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SUMMARY:2026 Right Livelihood International Conference
DESCRIPTION:The Right Livelihood International Conference is a five-week global conference exploring how education can strengthen democracy\, collective intelligence\, and just futures. Bringing together Right Livelihood Laureates\, students\, faculty\, and community partners across continents\, the conference combines asynchronous learning with participatory dialogue and collaborative action. Rather than advocating specific outcomes\, the conference positions education as a democratic practice and the Right Livelihood College as a steward of dialogue\, student voice\, and long-term institutional learning. \nRegistration is free and open to the public. Sign up to receive conference updates\, session links\, and participation opportunities.
URL:https://events.ucsc.edu/event/2026-right-livelihood-international-conference/
LOCATION:
CATEGORIES:Film Screening,Lectures & Presentations,Meetings & Conferences,Ph.D. Presentations,Seminars,Social Gathering,Training,Undergraduate,Workshop
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DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260417T122539
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SUMMARY:AM Seminar: Variational Inference and Density Estimation with Non-Negative Tensor Train
DESCRIPTION:Presenter: Dr. Xun Tang\, Stanford University \nDescription: This talk covers an efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format. The two settings we consider are variational inference and density estimation\, whereby one has access to either the unnormalized analytic formula of the distribution or the samples generated from the distribution. In particular\, the compression is done through a two-stage approach. In the first stage\, we use existing subroutines to encode the distribution function in a tensor train format. In the second stage\, we use an NTT ansatz to fit the obtained tensor train. For the NTT fitting procedure\, we use a log barrier term to ensure the positivity of each tensor component\, and then utilize a second-order alternating minimization scheme to accelerate convergence. In practice\, we observe that the proposed NTT fitting procedure exhibits drastically faster convergence than an alternative multiplicative update method that has been previously proposed. Through challenging numerical experiments\, we show that our approach can accurately compress target distribution functions. \nBio: Xun Tang is a postdoc in Stanford University\, department of mathematics\, hosted by Prof. Lexing Ying. Xun works on tensor network methods for scientific computing and data science\, and Xun also works on optimal transport algorithms. Xun will join HKUST department of mathematics in August 2026 as an incoming assistant professor. \nHosted by: Applied Mathematics Department
URL:https://events.ucsc.edu/event/am-seminar-variational-inference-and-density-estimation-with-non-negative-tensor-train/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260417T122539
CREATED:20260331T181211Z
LAST-MODIFIED:20260331T181211Z
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SUMMARY:Statistics Seminar: Hierarchical Clustering with Confidence
DESCRIPTION:Presenter: Snigdha Panigrahi\, Associate Professor\, Department of Statistics\, University of Michigan \nDescription:Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However\, its greedy nature makes it highly sensitive to small perturbations in the data\, often producing different clustering results and making it difficult to separate genuine structure from spurious patterns. In this talk\, I will show how randomizing hierarchical clustering can be useful not just for measuring stability but also for designing valid hypothesis testing procedures based on the clustering results. We propose a simple randomization scheme to construct valid p-values at each node of a hierarchical clustering dendrogram\, quantifying evidence against greedy merges while controlling the Type I error rate. Our method applies to any linkage without case-specific derivations\, is substantially more powerful than existing selective inference approaches\, and provides an estimate of the number of clusters with a probabilistic guarantee on overestimation. \nBio:Snigdha Panigrahi is an Associate Professor of Statistics at the University of Michigan\, where she also holds a courtesy appointment in the Department of Biostatistics. She received her PhD in Statistics from Stanford University in 2018 and has been a faculty member at Michigan since then. Her research focuses on converting purely predictive machine learning algorithms into principled inferential methods. She is an elected member of the International Statistical Institute\, and her work has been recognized with an NSF CAREER Award and the Bernoulli New Researcher’s Award. Her editorial service\, past and present\, includes Journal of Computational and Graphical Statistics\, Bernoulli\, and Journal of the Royal Statistical Society: Series B. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-hierarchical-clustering-with-confidence/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260422T110000
DTEND;TZID=America/Los_Angeles:20260422T121500
DTSTAMP:20260417T122539
CREATED:20260331T171056Z
LAST-MODIFIED:20260401T165930Z
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SUMMARY:CSE Colloquium - Robust Machine Learning for Biomedical Data: Efficiency\, Reliability\, and Generalizability
DESCRIPTION:Presenter\nChenyu You\, Stony Brook University \nAbstract\nIn the rapidly growing area of machine learning\, there is profound promise in crafting intelligent\, data-driven methods for diverse real-world applications. Yet\, in safety-critical domains like healthcare\, some fundamental challenges remain: (1) The insufficiency of raw biomedical data emphasizes the need for data-efficient and robust learning approaches. (2) The imperative of safety and stability necessitates a cohesive framework that unifies learning with theoretical guarantees. (3) The inherent heterogeneity and distribution shifts in real-world clinical data call for robust and generalizable learning methods. To address these challenges\, there are several major directions I have explored: (i) (Robust) Machine Learning for Imperfect Medical Data: The development of machine learning models\, particularly in the context of label scarcity\, increasingly necessitates the collection of substantial annotated medical data. Moreover\, medical data often display a long-tailed class distribution\, which consequently results in notable imbalance issues. To this end\, there are several growing interests in training machine learning models jointly across imbalanced class distributions and limited annotations. I have developed novel\, efficient\, statistically consistent algorithms to improve empirical performance for biomedical image analysis. (ii) Learning with Theoretical Guarantees: As machine learning methods have become ubiquitous in clinical decision-making\, their reliability and interpretability have become important. This is particularly crucial in the field of biomedical image analysis\, where decision outcomes can have profound implications. I have developed novel machine learning algorithms that enable provably accurate anatomical modeling with theoretical guarantees. (iii) Generalize across Diverse Biomedical Data: The development of medical foundation models often requires massive and diverse biomedical data. To this end\, I have developed various foundation models for biomedical imaging data and explored novel applications of these models. I have also developed novel medical AI Agents that lead to the scalable and accurate predictive modeling\, particularly for distribution shift problems. \nSpeaker Bio\nChenyu You is an Assistant Professor in the Department of Applied Mathematics & Statistics and Department of Computer Science at Stony Brook University. He is also the core faculty member of the CVLab\, AI institute\, and affiliated with the Institute for Advanced Computational Science. His research focuses on both fundamental and applied problems in computer vision and machine learning\, often with a focus on generalization\, and making machine learning more reliable. Our applied research includes applications to healthcare\, biomedical imaging\, and cognitive neuroscience. He received his Ph.D. in 2024 from Yale University under the advisement of James S. Duncan\, his M.S. in 2019 from Stanford University under the advisement of Daniel Rubin\, and his B.S. in 2017 from Rensselaer Polytechnic Institute under the advisement of Ge Wang\, all in electrical engineering. He has also spent wonderful time at Facebook AI Research (FAIR)\, as well as Google Research. He serves on the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI)\, and the SUNY AI Symposium Planning Committee\, and as associate editors for IEEE Transactions on Medical Imaging\, Medical Image Analysis\, IEEE Transactions on Neural Networks and Learning Systems\, Pattern Recognition\, and Transactions on Machine Learning Research. He has received AAAI’26 New Faculty Highlights\, CPAL’26 Rising Stars Award\, Tinker Research Grant Award\, Lambda Research Grant Award\, ICML’25 Oral Presentation Award\, EMBC’25 Top Paper Award\, MICCAI’25 NIH Registration Grant Award\, IEEE TMI’25 Distinguished Associate Editor Certificate of Excellence Award\, and Yale George P. O’Leary Graduate Fellowship\, and has been ranked as the World’s Top 2% most-cited scientists by Stanford University since 2024\, is a member of the Sigma Xi scientific research society\, and received the Excellence in Teaching Award for Spring and Fall 2025. For more information\, please check his website: https://chenyuyou.me/. \nHosted by: Professor Yuyin Zhou \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-robust-machine-learning-for-biomedical-data-efficiency-reliability-and-generalizability/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260427T123000
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SUMMARY:CM Seminar: Edward Wang\, "Inventing a New Blood Pressure Monitor"
DESCRIPTION:Presented by: Edward Wang \nDescription: “What does it actually look like to invent something? In this talk\, I trace the decade-long journey of turning a smartphone into a blood pressure monitor\, from Seismo\, which used smartphone accelerometers to measure pulse transit time\, to BPClip\, a dollar clip that brought calibration-free oscillometry to the fingertip\, to VibroBP\, which eliminated the attachment entirely using the phone’s vibration motor. Each project was born from the limitations of the last. And each time we thought we’d solved the problem\, new layers of unknowns appeared around usability\, manufacturing\, and FDA classification. This is a talk about what inventing looks like when you zoom in past the papers and patents. Less about creating something new\, and more about finding the unknowns between a need and its solution\, and creatively working through them\, one by one.” \nBio: Dr. Edward J. Wang is the Jacobs Faculty Chair in Entrepreneurship Associate Professor of Design and Electrical & Computer Engineering at UC San Diego\, where he directs the Digital Health Technologies Lab. His research explores practical solutions to address real-world medical needs drawn from collaborations with clinicians and world health organizations\, but solved using new and creative insights that leverage state-of-the-art applied machine learning\, embedded systems\, and mobile sensors. He has been named an NAI Senior Member\, NIH Trailblazer\, Norman Design Laureate\, and Google Research Scholar. He publishes in premier computer science and health science venues including ACM IMWUT\, CHI\, UIST\, Nature Publishing\, Frontiers in Digital Health\, and JMIR\, having been awarded 9 best paper awards. He actively engages in the translation of research through faculty entrepreneurship. He earned his Ph.D. from the University of Washington and his B.S. from Harvey Mudd College. \nHosted by: Professor Christina Chung \nWhen: Monday\, April 27\, 2026 from 12:30PM to 1:30PM \nLocation:  \nIN-PERSON @  SVC 3212. \nViewing room @ UCSC Main Campus\, E2-280. \nLUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend. \nZoom info: \nhttps://ucsc.zoom.us/j/91516487260?pwd=6qaylO1FY0XjYHIrFnxJqCikmypxam.1\nMeeting ID: 915 1648 7260\nPasscode: 086900 \n 
URL:https://events.ucsc.edu/event/cm-seminar-edward-wang-inventing-a-new-blood-pressure-monitor/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20260427T160000
DTEND;TZID=America/Los_Angeles:20260427T170000
DTSTAMP:20260417T122539
CREATED:20260408T191555Z
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SUMMARY:Statistics Seminar: Active Learning for Fair and Stable Allocations
DESCRIPTION:Presenter: Riddhiman Bhattacharya\, Postdoc\, UCSC \nDescription: We propose an active learning approach for dynamic fair resource allocation problems. In contrast to prior work that assumes full feedback from all agents on their allocations\, we focus on scenarios where feedback is available only from a carefully select subset of agents at each epoch of the online resource allocation process. Despite this limitation\, our algorithms achieve sub-linear regret in the number of time-periods for multiple fairness metrics commonly used in resource allocation problems and stability constraints inherent to matching mechanisms. The core innovation of our approach lies in the adaptive identification of the most informative feedback through dueling upper and lower confidence bounds. This strategy enables efficient decision-making with limited feedback\, achieving favorable outcomes across various problem classes. \nAbout the speaker: I am Riddhiman Bhattacharya\, currently a postdoc at UCSC\, Statistics Department\, working with Justin (Sangwon Hyun). I have previously been a postdoc at Purdue and have obtained my PhD from the University of Minnesota in Statistics. I am interested in methodological development in statistics with varied applications including oceanography\, biology and economics. I am also interested in theoretical development of statistics particularly in the fields of Markov Chain Monte Carlo\, Optimization and Fast Sampling.
URL:https://events.ucsc.edu/event/statistics-seminar-active-learning-for-fair-and-stable-allocations/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260427T160000
DTEND;TZID=America/Los_Angeles:20260427T170000
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SUMMARY:AM Seminar: Machine Learning in Molecular Simulations: From Free Energy to Vibrational Spectroscopy
DESCRIPTION:Presenter: Marcos Calegari Andrade\, Assistant Professor\, Chemistry and Biochemistry\, UC Santa Cruz \nDescription: In this talk\, I will demonstrate how neural networks can represent the high-dimensional potential energy surfaces of many-body systems. By achieving the accuracy of first-principles quantum calculations at a fraction of the computational cost\, these models enable atomistic simulations of condensed matter at unprecedented scales. I will discuss how this approach provides a detailed lens into chemical reaction dynamics under experimentally relevant conditions and facilitates the direct calculation of observables\, such as vibrational spectra\, effectively bridging the gap between theoretical simulation and experimental observation. \nAbout the speaker: Marcos Calegari Andrade is an Assistant Professor in the Department of Chemistry and Biochemistry at the University of California\, Santa Cruz. He earned his PhD from Princeton University\, where he developed machine learning models to simulate the chemistry and vibrational spectroscopy of condensed-phase systems. He later joined the Quantum Simulations Group at Lawrence Livermore National Laboratory\, applying deep neural network models to fundamental challenges in climate and energy security. His current research at UCSC focuses on the application of machine learning to molecular simulations\, with a particular emphasis on chemical reaction mechanisms\, vibrational spectroscopy\, and the development of automated simulation frameworks. \nThis seminar is hosted by Applied Mathematics
URL:https://events.ucsc.edu/event/am-seminar-machine-learning-in-molecular-simulations/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260429T110000
DTEND;TZID=America/Los_Angeles:20260429T121500
DTSTAMP:20260417T122539
CREATED:20260402T185047Z
LAST-MODIFIED:20260402T185047Z
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SUMMARY:CSE Colloquium - Towards Safe and Resilient Large-scale Distributed Programming
DESCRIPTION:Presenter: Philipp Haller\, KTH Royal Institute of Technology \nAbstract: \nDistributed programming is notoriously difficult. Not only are distributed systems concurrent\, they pose additional challenges including data consistency and fault tolerance. At the same time\, the share of software systems that are necessarily distributed systems is growing rapidly. As a result\, too many software developers are asked to become distributed systems experts. Thus\, tools and techniques for ensuring the correctness of distributed systems are urgently needed in order to leave this unsustainable trajectory. This talk presents research results towards the design and implementation of programming systems that support emerging applications and workloads; provide reliability and trust; and embrace simplicity and accessibility. Concretely\, the presented work focuses on two directions. \nThe first direction explores a distributed programming model that provides consistency while enabling high availability for workloads operating on join-semilattices without sacrificing partition tolerance. We propose a new consistency protocol\, called observable atomic consistency protocol (OACP)\, which leverages on-demand coordination to support both coordination-free operations as well as totally-ordered operations on replicated data types. We present a formal\, mechanized model of OACP in rewriting logic and verify key correctness properties using the model checking tool Maude. Furthermore\, we present the evaluation of a prototype implementation of OACP based on Akka\, a widely-used actor-based middleware. The second direction explores a programming system that aims to reconcile the scalability and fault tolerance of stream processing systems with the flexibility of the actor concurrency model. The programming system ensures a failure-transparency property\, effectively masking failures through transparent recovery. Our work is the first to formalize failure transparency using a small-step operational semantics\, and to provide proofs of failure transparency for stateful dataflow streaming and a fault-tolerant actor-based programming model. \nBio: \nPhilipp Haller is an Associate Professor in the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology in Stockholm\, Sweden. His main research interests are in the design and implementation of programming languages\, type systems\, concurrency\, and distributed programming. He was part of the team that received the 2019 ACM SIGPLAN Programming Languages Software Award for the development of the Scala programming language. Prior to KTH\, he was an early employee at Akka (previously Lightbend\, Inc.)\, a start-up company developing and supporting Scala as well as frameworks for large-scale distributed programming. Prior to Akka\, he was a post-doctoral fellow at Stanford University\, USA\, and at EPFL\, Switzerland. In 2010 he received his PhD in computer science from EPFL\, including a nomination for the 2010 EPFL Doctorate Award. In 2006 he received his Dipl.-Inform. degree from Karlsruhe Institute of Technology (previously University of Karlsruhe)\, Germany. \nHosted by: Professor Mohsen Lesani \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-towards-safe-and-resilient-large-scale-distributed-programming/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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