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SUMMARY:Zheng\, Z. (STATS) - Semi-Supervised Statistical Learning for Oceanographic Data
DESCRIPTION:Oceanographic data\, generated by modern technologies that measure biological systems across time\, space\, and cell populations\, are often rich\, high-dimensional\, and highly heterogeneous. Such data provide valuable opportunities to study subcellular organization\, cellular heterogeneity\, and dynamic biological processes in marine environments. However\, because marine plankton systems remain relatively understudied and less well characterized than many model biological systems\, both data generation and labeling are particularly challenging. Limited domain knowledge and less mature laboratory protocols often produce noisy observations\, while reliable annotation requires substantial expert effort and is therefore difficult to obtain at scale.\nThis proposal develops statistical methodology for oceanographic data settings in which a small amount of expert-labeled data must be combined with a much larger collection of unlabeled or imperfectly processed data. A central goal is to incorporate limited scientific knowledge into statistical learning procedures to improve interpretability\, component identifiability\, and inferential reliability. In particular\, I develop semi-supervised statistical methods that explicitly quantify the information contributed by expert annotation.\nTo address this goal\, I study three related problems: semi-supervised functional clustering for subcellular spatial proteomics\, anchored semi-supervised mixture-of-experts models for flow cytometry\, and temporally structured latent-variable models that separate smooth trend and seasonal variation from scientific signals of interest. Together\, these projects aim to develop principled and interpretable methodology for partially labeled\, structured\, and high-dimensional oceanographic data\, with an emphasis on valid uncertainty quantification. \nEvent Host: Ziyue Zheng\, Ph.D. Student\, Statistical Science \nAdvisor: Sangwon Hyun \nZoom: https://ucsc.zoom.us/j/93229540289?pwd=8bsBOSBFmISlexmS4OWTmTZKp420u2.1
URL:https://events.ucsc.edu/event/zheng-z-stats-semi-supervised-statistical-learning-for-oceanographic-data/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260423T170000
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SUMMARY:Careers in Climate Tech & Sustainability
DESCRIPTION:Ready to explore career pathways that matter? \nAttend our very special Careers in Climate Tech & Sustainability Panel for an inside look at careers that will help build a sustainable future. Panelists representing different roles and organizations will share their career journeys and offer practical insights into working in climate tech. There will also be a catered networking reception that follows\, don’t miss it! \nGet informed\, inspired\, and discover your path to a career in sustainability! \n  \nThis event is part of Baskin Engineering’s Climate Tech Day featuring a community fair where students\, faculty\, climate/sustainability tech companies\, and community organizations will showcase their works through various means like demos\, poster presentations\, and tabling. This will be in the Baskin Courtyard from 2pm – 5pm.  \n  \n  \nIf you have disability-related needs\, please contact the Career Success office at csuccess@ucsc.edu or (831) 459-4420 as soon as possible. \n  \nYOU BELONG HERE\nPrograms and services 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. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/careers-in-climate-tech-sustainability-2/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Undergraduate
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DTSTART;TZID=America/Los_Angeles:20260423T170000
DTEND;TZID=America/Los_Angeles:20260423T181500
DTSTAMP:20260424T204108
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SUMMARY:Careers in Climate Tech & Sustainability
DESCRIPTION:Ready to explore career pathways that matter? \nAttend our very special Careers in Climate Tech & Sustainability Panel—celebrating Baskin Engineering Climate Week—for an inside look at careers that will help build a sustainable future. Panelists representing different roles and organizations will share their career journeys and offer practical insights into working in climate tech. There will also be a catered networking reception that follows—don’t miss it! \nGet informed\, inspired\, and discover your path to a career in sustainability! \nThis event is part of Baskin Engineering’s Climate Tech Day featuring a community fair where students\, faculty\, climate and sustainability tech companies\, and community organizations will showcase their works through demonstrations\, poster presentations\, tabling\, and more.  \nWhere: E2-180\nWhen: Thursday\, April 23\, 5:00-6:15 p.m. \nRegister via Handshake. \nIf you have disability-related needs\, please contact the Career Success office at csuccess@ucsc.edu or (831) 459-4420 as soon as possible. \nYOU BELONG HERE\nPrograms and services 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. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/careers-in-climate-tech-sustainability/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20260423T153000
DTEND;TZID=America/Los_Angeles:20260423T173000
DTSTAMP:20260424T204108
CREATED:20260401T183254Z
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SUMMARY:Pawl\, E. (STAT) - Flexible and Scalable Mixtures of Experts for Oceanographic Flow Cytometry Data
DESCRIPTION:Flow cytometry is a valuable technique in microbial research used to measure the optical properties of single-celled organisms at high throughput. Oceanographers often deploy flow cytometers on research cruises in order to study the characteristics of phytosynthetic microbes—called phytoplankton—in regions and times with diverse environmental conditions. Because cytometers cannot distinguish between subpopulations\, researchers typically cluster observations into subpopulations and subsequently analyze cluster characteristics. This two-stage workflow is often manual\, difficult to reproduce\, and fails to account for uncertainty in cluster assignments when relating subpopulation behavior to environmental conditions. To address these shortcomings\, statistical mixture models are gradually being introduced as alternatives to manual flow cytometry data analysis. However\, existing models either cannot use covariates or make restrictive assumptions about the relationships between cluster characteristics and covariates. Additionally\, they are designed to analyze individual cruises and consequently characterize local\, rather than global\, patterns in phytoplankton behavior. We propose to develop computationally efficient mixtures of experts which account for the complex dependency structures in oceanographic flow cytometry data. In this framework\, cells are probabilistically assigned to latent subpopulations\, while cluster-specific regressions relate each subpopulation’s optical properties and relative abundance to environmental conditions. Our first project develops a mixture of random weight neural network experts which can estimate arbitrary nonlinear regressions at low computational cost\, without a priori specification of functional forms. In the second project\, we develop a variational Bayesian mixture of experts which automatically selects variables without requiring cross-validation for hyperparameter selection. The final project incorporates spatial and temporal dependence\, allowing joint inference on data collected from multiple research cruises conducted at different locations and times. \nEvent Host: Ethan Pawl\, Ph.D. Student\, Statistical Science \nAdvisors: Sangwon Hyun & Paul Parker \nZoom- https://ucsc.zoom.us/j/96353239941?pwd=a4PJ94EMSD6D0SJ75S3WYzrPbYsBtn.1 \nPasscode- 244463
URL:https://events.ucsc.edu/event/pawl-e-stat-flexible-and-scalable-mixtures-of-experts-for-oceanographic-flow-cytometry-data/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260422T110000
DTEND;TZID=America/Los_Angeles:20260422T121500
DTSTAMP:20260424T204108
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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:20260416T110000
DTEND;TZID=America/Los_Angeles:20260416T130000
DTSTAMP:20260424T204108
CREATED:20260408T202602Z
LAST-MODIFIED:20260408T202602Z
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SUMMARY:Mirchandani\, C. (BMEB) - Population and Evolutionary Genomics Across Ecological Scales
DESCRIPTION:Sequencing technologies have transformed population and evolutionary genetics\, making it possible to ask questions at scales that were intractable a decade ago. Realizing that potential depends on tailored computational approaches\, and on the tools and infrastructure those approaches are built on. My dissertation works across this spectrum. Using an in vitro Drosophila cell culture system\, I show that mixed Wolbachia infections resolve rapidly and deterministically\, with one strain competitively excluding the other across host species and starting frequencies\, offering an explanation for why mixed infections are rarely observed in nature. In a deep-sea clam and its obligate bacterial symbiont\, I use two ultra-accurate sequencing methods and demographic modeling to directly estimate the effective transmission bottleneck between host generations\, finding it to be roughly eight symbionts\, orders of magnitude below prior cell-count estimates. I also present two tools for population genomics at scale: snpArcher\, a reproducible variant calling workflow developed for the California Conservation Genomics Project and now used across hundreds of species and tens of thousands of samples; and clam\, a Rust-based tool that efficiently estimates population genetic statistics by leveraging callable loci\, producing results identical to existing all-sites approaches at a fraction of the computational cost. Together\, these projects demonstrate how tailored computational approaches can unlock biological insight across diverse systems and scales. \nEvent Host: Cade Mirchandani\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics \nAdvisors: Russ Corbett-Detig & Shelbi Russell \nZoom- https://ucsc.zoom.us/j/98034081971?pwd=L5RoKoNEFxyapNhSRoXC8os2K2YZwv.1
URL:https://events.ucsc.edu/event/mirchandani-c-bmeb-population-and-evolutionary-genomics-across-ecological-scales/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260415T110000
DTEND;TZID=America/Los_Angeles:20260415T121500
DTSTAMP:20260424T204108
CREATED:20260407T155318Z
LAST-MODIFIED:20260407T155318Z
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SUMMARY:CSE Colloquium - Algorithmic Problems in Discrete Choice by Ravi Kumar
DESCRIPTION:Presenter: Ravi Kumar\, Google \nAbstract:\nIn discrete choice\, a user selects one option from a finite set of available alternatives\, a process that is crucial for recommendation systems applications in e-commerce\, social media\, search engines\, etc.  A popular way to model discrete choice is through Random Utility Models (RUMs).  RUMs assume that users assign values to options and choose the one with the highest value from among the available alternatives.  RUMs have become increasingly important in the Web era; they offer an elegant mathematical framework for researchers to model user choices and predict user behavior based on (possibly limited)  observations.   While RUMs have been extensively studied in behavioral economics and social sciences\, many basic algorithmic tasks remain poorly understood.  In this talk\, we will discuss various algorithmic and learning questions concerning RUMs. \nBio: \nRavi Kumar has been a research scientist at Google since 2012. Prior to this\, he was at the IBM Almaden Research Center and at Yahoo! Research. His interests include algorithms for massive data\, ML/privacy\, and the theory of computation. He maintains an extensive publication record that includes Test-of-Time Awards from STOC and WWW\, as well as Best Paper Awards from KDD and WWW\, to mention a few. He is an ACM fellow.\n\nHosted by: Professor Sungjin Im \n\nDate and Time: Wednesday\, April 15\, 2026 from 11:00 am – 12: 15 pm \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \n\nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3 \n\n 
URL:https://events.ucsc.edu/event/cse-colloquium-algorithmic-problems-in-discrete-choice-by-ravi-kumar/
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:20260414T140000
DTEND;TZID=America/Los_Angeles:20260414T160000
DTSTAMP:20260424T204108
CREATED:20260326T162922Z
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SUMMARY:Castello\, J. (CSE) - Space Mission Simulation From the Outside In
DESCRIPTION:Robotic space missions often use discrete event simulation to reduce risk in operation. A simulation applies a set of planned activities to a model of mission resources\, and the model’s observed behavior is used to predict real-world outcomes. However\, logically concurrent activities are typically simulated under one possible linearization of their events – an order that may not reflect the eventual reality. Some simulation systems provide mechanisms for controlling the order of events; but this is a solution to a self-imposed problem. We instead question the assumption causing this problem: that events are totally ordered to begin with. \nWe study Merlin\, an open-source simulator developed at Caltech’s Jet Propulsion Laboratory and designed by the author\, that exchanges the traditional total order of events for a *partial* order. Under this approach\, a resumed activity can only observe events that causally precede its resumption\, and concurrent events are reconciled under custom policies. However\, the resulting design is more complex (and less understood) than that of linearizing simulators\, obscuring its key insights. As such\, we have developed Eidolon: a compact core calculus for Merlin-style simulation whose operational semantics follows an “outside-in”\, substitution-based execution model. Although Eidolon is derived from the concepts present in Merlin\, we intend it to be a vehicle for exploring non-linearizing simulation in general. \nFirst\, we propose making Eidolon *incremental*: a change to the set of planned activities should not incur a full resimulation from scratch except in the worse case\, instead reusing any cached computations that are not sensitive to the change. Since mission planning is a highly iterative process involving many simulations and subsequent tweaks to the plan\, incremental resimulation may allow plans to be finalized in less time\, or allow higher-quality plans to be obtained in the same amount of time. The substitution-oriented approach of Eidolon is what makes this feasible\, since individual computations align cleanly with subtree boundaries. \nSecond\, in the spirit of Reynolds’ defunctionalization and Danvy’s rational reconstruction\, we propose developing a denotational semantics for Eidolon and demonstrating its mechanical conversion into an abstract machine. As a mathematical artifact\, Eidolon is designed for reasoning and legibility rather than efficiency; nonetheless\, defunctionalization allows us to *refactor* our semantics into something that stands a chance of being practical. In particular\, defunctionalization reifies the recursive structure of a denotational semantics into an explicit data structure. As a result\, the defunctionalized form of Eidolon will recover an explicit priority queue like that of traditional linearizing simulators\, but without their assumption of total ordering. \nEvent Host: Jonathan Castello\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Lindsey Kuper  \nZoom- https://ucsc.zoom.us/j/98171466380?pwd=L2rkpr8tEt0MZamYbxxPTfvhAd4gl6.1 \nPasscode- 990848 \n 
URL:https://events.ucsc.edu/event/castello-j-cse-space-mission-simulation-from-the-outside-in/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260414T130000
DTEND;TZID=America/Los_Angeles:20260414T170000
DTSTAMP:20260424T204108
CREATED:20260410T064650Z
LAST-MODIFIED:20260410T064650Z
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SUMMARY:Gen AI Dev Tools Bootcamp with AWS
DESCRIPTION:Are you curious about generative AI development tools and vibe coding? \nJoin us for an excitng in-person boot camp where you will take part in a hands-on lab that will introduce you to the fundamentals of this rapidly growing field. \nHere’s the tentative schedule: \n1:00 – 1:45 – Intro to AI/ML \n1:45 – 2:45 – Hands-on developers workshop \n3:00 – 3:20 – Student demos \n3:20 – 3:50 – Career panel \n3:50 – 4:00 – Kahoot and closing (with giveaways) \n  \nIMPORTANT NOTE: Because participants will be given a temporary AWS account\, all attendees MUST register in advance!!! That means that registration will close on April 6th\, no exceptions! \nIf you have disability-related needs\, please contact the Career Success office at csuccess@ucsc.edu or (831) 459-4420 as soon as possible. \n  \nYOU BELONG HERE\nPrograms and services 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. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/gen-ai-dev-tools-bootcamp-with-aws/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Undergraduate
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DTSTART;TZID=America/Los_Angeles:20260413T104000
DTEND;TZID=America/Los_Angeles:20260413T114500
DTSTAMP:20260424T204108
CREATED:20260409T225747Z
LAST-MODIFIED:20260409T225806Z
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SUMMARY:ECE 290 Seminar: Speaker Dr. Josh Star-Lack - Photon Counting Detectors for X-Ray Computed Tomography
DESCRIPTION:Presenter: Dr. Josh Star-Lack\, Principal Scientist and Research Manager\, Varex Imaging Inc \nDescription: X-ray computed tomography (CT) provides rapid\, detailed 3D imaging of internal organs\, bones\, and vasculature. By enabling the swift diagnosis of cancer\, cardiac disease\, neurological disorders\, and other pathologies\, CT has revolutionized medicine—reducing the need for invasive exploratory surgeries and facilitating precise treatment planning. Despite the technology’s maturity\, the clinical demand for higher spatial resolution\, increased sensitivity\, and lower ionizing radiation doses remains high. This presentation reviews the fundamental principles of CT\, traces its evolution since its invention 50 years ago\, and describes a new technology\, photon-counting x-ray detection\, as a transformative solution to current clinical challenges. \nBio: Josh Star-Lack received his B.S. in Applied and Engineering Physics from Cornell University and Ph.D. in Electrical Engineering from U.C. Berkeley. He has worked on the development of medical imaging technologies\, including X-ray computed tomography and magnetic resonance imaging\, for his entire professional career. He is currently a Principal Scientist and Research Manager at Varex Imaging Inc\, the world’s largest manufacturer of X-ray detectors and tubes. He has co-authored over 150 publications and holds over 50 patents. \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-290-seminar-speaker-dr-josh-star-lack/
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:20260409T100000
DTEND;TZID=America/Los_Angeles:20260409T120000
DTSTAMP:20260424T204108
CREATED:20260325T172250Z
LAST-MODIFIED:20260325T172250Z
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SUMMARY:Ticknor\, B. (STAT) - Clustering and Tractable Multivariate Inference for Extremes
DESCRIPTION:Modeling environmental extremes often involves large collections of spatial or temporal records where both clustering similar series and modeling dependence among extremes are challenging tasks. This Ph.D. proposal addresses several related problems in extreme value analysis. In particular\, we study how to cluster many time series based on their extremal behavior using strategies defined via univariate extremal models\, motivated by an application to 975 coastal wave-height records. We also investigate the development of scalable multivariate models for dependent extremes. A tractable construction based on a latent multivariate $t$ process with generalized extreme value margins is proposed\, together with a regularization strategy that encourages extremal dependence consistent with a max-stable limit while preserving likelihood-based inference. Together\, these efforts aim to provide practical tools for analyzing large collections of environmental extremes. \nEvent Host: Benjamin Ticknor\, Ph.D. Student\, Statistical Science \nAdvisor: Robert Lund \nZoom- https://ucsc.zoom.us/j/94347069554?pwd=21jbzUIlbopj2OFRySIHmBV11Ngoef.1 \nPasscode- 822764 \n 
URL:https://events.ucsc.edu/event/ticknor-b-stat-clustering-and-tractable-multivariate-inference-for-extremes/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260402T090000
DTEND;TZID=America/Los_Angeles:20260402T110000
DTSTAMP:20260424T204108
CREATED:20260312T191832Z
LAST-MODIFIED:20260312T191832Z
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SUMMARY:Learn to use the UCSC Genome Browser
DESCRIPTION:The UCSC Genome Browser is hosting a free workshop for new and advanced users!\n\nBeginner session starts at 9 and will include:\n\nHow to understand genome annotations\nHow to find annotation data\nOverview of training resources\n\nIntermediate + advanced session starts at 10\, covering\n\nCustom tracks\nTrack hubs\nBLAT\nAnd more!!\n\n\nJoin us and be the FIRST to see a brand new feature! At the end we’ll have free stickers and a chance to ask the team questions.\n\nBring your laptop to follow along\, and we encourage questions. See you April 2nd in E2 180.
URL:https://events.ucsc.edu/event/learn-to-use-the-ucsc-genome-browser/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Training,Workshop
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260401T110000
DTEND;TZID=America/Los_Angeles:20260401T121500
DTSTAMP:20260424T204108
CREATED:20260325T164503Z
LAST-MODIFIED:20260330T203519Z
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SUMMARY:CSE Colloquium - Messages from across the event horizon:  AI Agentic Design for Computer Architecture (and more generalizable learnings)
DESCRIPTION:Presenter: Christopher Fletcher\, UC Berkeley \nAbstract: \nIt is difficult to escape the hype of agentic coding.  Is the hype real?  Are we still living in ~Summer 2025 — when AI coding would accomplish little more than upset its human supervisor?  Or has a level shift in technology finally arrived? \nIn this talk I will argue the latter.  I will describe a self-imposed experiment to discover modern AI coding tools’ capabilities (starting mid February 2026).  I will try (my best) to communicate my utter and sheer surprise at where the state of the art actually is.  Then I will do a deep dive and try to relay everything I have learned about this new engineering discipline—based on my attempts to push the technology as hard as I can for the past 1.5 months.  I will conclude by pontificating about the future of computer architecture and academic research more generally. \nBio: \nChristopher Fletcher is an Associate Professor of EECS at UC Berkeley. He is a computer architect whose research spans architecture\, security\, and domain-specific acceleration\, especially at their intersections from cryptography and hardware attacks to algorithm-to-hardware co-design. His work has received 31 paper recognitions and several other honors\, including the NSF CAREER Award\, Intel and Google faculty awards\, UIUC research and promotion awards\, election to DARPA ISAT\, and MIT’s George M. Sprowls Award\, with related work also recognized by Scientific American as one of ten “World Changing Ideas.” \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 \n 
URL:https://events.ucsc.edu/event/cse-colloquium-messages-from-across-the-event-horizon-ai-agentic-design-for-computer-architecture-and-more-generalizable-learnings/
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:20260313T100000
DTEND;TZID=America/Los_Angeles:20260313T120000
DTSTAMP:20260424T204108
CREATED:20260304T172425Z
LAST-MODIFIED:20260304T172425Z
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SUMMARY:Moghadam\, M. (CE) - Constraint-Aware Scene Understanding and Trajectory Generation Using Deep Reinforcement Learning for Autonomous Vehicles
DESCRIPTION:Advanced driver-assistance systems (ADAS) are commonly organized as modular pipelines that transform raw sensor measurements into low-level actuation commands through perception\, planning\, and control. While learning-based methods have achieved state-of-the-art performance in perception and environment modeling\, the planning layer remains a key bottleneck for reliable autonomy. Highway driving in particular requires long-horizon reasoning and socially aware interaction with multiple actors\, while also producing smooth and dynamically feasible motion that can be tracked by classical controllers. \nThis thesis focuses on scene understanding and planning for highway driving. We study the problem through two complementary simulation environments: the high-fidelity CARLA simulator for motion planning and continuous trajectory generation under realistic vehicle dynamics and road geometry\, and the lightweight HighwayEnv simulator for interaction-rich behavior planning at high episode throughput. \nWe present three planning contributions that increase autonomy. First\, we introduce a modular hierarchical planning framework in Frenet space that combines long-term decision-making with short-term trajectory optimization. The approach includes a corridor-based dynamic obstacle avoidance strategy that generates spatiotemporal polynomial trajectories and supports diverse driving styles through interpretable parameter tuning. Second\, we propose an end-to-end continuous deep reinforcement learning approach that unifies decision-making and motion planning into a single policy that outputs continuous polynomial trajectories in the Frenet frame. A spatiotemporal observation tensor and a temporal convolutional backbone enable the learned planner to exploit interaction history and outperform optimization-based and discrete RL baselines in CARLA. Third\, we develop an interaction-aware behavior planning neural network architecture that couples trajectory prediction with high-level decision-making via a social pooling scene encoder built on actor histories and an ego-centered BEV representation. This unified design improves RL social awareness\, safety\, and overall driving performance in multi-agent highway scenarios in HighwayEnv. \nAcross extensive simulation studies\, the results show that constraint-aware representations and learning-based policies can improve planning quality beyond hand-crafted objectives\, especially when the policy is equipped with spatiotemporal social context while retaining classical feedback control for stable trajectory tracking. Finally\, we provide supporting simulation and evaluation infrastructure\, including observation tensor and neural network designs\, BEV utilities\, and scalable training and testing pipelines\, to enable reproducible research on learning-based planning in interactive traffic. \nEvent Host: Majid Moghadam\, Ph.D. Candidate\, Computer Engineering  \nAdvisor: Gabriel Elkaim \nZoom- https://ucsc.zoom.us/j/95848602314?pwd=2jlktZ6BChlXcyqT3anX4ZuKrYV4wE.1 \nPasscode- 325939
URL:https://events.ucsc.edu/event/moghadam-m-ce-constraint-aware-scene-understanding-and-trajectory-generation-using-deep-reinforcement-learning-for-autonomous-vehicles/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260311T110000
DTEND;TZID=America/Los_Angeles:20260311T121500
DTSTAMP:20260424T204108
CREATED:20260303T181914Z
LAST-MODIFIED:20260303T181914Z
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SUMMARY:CSE Colloquium: Co-Active AI-Assisted Programming
DESCRIPTION:Presenter: Nadia Polikarpova\, UCSD \nAbstract: \nAI-assisted programming has rapidly moved from novelty to default. Today\, most developers use AI coding tools\, and increasingly rely on agentic systems capable of making multi-step design and implementation decisions with minimal human guidance. While these systems boost productivity\, they also introduce new risks: developers may disengage from the reasoning behind generated code\, leading to shallow understanding\, loss of ownership\, and what is increasingly described as cognitive debt. \nIn this talk\, I argue that AI-driven software development must be co-active: humans and AI should remain continuously engaged in a shared process of understanding and decision-making. I will present two complementary research directions toward this goal. The first focuses on observability—helping developers understand\, validate\, and trace the behavior of AI-generated code. The second focuses on controllability—making AI decisions explicit\, persistent\, and steerable. Together\, these ideas restore programmer agency while maintaining the productivity gains of AI-assisted development. \nBio: \nNadia Polikarpova is an associate professor at UC San Diego\, and a member of the Programming Systems group. She received her Ph.D. in Computer Science from ETH Zurich in 2014\, and then spent some time as a postdoctoral researcher at MIT. Nadia’s research interests are at the intersection of programming languages\, AI\, human-computer interaction\, and social computing. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (*Refreshments such as coffee\, tea\, pastries\, and fresh fruit will be available.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-co-active-ai-assisted-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:20260309T104000
DTEND;TZID=America/Los_Angeles:20260309T114500
DTSTAMP:20260424T204108
CREATED:20260305T230039Z
LAST-MODIFIED:20260305T230039Z
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SUMMARY:ECE 290 Seminar: Dynamical Signatures: Harnessing the Hidden Language of In-Space Electric Propulsion
DESCRIPTION:Presenter: Dr. Christine Greve\, Research Engineer\,  Edwards AFB \nDescription: Low-thrust space electric propulsion systems offer long propulsion system lifetimes for satellite maintenance maneuvers. These thrusters operate by generating and accelerating plasmas\, making the thrusters throttleable\, propellant-efficient\, and scalable from low-to-high power operations. This talk will focus on efforts to leverage the underlying time-dependent dynamics of plasma to investigate and influence thruster research and development. Prior years of study have developed techniques to uniquely represent the dynamics of such systems that have since been used to open a new way to test and operate plasma systems. Additional work has investigated the correlations between time-dependent measurements of these dynamics to develop digital twins\, automate test processes with machine learning\, inform design of experiments\, and develop on-orbit system diagnostics. The talk will conclude with a look to the future as these tools are further applied both within the lab and potentially transitioned to on-orbit applications. \nBio: Dr. Christine Greve is a research engineer for the Air Force Research Laboratory at Edwards AFB. She received her Ph.D. in Aerospace Engineering from Texas A&M University under an NDSEG fellowship for her work in data-driven modeling of plasma-based systems. She now serves as the Electric Propulsion group lead with interests in high-power electric propulsion\, machine learning\, data-driven modeling\, and novel plasma diagnostic techniques. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-dynamical-signatures-harnessing-the-hidden-language-of-in-space-electric-propulsion/
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:20260305T130000
DTEND;TZID=America/Los_Angeles:20260305T150000
DTSTAMP:20260424T204108
CREATED:20260217T182432Z
LAST-MODIFIED:20260217T182432Z
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SUMMARY:Xu\, Y. (CSE) - Right Place\, Right Time: Accelerating Edge Computation on Modern Heterogeneous SoCs
DESCRIPTION:Modern edge computing increasingly relies on heterogeneous System-on-Chip (SoC) architectures. These chips tightly integrate general-purpose CPUs with various specialized accelerators\, including GPUs\, FPGAs\, and AI accelerators\, all under a shared memory architecture. Although these shared-memory SoCs enable more efficient communication and data sharing between different processing units\, they are notoriously difficult to program and tune due to architectural diversity across vendors and asymmetric compute capabilities within each SoC. \nThis dissertation introduces Redwood and BetterTogether\, two frameworks that rethink CPU-accelerator collaboration on heterogeneous SoCs. Redwood targets a class of algorithms termed traverse–compute\, that combine irregular tree traversals with dense leaf-level computation\, e.g.\, Nearest-Neighbor Search and Barnes–Hut algorithm. \nIt addresses the efficient mapping of these algorithms onto heterogeneous systems by exploiting the architectural strengths of CPUs\, GPUs\, and FPGAs. BetterTogether extends this methodology to a different class of edge workloads\, specifically multi-stage pipelines and neural networks commonly used in computer vision tasks. Furthermore\, it introduces interference-aware analysis and scheduling techniques tailored for mobile SoCs. Finally\, to broaden the scope of heterogeneous acceleration\, we evaluated emerging domain-specific accelerators. We provide a preliminary analysis of Tensor Processing Units and Tensor Cores within the context of modern programming abstractions. \nEvent Host: Yanwen Xu\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Tyler Sorensen \nZoom- https://ucsc.zoom.us/j/5354629158?pwd=0CVhbwLuXDMX5fAGZd63tcfNqDWp0t.1 \nPasscode- 114514
URL:https://events.ucsc.edu/event/xu-y-cse-right-place-right-time-accelerating-edge-computation-on-modern-heterogeneous-socs/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260304T110000
DTEND;TZID=America/Los_Angeles:20260304T121500
DTSTAMP:20260424T204108
CREATED:20260217T182353Z
LAST-MODIFIED:20260217T182353Z
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SUMMARY:CSE Colloquium - Improving Efficiency and Reliability of Foundation Models in Clinical AI
DESCRIPTION:Presenter: Vasiliki “Vicky” Bikia\, PhD\, Stanford Department of Biomedical Data Science and Institute for Human-Centered AI (HAI) \nAbstract: \nDeploying foundation models in health requires both computational efficiency and reliable generation. In this talk\, I present two studies that address these dimensions separately but with a shared goal of real-world clinical deployment. The first study focuses on reduced-resolution distillation for multimodal clinical data\, particularly medical imaging. As model and input sizes increase\, inference cost and memory constraints become major barriers to deployment. We investigate how high-capacity teacher models can transfer structured knowledge to compact student models trained on downsampled images\, using embedding-space supervision to preserve clinically meaningful representations while reducing computational footprint. The second study examines the reliability of AI-generated clinical text. Foundation models are increasingly used to produce discharge summaries and patient-facing explanations\, yet fluency does not guarantee safety. We develop a structured evaluation framework grounded in clinical error taxonomies and clinician-calibrated metrics to quantify hallucinations\, omissions\, and semantic misalignment. Together\, these studies emphasize that scalable clinical AI requires not only smaller and faster models\, but also rigorous evaluation of generative reliability before deployment. \nBio: \nVasiliki Bikia is a Postdoctoral Researcher at Stanford University\, affiliated with the Department of Biomedical Data Science and the Stanford Institute for Human-Centered Artificial Intelligence (HAI). She received an Advanced Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki\, and a Ph.D. in Bioengineering from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her research focuses on medical foundation models\, structured representations of health data\, and the evaluation of generative systems in clinical settings. Previously\, she was a Machine Learning Scientist at the Mussallem Center for Biodesign at Stanford University\, where she developed software pipelines to improve data accessibility and interoperability in digital health applications. Vasiliki was selected as an MIT Rising Star in EECS (2025) and as an Emerson Consequential Scholar (2025)\, and is actively engaged with the Silicon Valley entrepreneurial ecosystem through collaborations at the intersection of research\, industry\, and healthcare. She is an organizing member of the Conference on Health\, Inference\, and Learning (CHIL) and serves as Unconference Chair for the 2025 and 2026 editions\, where she leads the design and execution of the entrepreneurship-focused track bridging academic research and real-world deployment. Her work has appeared in venues including IEEE journals\, npj Digital Medicine\, Nature Communications\, and leading AI conferences\, and she has contributed to multiple funded research proposals and clinical studies at the intersection of AI\, medicine\, and translational impact. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, E2-180 (*Refreshments such as coffee\, tea\, fresh fruit\, and pastries will be provided) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-improving-efficiency-and-reliability-of-foundation-models-in-clinical-ai/
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:20260302T104000
DTEND;TZID=America/Los_Angeles:20260302T114500
DTSTAMP:20260424T204108
CREATED:20260224T232851Z
LAST-MODIFIED:20260224T232851Z
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SUMMARY:ECE 290 Seminar: Precision Nuclear Medicine: Engineering Solutions from Acquisition to Analysis
DESCRIPTION:Presenter: Spencer L. Bowen\, Assistant Professor in the Departments of Radiology and Biomedical Engineering\, UT Southwestern Medical Center \nDescription: The Bowen Lab focuses on the development of tools for positron emission tomography (PET) and hybrid systems (e.g. PET/CT)\, to advance precision imaging for the care and study of oncology\, neurology\, and cardiology patients. Quantitative metrics from PET are integral to both patient workup and clinical research. However\, current approaches to enable quantitative imaging have substantial performance limitations that can compromise study conclusions\, fail to generalize across exams and scanners\, expose patients to additional ionizing radiation\, or necessitate invasive procedures. To address these key barriers\, Dr. Bowen and his team investigate advanced acquisition techniques\, image reconstruction algorithms\, and post-processing methods. Their studies span from digital simulations to human subjects research. This lecture will cover recent developments by the Bowen Lab\, including 1) advanced PET data correction methods for low-dose and standalone exams\, 2) non-invasive fully quantitative imaging\, and 3) leveraging topical sensors to detect faulty radiotracer injections. \nBio: Spencer L. Bowen\, Ph.D.\, is an Assistant Professor in the Departments of Radiology and Biomedical Engineering at UT Southwestern Medical Center. He earned his doctorate in biomedical engineering from University of California\, Davis\, where he developed hardware and algorithmic solutions to enable quantification with a breast PET/CT scanner. Dr. Bowen then worked as a research fellow at Massachusetts General Hospital on precision PET imaging methods for combined PET/MR. Prior to joining the UT Southwestern faculty in 2020\, he served as a research assistant professor at the Fralin Biomedical Research Institute at Virginia Tech-Carilion. Dr. Bowen’s research program is funded by both industry and the NIH. His work has been featured on the cover of the Journal of Nuclear Medicine and detailed by the press. \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-290-seminar-precision-nuclear-medicine-engineering-solutions-from-acquisition-to-analysis/
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:20260225T173000
DTEND;TZID=America/Los_Angeles:20260225T190000
DTSTAMP:20260424T204108
CREATED:20260130T054047Z
LAST-MODIFIED:20260209T232119Z
UID:10009139-1772040600-1772046000@events.ucsc.edu
SUMMARY:Exploring Research Pathways at Baskin Engineering
DESCRIPTION:Curious how being part of a research lab can supercharge your experience as a Baskin Engineer?   \nJoin us for this informative event to learn about opportunities to solve open-ended problems\, build deeper technical skills\, and learn how to think like an engineer. \nWe’ll kick things off with a quick overview of the kinds of research opportunities available to undergrads and how to get started\, then you’ll hear directly from students who’ve worked in research labs as undergraduates. They’ll share what they actually did day-to-day\, the skills they built (technical and professional)\, and how research shaped their confidence\, career goals\, and next steps. We’ll then have pizza and networking to end the evening. \nWhether you’re aiming for industry\, graduate school\, or just want hands-on experience that goes beyond coursework\, this panel will help you understand how undergraduate research can set you apart—academically\, professionally\, and personally! \n\nRegister via Handshake. \nYOU BELONG HERE\nPrograms and services 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. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/exploring-research-pathways-at-baskin-engineering/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T110000
DTEND;TZID=America/Los_Angeles:20260225T121500
DTSTAMP:20260424T204108
CREATED:20260224T172405Z
LAST-MODIFIED:20260224T172405Z
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SUMMARY:CSE Colloquium: Agile and evolvable software construction in the era of rapidly evolving hardware accelerator designs
DESCRIPTION:Presenter\n\nCharith Mendis\, Siebel School of Computing and Data Science\, University of Illinois at Urbana-Champaign\n\nAbstract\n\nModern AI workloads have become exceedingly abundant and important in the current computing landscape. As a result\, there have been numerous software and hardware innovations aimed at accelerating these workloads. However\, we observe a subtle disconnect between the software and hardware communities. Most software innovations target well-established hardware platforms such as CPUs (e.g.\, x86\, ARM) and GPUs (e.g.\, NVidia GPUs)\, while hardware innovations produce plenty of other tensor accelerator designs (e.g.\, Gemmini\, Feather\, Trainium) each year.\n\nWe asked the question\, why aren’t the software community using these accelerators or even evaluating on them? The simple yet undeniable reason is the lack of standardized software tooling compared to CPUs and GPUs. For an architecture to be used\, properly designed compiler backends\, correctness\, and performance testing tools should be abundant (e.g.\, CUDA ecosystem).\n\nIn this talk\, I will describe how we bridge this gap by automatically generating the necessary software tools for a large class of accelerators through the Accelerator Compiler Toolkit (ACT) ecosystem. Central to ACT is an ISA definition language\, TAIDL\, that for the first time standardizes the hardware-software interfaces for a large class of accelerators. Departing from the traditional approach of manually constructing test oracles\, performance models\, or retargetable compiler backends\, we instead introduce agile and evolvable methodologies to automatically generate such necessary tooling using both formal methods and machine learning techniques for any TAIDL-defined accelerator interface. I will show how such automation enables rapid software prototyping\, making rapidly evolving accelerator designs usable by the software community.\n\nBio\n\nCharith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign. His broad research interests are at the intersection of compilers\, programming languages\, and machine learning. He received his Ph.D. and Master’s from the Massachusetts Institute of Technology and his B.Sc. from the University of Moratuwa. He is the recipient of the DARPA Young Faculty Award\, the NSF CAREER Award\, the Google ML and Systems Junior Faculty Award\, the Outstanding Advisor award at UIUC\, the William A. Martin Outstanding Master’s Thesis Award at MIT\, and the University Gold Medal for his B.Sc. He has won numerous paper awards\, including a Distinguished Paper Award at POPL\, a Best Student Paper Award at the IEEE BigData conference\, an honorable mention for the Best Artifact Award at SIGMOD\, a Best Paper Award at ML for Systems workshop at ISCA\, and an IEEE Top Picks Honorable Mention.\n\nHosted by: Professor Nikos Tziavelis\n\nLocation: Engineering 2\, E2-180 (Refreshments such as fruit\, pastries\, tea\, and coffee will be available for guests.)\n\nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3 
URL:https://events.ucsc.edu/event/cse-colloquium-agile-and-evolvable-software-construction-in-the-era-of-rapidly-evolving-hardware-accelerator-designs/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260224T170000
DTEND;TZID=America/Los_Angeles:20260224T181500
DTSTAMP:20260424T204108
CREATED:20260130T054112Z
LAST-MODIFIED:20260209T231917Z
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SUMMARY:AI and Security 101
DESCRIPTION:Join us for an informative conversation with Neta Haiby\, Head of Product | AI Security at Microsoft! \nArtificial Intelligence is transforming both cyber defense and cyber offense. It creates unique risks in how we build\, deploy\, and operate AI apps and Agents. This session examines how AI can be attacked or misused – through techniques such as jailbreaks\, intent breaking\, and supply-chain compromise and discusses practical defense strategies\, including guardrails\, access controls\, monitoring\, and evaluation. \nDesigned for students interested in cybersecurity and AI\, this session emphasizes a practical understanding of AI security. \nAttendees will also receive resources to help them further explore and get started in the field! \nDon’t miss this highly informative event! \nYOU BELONG HERE\nPrograms and services 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. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/ai-and-security-101/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Undergraduate,Workshop
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T104000
DTEND;TZID=America/Los_Angeles:20260223T114500
DTSTAMP:20260424T204108
CREATED:20260219T235259Z
LAST-MODIFIED:20260219T235259Z
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SUMMARY:ECE 290 Seminar: High-Frequency Circuits for Next-Generation Communication: From Beyond-5G mm-Wave MIMO to Co-Packaged Optics
DESCRIPTION:Presenter: Susnata Mondal\, Research Scientist\, Intel \nDescription: \nRapid growth in wireless connectivity\, cloud computing\, and AI infrastructure is driving an urgent need for communication systems that can deliver higher data rates with improved energy efficiency. Meeting these demands requires advances in high-frequency circuit design across both wireless and wireline domains\, spanning millimeter-wave radios to optical interconnects. \nThis seminar will present recent developments in two complementary directions. The first focuses on millimeter-wave MIMO systems for beyond-5G communication. Conventional phased arrays are typically limited to single-stream beamforming\, while fully digital solutions\, although flexible\, incur significant power and area overhead. Emerging hybrid architectures enable multi-stream\, multi-band operation with improved spectral efficiency by combining RF and baseband beamforming\, supporting carrier aggregation\, adaptive spatial processing\, and full-duplex operation. Prototype systems have demonstrated scalable multi-antenna transceivers operating across 28/37 GHz bands\, integrating RF front-ends\, beamforming networks\, and system-level signal processing. \nThe second direction addresses high-performance computing interconnects\, where electrical links increasingly struggle with loss and energy efficiency at high data rates. Co-packaged optics offers a promising alternative by placing optical engines in close proximity to compute and switch chips\, improving link efficiency. The seminar will discuss circuit and system innovations enabling scalable optical I/O\, including equalization\, clocking\, and high-linearity design techniques for high-speed optical links\, along with recent prototype demonstrations achieving high data rates with low energy per bit. \nBio: Susnata Mondal received the B.Tech. and M.Tech. degrees in E&ECE from IIT Kharagpur in 2015 and the Ph.D. degree in ECE from Carnegie Mellon University\, Pittsburgh\, in 2020. Since then\, he has been a Research Scientist at Intel\, Hillsboro\, working on co-packaged optics and high-speed I/O. He has authored several lead-author papers in ISSCC and JSSC and holds 18 U.S. patents. He is a Technical Program Committee member of RFIC and an Associate Editor for TCAS-I\, TCAS-II\, and SSCL. His honors include the SSCS Predoctoral Achievement Award\, the Best Ph.D. Thesis Award from CMU ECE\, and selection as an SSCS Rising Star. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-high-frequency-circuits-for-next-generation-communication-from-beyond-5g-mm-wave-mimo-to-co-packaged-optics/
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:20260220T140000
DTEND;TZID=America/Los_Angeles:20260220T160000
DTSTAMP:20260424T204108
CREATED:20260210T193542Z
LAST-MODIFIED:20260210T193542Z
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SUMMARY:Fredrickson\, K. (CSE) - Practical Anonymity with Formal Resistance to Traffic Analysis
DESCRIPTION:Anonymous communication systems hide who is talking to whom\, not just what is said. However\, existing systems are either vulnerable to traffic analysis attacks–attacks where adversaries observe and correlate the network traffic of users–or are forced to rely on unrealistic and unenforceable assumptions about how users behave. Worse\, existing theory lacks tools to rigorously model traffic analysis attacks\, much less inform whether if a system is secure against traffic analysis or how to design systems that are. \nWe make several contributions toward our goal of practical anonymity systems that resist traffic analysis. First\, we develop the first formal framework for describing the security of systems against traffic analysis attacks\, allowing us to quantitatively describe and compare the security of all existing works. Second\, leveraging this framework\, we develop a security definition that distinguishes between systems that are and are not susceptible to traffic analysis. We call this property input/output independence. We use this definition to prove that the dominant model of systems–synchronous systems–cannot practically provide input/output independence. We then design a new asynchronous anonymity functionality\, deferred retrieval\, that achieves input/output independence with far more flexible user assumptions and up to 3400 times less traffic overhead for the same latency compared to prior methods. Finally\, we design and implement Sparta\, a family of high-throughput\, scalable instantiations of deferred retrieval using trusted execution environments and oblivious algorithms\, yielding the first practical anonymity systems that are formally resistant to long-term traffic analysis. \nEvent Host: Kyle Fredrickson\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Darrell Long \nZoom – https://ucsc.zoom.us/j/98133127429?pwd=QNICsMrQa6bQUKNPo40PthZyQEQCFl.1 \nPasscode – 242206
URL:https://events.ucsc.edu/event/fredrickson-k-cse-practical-anonymity-with-formal-resistance-to-traffic-analysis/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260218T110000
DTEND;TZID=America/Los_Angeles:20260218T121500
DTSTAMP:20260424T204108
CREATED:20260210T212856Z
LAST-MODIFIED:20260210T212955Z
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SUMMARY:CSE Colloquium - Query Optimization: How to design a Meta-Algorithm that designs Algorithms?
DESCRIPTION:Presenter: Mahmoud Abo Khamis\, RelationalAI \nAbstract: \nDatabase systems have evolved from simple bookkeeping tools to comprehensive data analytics platforms capable of learning from the data and making business decisions. As a result\, database queries expanded in their expressive power and applications to include tensor computations\, constraint satisfaction problems\, graph analytics\, scientific computing\, SAT solving\, among others. This puts a lot of pressure on modern query optimizers to rise up to the occasion and produce efficient query plans for a wide variety of very complex queries that describe problems in different domains. The ultimate goal of query optimization is for the query optimizer to become a “meta-algorithm” where you can feed in any problem definition and get back an efficient algorithm for this particular problem. \nIn this talk\, we describe two related frameworks for query optimization that aim to take us one step in the direction of the above goal. The first framework is based on information theory. It uses information theory to get provably accurate cost estimates for query plans and to find the best query plan. Among other applications\, this framework currently achieves the best known complexity for graph pattern matching problems\, thus subsuming and generalizing known results in this area\, where\, for decades\, algorithms used to be designed by hand for specific graph patterns. The second framework is based on algebra. It uses algebraic abstractions to unify and generalize algorithms across different domains\, in the same way template programming allows for reusing code across different applications. \nBio: \nMahmoud Abo Khamis is a Senior Computer Scientist at RelationalAI\, where he has worked since 2017. He received his Ph.D. in Computer Science and Engineering from the State University of New York at Buffalo in 2016. Prior to joining RelationalAI\, he was a Senior Database Engineer at Infor from 2015 to 2017. His research interests include database systems and theory\, in-database machine learning\, query optimization and evaluation\, information theory\, and beyond worst-case analysis. His work has been recognized with two Test-of-Time Awards at ACM PODS 2025 and 2026\, three Best Paper Awards at ACM SIGMOD 2025 and ACM PODS 2022 and 2016\, three ACM SIGMOD Research Highlight Awards\, and the 2016 Best CSE Dissertation Award from SUNY Buffalo. His work has also received multiple invitations to the Journal of the ACM\, ACM STOC\, and ACM TODS. He is on the Editorial Board of ACM TODS\, and serves on the program committees of ACM PODS\, ICDT\, and ICALP among others. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (*Refreshments such as coffee\, tea\, pastries\, and fresh fruit will be provided in-person.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-query-optimization-how-to-design-a-meta-algorithm-that-designs-algorithms/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260212T163000
DTEND;TZID=America/Los_Angeles:20260212T173000
DTSTAMP:20260424T204108
CREATED:20260203T172912Z
LAST-MODIFIED:20260203T173017Z
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SUMMARY:Sambamurthy\, A. (AM) - Lazy Diffusion: Resolving Spectral Collapse in Generative Models for Turbulence
DESCRIPTION:Diffusion-based generative models offer a principled framework for probabilistic forecasting\, but we show they suffer from a fundamental spectral collapse when applied to turbulent flows. A Fourier-space analysis of the forward SDE reveals that the mode-wise signal-to-noise ratio decays monotonically in wavenumber for power-law spectra\, rendering high-wavenumber content indistinguishable from noise. We reinterpret the noise schedule as a spectral regularizer and introduce power-law schedules that preserve fine-scale structure deeper into diffusion time. We further propose Lazy Diffusion\, a one-step distillation method that leverages the learned score geometry to bypass long reverse trajectories and prevent high-wavenumber degradation. Applied to high-Reynolds-number 2D Kolmogorov turbulence and ocean reanalysis data\, these methods resolve spectral collapse and enable stable long-horizon autoregressive emulation. \nEvent Host: Anish Sambamurthy\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Ashesh Chattopadhyay \nZoom- https://ucsc.zoom.us/j/5144530307?pwd=TllaWnNDc01tcVNpa1NNeVVIMnp5QT09 \nPasscode- 55555
URL:https://events.ucsc.edu/event/sambamurthy-a-am-lazy-diffusion-resolving-spectral-collapse-in-generative-models-for-turbulence/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260211T110000
DTEND;TZID=America/Los_Angeles:20260211T121500
DTSTAMP:20260424T204108
CREATED:20260105T205936Z
LAST-MODIFIED:20260105T205936Z
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SUMMARY:CSE Colloquium: Incentivized Alignment for Strategic Agents (Human and Otherwise)
DESCRIPTION:Presenter: Grant Schoenebeck\, University of Michigan \nAbstract: Advances in machine learning enable new forms of human-AI collaboration\, but collaborative settings typically involve agents with divergent objectives and private information. This will become increasingly critical in the emerging world of agentic AI\, where ML-powered agents act on behalf of individuals or institutions with conflicting goals. I use the term incentivized alignment to describe the approach of combining both machine learning and incentive design to achieve alignment of system outcomes despite misaligned agents. This talk presents two case studies of incentivized alignment showing how machine learning can make mechanism design scalable and practical\, and how mechanism design can make machine learning strategically robust. First\, I examine the use of LLMs as judges for rating subjective responses. While LLMs perform well on existing datasets\, they are highly susceptible to manipulation. I propose adapting peer-prediction mechanisms to create strategically-robust scoring mechanisms that incentivize honest reporting. Beyond ensuring high-quality inputs to AI systems\, these mechanisms can potentially eliminate reward hacking in ML training pipelines. Second\, I consider collective decision-making where agents hold different objectives and private information. The goal is to design mechanisms that incentivize strategic agents to select outcomes that would be optimal under full information sharing\, according to certain criteria. Both case studies demonstrate solutions for incentivized alignment in multi-agent systems employing the combination of incentive design and machine learning\, a theme likely to be central to the future of collaborative AI. \nBio: Grant Schoenebeck is an associate professor at the University of Michigan in the School of Information. His work has recently focused on developing and analyzing systems for eliciting and aggregating information from a diverse group of agents with varying information\, interests\, and abilities by combining ideas from machine learning and economics (e.g. game theory\, mechanism design\, and information design). More generally\, his recent work has been about incentives and (machine) learning in a variety of contexts. His research is supported by multiple NSF grants including a CAREER award and spans publications in top venues including NeurIPS\, ICLR\, EC\, WINE\, the Web Conference\, STOC\, and FOCS. His former PhD students and postdocs now hold tenure-track positions at the University of Illinois Urbana-Champaign\, Peking University\, George Mason University\, and Shanghai Jiao Tong University. He recently served as Program Committee Co-chair for WINE\, Theory Track Co-chair for EC\, and Economics and Computation Track co-chair at the Web Conference. Grant received his PhD at UC Berkeley\, studied theology at Oxford University\, and received his BA in mathematics and computer science from Harvard. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 \n*Light refreshments such as coffee\, pastries\, and fruit will be available. \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-incentivized-alignment-for-strategic-agents-human-and-otherwise/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T123000
DTEND;TZID=America/Los_Angeles:20260209T133000
DTSTAMP:20260424T204108
CREATED:20260126T235923Z
LAST-MODIFIED:20260204T204343Z
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SUMMARY:CM Seminar - “The ‘Social’ Side of Social Virtual Reality”
DESCRIPTION:Presented by: Bree McEwan \nDescription: One of the potential use cases of virtual reality is to create spaces where humans can interact with each other or virtual agents across distances. However\, despite many of the technological challenges of social VR being solved\, social VR does not see poised for widespread adoption. Multi-user social VR needs to be perceived not just as a technology to be solved but an emerging communication channel. Social science approaches\, particularly from communication scholars\, are needed to truly understand the way that humans engage with VR and each other in these new environments. McEwan’s talk will outline a program of research using qualitative and quantitative approaches to understand communication processes\, effects\, and user perceptions of VR design to deepen our understanding of how people engage with environments and each other in social VR. \nBio: Bree McEwan is a Professor in the Institute of Communication\, Culture\, Information and Technology\, an associate director of the Data Sciences Institute\, and a faculty affiliate of the Schwartz Reisman Institute for Technology and Society at the University of Toronto. She is a co- organizer and founder of the Questioning Reality conference\, a social VR research incubator. McEwan authored Navigating New Media Networks and co-authored Interpersonal Encounters. She directs the McEwan Mediated Communication Lab which researches the intersection of technology and social interaction. McEwan has published on relational maintenance on social network sites\, perceived social affordances of communication channels\, linguistic patterns in online communities\, and the diffusion of information through social media. In addition\, McEwan has metascience interests focused on transparency and replication in the social sciences. Current studies of the McMC Lab focus on affordances of social virtual environments\, cognition and heuristics related to learning in VR spaces\, and nonverbal communication patterns of avatars and agents. \nHosted by: Professor Katherine Isbister \nWhen: Monday\, February 9\, 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/91469785121?pwd=F0jplMgh4eTjy6qNZI0lEhlljs0XhG.1 \nMeeting ID: 914 6978 5121\nPasscode: 183098
URL:https://events.ucsc.edu/event/cm-seminar-the-social-side-of-social-virtual-reality/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T104000
DTEND;TZID=America/Los_Angeles:20260209T234500
DTSTAMP:20260424T204108
CREATED:20260202T233432Z
LAST-MODIFIED:20260202T233432Z
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SUMMARY:ECE Seminar: Integrated Micro- and Nanosystems for Biosensing\, Neural Therapy\, and Nanotoxicity
DESCRIPTION:Presenter: Dr. Ke Du\, Associate Professor of Chemical and Environmental Engineering\, University of California\, Riverside \nDescription: Miniaturized micro- and nanofluidic systems\, integrated with biochemistry\, microscopy\, nanomaterials\, and computer vision algorithms\, provide powerful platforms for diverse biomedical applications\, including molecular diagnostics\, biophysics\, and optogenetics. In this presentation\, we introduce a pneumatically controlled nano-sieve device with nanolithography-defined microstructures designed to enhance target capture efficiency in bodily fluids. This system incorporates sheath flow configurations\, surface-enhanced Raman probes\, and CRISPR reactions for the sensitive and multiplexed detection of drug-resistant bacteria in nanoconfined environments. We also highlight our recent advancements in implantable devices for adeno-associated virus (AAV) delivery and the treatment of neurological disorders in mouse models. These devices\, fabricated via high-resolution 3D printing\, utilize total internal reflection at the liquid–air–microstructure interface to efficiently stimulate neurons. Finally\, we integrate experimental approaches with molecular dynamics simulations to study the interactions between arbitrary nanoparticles and living cells—advancing our understanding of nanotoxicity and guiding the design of next-generation drug delivery systems. \nBio: Dr. Ke Du is an Associate Professor of Chemical and Environmental Engineering at the University of California\, Riverside. He established his independent research lab in 2018 following postdoctoral training with Richard Mathies at the University of California\, Berkeley\, and Holger Schmidt at the University of California\, Santa Cruz. His research team focuses on molecular diagnostics for infectious diseases such as sepsis\, in vivo bioimaging\, and nanotoxicology. Dr. Du has received numerous honors\, including the EIPBN Inaugural Early Career Award (2024) and the NIH Maximizing Investigators’ Research Award (2021). He was recognized as an Emerging Investigator by Lab on a Chip (2024) and Nanoscale (2025)\, and named a Global Rising Star in Sensing by ACS Sensors. His research is supported by federal agencies and industry partners\, including NIH NIGMS\, NIH NIAID\, NSF CBET\, NSF CMMI\, USDA\, DOE\, the Burroughs Wellcome Fund\, Mammoth Biosciences\, and Biological Mimetics. Beyond his research activities\, Dr. Du serves as an Early Career Editorial Advisory Board member for Biomicrofluidics (AIP Publishing) and Sensors and Actuators Reports (Elsevier). \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-integrated-micro-and-nanosystems-for-biosensing-neural-therapy-and-nanotoxicity/
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:20260206T110000
DTEND;TZID=America/Los_Angeles:20260206T120000
DTSTAMP:20260424T204108
CREATED:20260127T193801Z
LAST-MODIFIED:20260127T193801Z
UID:10009119-1770375600-1770379200@events.ucsc.edu
SUMMARY:Johnstone\, J. (AM) - The Effects of Asymmetry on Overshooting and Magnetic Pumping from Compressible Convection Zones
DESCRIPTION:We present a comprehensive numerical investigation examining how vertical asymmetry in compressible convection affects overshooting and the transport of large-scale magnetic fields from convective to stably stratified regions. Using three-dimensional direct numerical simulations\, we systematically vary the superadiabaticity and stratification of a convective layer to control the vertical asymmetry of the flow and analyze its influence on overshooting depth and magnetic pumping efficiency. We extend previous work by Tobias et al. (2001) and draw guidance from the asymmetry regimes identified by John & Schumacher (2023)\, investigating whether similar asymmetric convecting regimes emerge in our overshooting model that incorporates a stably stratified region below. We find that vertical asymmetry increases significantly with stratification at a moderate\, fixed Rayleigh number\, while superadiabaticity contributes primarily through enhanced downflow velocities\, with both combined leading to increasing overshooting depths reaching approximately 0.46 − 0.7 pressure scale heights. Magnetic pumping efficiency initially increases with stratification but unexpectedly decreases at higher stratification\, despite increasing overshooting depths. We find that this behavior arises from the increasing thermal and magnetic diffusivities that result from increasing stratification at fixed Ra. When instead either holding these diffusivities constant or increasing Ra sufficiently\, we find that then both overshooting and magnetic pumping depths both decrease with increasing stratification. This behavior is explained by a change of dynamical state from one of laminar downflows to one of turbulent downflowing plumes leading to a high degree of turbulent mixing and entrainment. We thus find two distinct regimes that might be described as a microscopically diffusive regime and a turbulently diffusive one. These results suggest that\, in the highly turbulent regime expected in the Sun\, magnetic pumping efficiency may decrease with increasing stratification due to enhanced turbulent entrainment\, with important implications for solar dynamo theory and the transport of large-scale magnetic fields in the solar interior. \n  \nEvent Host: Jason Johnstone\, Ph.D. Student\, Applied Mathematics \nAdvisor: Nic Brummell \nZoom- https://ucsc.zoom.us/j/5428987373?pwd=JSmNz3ZZby5ZnVBYbSoakjjQb2qQj6.1&omn=98571815542 \nPasscode- 778899
URL:https://events.ucsc.edu/event/johnstone-j-am-the-effects-of-asymmetry-on-overshooting-and-magnetic-pumping-from-compressible-convection-zones/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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GEO:37.0009723;-122.0632371
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