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SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/1/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260309T104000
DTEND;TZID=America/Los_Angeles:20260309T114500
DTSTAMP:20260406T115857
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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DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260406T115857
CREATED:20260217T230434Z
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SUMMARY:AM Seminar: Solution Discovery in Fluids with High Precision Using Neural Networks
DESCRIPTION:Presenter: Ching-Yao Lai\, Assistant Professor\, Stanford University \nDescription: I will discuss examples utilizing neural networks (NNs) to find solutions to partial differential equations (PDEs) that facilitate new discoveries. Despite being deemed universal function approximators\, neural networks\, in practice\, struggle to fit functions with sufficient accuracy for rigorous analysis. Here\, we developed multi-stage neural networks (Wang and Lai\, J. Comput. Phys. 2024) that can reduce the prediction error to nearly the machine precision of double-precision floating points within a finite number of iterations. We use accurate NNs to tackle the challenge of searching for singularities in fluid equations (Wang-Lai-Gómez-Serrano-Buckmaster\, Phys. Rev. Lett. 2023). Unstable singularities\, especially in dimensions greater than one\, are exceptionally elusive. With NNs we demonstrate the first discovery of smooth unstable self-similar singularities to unforced incompressible fluid equations (Wang et al.\, arXiv:2509.14185). The example illustrates how deep learning can be used to discover new and highly accurate numerical solutions to PDEs. \nBio: Ching-Yao Lai (Yao) is an Assistant Professor in the Department of Geophysics and an Affiliated Faculty of the Institute for Computational and Mathematical Engineering (ICME) at Stanford. Before joining Stanford\, she was an Assistant Professor at Princeton University. She received an undergraduate degree (2013) in Physics from National Taiwan University and a PhD (2018) in Mechanical and Aerospace Engineering from Princeton University. She completed her postdoctoral research at Columbia University where she received the Lamont Postdoctoral Fellowship. Her current research focuses on enhancing the representation of machine-learning models to tackle multiscale problems. She was the recipient of the 2023 Google Research Scholar Award\, the 2024 Sloan Research Fellowship\, and the 2025 NSF CAREER Award. \nHosted by: Applied Mathematics
URL:https://events.ucsc.edu/event/am-seminar-solution-discovery-in-fluids-with-high-precision-using-neural-networks/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260406T115857
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
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SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/2/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260311T110000
DTEND;TZID=America/Los_Angeles:20260311T121500
DTSTAMP:20260406T115857
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260312T114000
DTEND;TZID=America/Los_Angeles:20260312T131500
DTSTAMP:20260406T115857
CREATED:20260303T000204Z
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SUMMARY:BME 280B Seminar: Modulating Insulin Receptor Through New Ligands
DESCRIPTION:Presenter: Danny Chou\, Associate Professor of Pediatrics\, Stanford University \nDescription: Since its discovery in 1921\, insulin has been at the forefront of scientific breakthroughs. From its amino acid sequencing to the revelation of its three‐dimensional structure\, the progress in insulin research has spurred significant therapeutic breakthroughs. In recent years\, protein engineering has introduced innovative chemical and enzymatic methods for insulin modification\, fostering the development of therapeutics with tailored pharmacological profiles. In this seminar\, I will highlight the use of new ligands to modulate insulin receptors and discuss how they continue to shape the future trajectory of insulin research. \nBio: Danny Chou is an Associate Professor of Pediatrics and by courtesy\, of Chemical & Systems Biology at Stanford University. He is an affinity group leader at Stanford Diabetes Research Center. His research interests lie in the intersection of peptide therapeutics and metabolic diseases. He started his independent career as an assistant professor of biochemistry at University of Utah in 2014. He moved his lab to Stanford University in 2020 and continued their pursuit of using peptide and protein chemistry to develop therapeutics to address unmet needs.  \nHosted by: Professor Andy Yeh\, BME Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-modulating-insulin-receptor-through-new-ligands/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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