2025\, and the New Perspectives in Advancing Graph Machine Learning Workshop at NeurIPS 2025. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (Refreshments such as coffee\, pastries\, and fruit will be provided.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-towards-relational-foundation-models-zero-shot-forecasting-over-relational-databases/
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:20260128T120000
DTEND;TZID=America/Los_Angeles:20260128T130000
DTSTAMP:20260417T231620
CREATED:20260121T235125Z
LAST-MODIFIED:20260128T171042Z
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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/
LOCATION:https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260129T114000
DTEND;TZID=America/Los_Angeles:20260129T131500
DTSTAMP:20260417T231620
CREATED:20260122T232352Z
LAST-MODIFIED:20260122T232352Z
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SUMMARY:BME 280B Seminar: Satellite repeats encode megabase-scale transcription factor hubs
DESCRIPTION:Presenter: Matt Franklin\, Postdoctoral Researcher\, Stanford University \nDescription: Eukaryotic genomes contain large stretches of repetitive DNA called satellite DNA\, often found near centromeres and ribosomal DNA regions. In humans\, alpha satellite has well-established roles in centromere biology\, however the functions of other human satellite DNAs remain largely unexplored. \nWe recently identified the Hippo pathway effector TEAD as a novel Human Satellite 3 (HSat3) binding TF. Because HSat3 is highly enriched near ribosomal DNA (rDNA) genes\, we examined whether the Hippo pathway regulates rDNA via HSat3. Our work demonstrates that HSat3 localizes the Hippo factors YAP and TEAD inside the nucleolus\, where YAP directly activates ribosomal RNA (rRNA) transcription. These findings present the first evidence that the Hippo pathway factor YAP directly regulates RNA Polymerase I activity. \nDisparate studies have identified examples of transcription factors that bind repetitive DNA elements through motif recognition. However\, a systematic search for such factors has not been conducted. Using Telomere-to-telomere genome assemblies\, we predicted and validated dozens of new satellite-binding TFs\, many of which are part of highly conserved signaling pathways. Beyond revealing a direct relationship between the Hippo pathway and ribosomal DNA regulation\, this work demonstrates that satellite DNA can encode a broad range of functional motifs\, hinting at new roles for these enormous genomic elements. \nBio: Following his undergraduate studies\, Matt conducted a 1-year research fellowship at EMBL Hamburg\, where he worked on X-ray scattering methods for structural biology. He then earned his PhD in chemical engineering at Stanford University\, where he investigated mechanotransduction and Hippo pathway signaling. Matt continued this research as a postdoc under Kun-Liang Guan at UC San Diego\, where he discovered that Hippo pathway effectors bind repetitive DNA elements. To expand on his newfound interest in repetitive DNA\, Matt returned to Stanford as a postdoctoral researcher under Nicolas Altemose\, where he is studying the functions of satellite repeats as hubs for transcription factor binding. \nHosted by: Professor Karen Miga\, BME Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-satellite-repeats-encode-megabase-scale-transcription-factor-hubs/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260129T130000
DTEND;TZID=America/Los_Angeles:20260129T163000
DTSTAMP:20260417T231620
CREATED:20260105T180456Z
LAST-MODIFIED:20260105T180456Z
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SUMMARY:Allen van Gelder Memorial
DESCRIPTION:You are cordially invited to an event celebrating the life and research legacy of Allen van Gelder\, who passed away in April 2025 after 37 years of dedicated service to the Computer Science and Engineering Department at UC Santa Cruz. \nThursday\, January 29\, 2025\nReception begins 1pm\, Program begins 1:30pm\nAlumni Room\, University Center\, UC Santa Cruz \nPlease RSVP via the following link:\nhttps://forms.gle/iyFTL2aAxLMWdRMU9 \nThe gathering will include three presentations reflecting on Allen’s contributions to computer science\, followed by an opportunity for attendees to share remembrances and stories \nOn Allen’s contributions to databases and logic programming\nJeff Ullman\, Stanford W. Ascherman Professor of Computer Science (Emeritus)\, Computer Science Department\, Stanford University \nOn Allen’s contributions to computer graphics and visualization\nClaudio Silva\, Institute Professor of Computer Science and Engineering\, NYU Tandon School of Engineering\, New York University \nOn Allen’s contributions to satisfiability\nOlaf Beyersdorff\, Professor of Theoretical Computer Science\, Institute of Computer Science\, Friedrich Schiller University Jena \nWhether you were a colleague\, student\, or friend\, we hope you’ll join us in celebrating Allen’s career and its impact.
URL:https://events.ucsc.edu/event/allen-van-gelder-memorial/
LOCATION:University Center\, University Center\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars,Social Gathering
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