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SUMMARY:The Critical Role of Cyber Infrastructure in City Innovation and Beyond
DESCRIPTION:Talk Abstract \n\nCities\, humanity’s greatest inventions\, offer vast opportunities for innovation in science and technology. The increasing availability of big data paints a promising future for our cities. Over the past decade\, my work has focused on applying AI to address real-world city challenges. Recent collaborations with city practitioners have deepened my understanding of these complexities and refined my vision for achieving city intelligence. \nIn this talk\, I will present my work on advanced AI techniques for city transportation problems\, e.g.\, reinforcement learning for traffic signal control. I will then expand on this to discuss the resource-centric concept of city intelligence\, using real-world practices to showcase its practical applications. Finally\, I will emphasize the urgent need for new cyber infrastructure\, vital not only for city innovations but for all scientific disciplines driven by big data and intensive computing. \n\nSpeaker Bio\nDr. Zhenhui (Jessie) Li currently serves as the chief scientist at the Yunqi Academy of Engineering\, a non-profit institution situated in Hangzhou\, China. Prior to this role\, she held a tenured associate professor position at Pennsylvania State University. She earned her doctoral degree in Computer Science from the University of Illinois at Urbana-Champaign. Her research has been primarily devoted to advancing computing technologies to unlock the potential of data for cross-disciplinary research\, with a specific emphasis on city applications. For further information\, you can visit her website at (https://jessielzh.com/).
URL:https://datascience.ucsd.edu/event/the-critical-role-of-cyber-infrastructure-in-city-innovation-and-beyond/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202
CATEGORIES:Webinar
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DTSTART;TZID=America/Los_Angeles:20240403T140000
DTEND;TZID=America/Los_Angeles:20240403T153000
DTSTAMP:20260530T220252
CREATED:20240326T221709Z
LAST-MODIFIED:20240329T001345Z
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SUMMARY:"Contextualized learning for adaptive yet persistent AI in biomedicine" | Ben Lengerich
DESCRIPTION:Abstract: “In biomedical data analysis\, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution\, context-specific insights by integrating information across datasets\, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim\, this presentation will examine “contextualized learning”\, a meta-learning paradigm which learns relationships between dataset context and statistical parameters. Using contextualized network inference as an illustrative example\, I will show how we can estimate context-specific graphical models\, offering insights such as personalized gene expression analysis for SOTA cancer subtyping. The talk will also discuss trends towards “contextualized understanding”\, bridging statistical and foundation models to standardize interpretability. The primary aim is to illustrate how contextualized learning and understanding contribute to creating learning systems that are both adaptive and persistent\, facilitating cross-context information sharing and detailed analysis.” \nBio: “Ben Lengerich is a Postdoctoral Associate and Alana Fellow at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Broad Institute of MIT and Harvard\, where he is advised by Manolis Kellis. His research in machine learning and computational biology emphasizes the use of context-adaptive models to understand complex diseases and advance precision medicine. Through his work\, Ben aims to bridge the gap between data-driven insights and actionable medical interventions. He holds a PhD in Computer Science and MS in Machine Learning from Carnegie Mellon University\, where he was advised by Eric Xing. His work has been recognized with spotlight presentations at conferences including NeurIPS\, ISMB\, AMIA\, and SMFM\, financial support from the Alana Foundation\, selection as a “”Rising Star in Data Science” by the University of Chicago and UC San Diego\, and “”Next Generation in Biomedicine”” by the Broad Institute.”
URL:https://datascience.ucsd.edu/event/special-seminar-ben-lengerich/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20230609T150000
DTEND;TZID=America/Los_Angeles:20230609T160000
DTSTAMP:20260530T220252
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SUMMARY:Deep Latent Variable Models for Compression and Natural Science | Stephan Mandt
DESCRIPTION:Abstract: Latent variable models have been an integral part of probabilistic machine learning\, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression\, and the potential of these models for advancing natural science. This talk will explore these topics. I will begin by showcasing connections between variational methods and the theory and practice of neural data compression. On the applied side\, variational methods lead to machine-learned compressors of data such as images and videos and offer principled techniques for enhancing their compression performance\, as well as reducing their decoding complexity. On the theory side\, variational methods also provide scalable bounds on the fundamental compressibility of real-world data\, such as images and particle physics data. Lastly\, I will also delve into climate science projects\, where a combination of deep latent variable modeling and vector quantization enables assessing distribution shifts induced by varying climate models and the effects of global warming. \nBio: Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California\, Irvine. From 2016 until 2018\, he was a Senior Researcher and Head of the statistical machine learning group at Disney Research in Pittsburgh and Los Angeles. He held previous postdoctoral positions at Columbia University and Princeton University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne in Germany\, where he received the National Merit Scholarship. He received the NSF CAREER Award\, a Kavli Fellowship of the U.S. National Academy of Sciences\, the German Research Foundation’s Mercator Fellowship\, and the UCI ICS Mid-Career Excellence in Research Award. He is a member of the ELLIS Society and a former visiting researcher at Google Brain. Stephan will serve as Program Chair of the AISTATS 2024 conference\, currently serves as an Action Editor for JMLR and TMLR\, and frequently serves as Area Chair for NeurIPS\, ICML\, AAAI\, and ICLR.
URL:https://datascience.ucsd.edu/event/deep-latent-variable-models-for-compression-and-natural-science-stephan-mandt/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202
CATEGORIES:Colloquium
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