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  • Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective

    Hybrid Event

    Presenter: Jiaqi Li, William H. Kruskal Instructor, University of Chicago Description:Modern machine learning (ML) algorithms achieve remarkable empirical success, yet providing rigorous statistical guarantees remains a major challenge, particularly in distributional theory and online inference methods. In this talk, we will introduce a novel framework to provide mathematical foundations for ML by bringing powerful tools […]

  • AM Seminar: Are Graph Learning Methods Actually Learning?

    Presenter: Seshadhri Comandur, Professor of Computer Science, UCSC Description: There has been a lot of literature on graph machine learning over the past few years, and a bewildering array of new methods. This talk is based on a series of results making a provocative argument. Maybe many graph machine learning methods are not really that […]

  • Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era

    Hybrid Event

    Presenter: Qi Xu, Postdoctoral Researcher, Department of Statistics & Data Science, Carnegie Mellon University Description: Multi-modality data are increasingly common across science medicine and technology, such as imaging, text, sensors, and genomics. These modalities are often high dimensional or unstructured and naturally exhibit blockwise (nonmonotone) missingness where different samples observe different subsets of modalities. Such […]

  • AM Seminar: Data Driven Modeling for Scientific Discovery and Digital Twins

    Presenter: Dongbin Xiu, Professor, Ohio State University Description:We present a data-driven modeling framework for scientific discovery, termed Flow Map Learning (FML). This framework enables the construction of accurate predictive models for complex systems that are not amenable to traditional modeling approaches. By leveraging data and the expressiveness of deep neural networks (DNNs), FML facilitates long-term […]

  • Liam Stanton

    AM Seminar: Multiscale Modeling of Cellular Membranes and Oncogenic Proteins

    Presenter: Liam Stanton, Professor, San Jose State University Description: In this talk, I will present a multiscale model for cellular membranes, which is trained on molecular dynamics simulations. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane […]

  • Statistics Seminar: Rotated Mean-Field Variational Inference and Iterative Gaussianization

    Presenter: Sifan Liu, Assistant Professor, Department of Statistical Science, Duke University Description:Mean-field variational inference (MFVI) approximates a target distribution with a product distribution in the standard coordinate system, offering a scalable approach to Bayesian inference but often severely underestimating uncertainty due to neglected dependence. We show that MFVI can be greatly improved when performed along […]

  • Semiconductor Career Summit – From Campus to Silicon Valley

    Stevenson Event Center Stevenson Service Road, Santa Cruz, CA

    A SEMI Professional Development Seminar organized by the SEMI Silicon Valley Chapter – Connecting College Students to the Semiconductor Industry. Learn about career opportunities in high tech and acquire valuable, practical information that will help you choose career directions and plan for your success.

  • Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean

    Presenter: Francois Ribalet, Research Associate Professor, School of Oceanography, University of Washington Description: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow, a custom-built automated flow cytometer deployed on over 100 research cruises, his team has collected nearly 850 billion cell measurements across global oceans. […]

  • AM Seminar: The Evolving Landscape of AI for Science and Engineering: Bridging Simulation, Experiment, and Multi-scale Dynamics

    Presenter: Aditi Krishnapriyan, Assistant Professor, UC Berkeley Description: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This […]

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    Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data

    Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data

    Presenter: Amanda Coston, Assistant Professor, University of California Berkeley Description: 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 […]

  • AM Seminar: Solution Discovery in Fluids with High Precision Using Neural Networks

    Presenter: Ching-Yao Lai, Assistant Professor, Stanford University Description: 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 […]

  • BE logo

    Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data

    Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data

    Presenter: Amanda Coston, Assistant Professor, University of California Berkeley Description: 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 […]

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    AM Seminar: Flexible Filaments and Swimming Cups: Just Go with the Flow

    Presenter: Lisa Fauci, Professor, Tulane University Description: The motion of waving or rotating filaments in a fluid environment is a common element in many biological and engineered systems. Examples at the microscale include chains of diatoms moving in the ocean, flagella of individual cells comprising multicellular colonies, as well as engineered nanorobots designed to deliver […]

  • AM Seminar: The Thinking Eye: AI That Sees, Reads, and Reasons in Medicine

    Presenter: Yuyin Zhou, Assistant Professor, UCSC Description: Medical AI is undergoing a profound transformation, evolving from simple pattern recognition to systems capable of complex clinical reasoning. This talk will chart this evolution across three dimensions: data, models, and evaluation. I will first highlight the shift from limited, unimodal datasets to massive multimodal resources. In particular, […]

  • Statistics Seminar: Some Recent Results on Transfer Learning

    Presenter: Oscar Hernan Madrid Padilla, Assistant Professor, University of California, Los Angeles Description: In the first part of the talk, I will introduce TRansfer leArning via guideD horseshoE prioR (TRADER), a novel approach enabling multi-source transfer through pre-trained models in high-dimensional linear regression. TRADER shrinks target parameters towards a weighted average of source estimates, accommodating […]

  • Statistics Seminar: Calibration Weighting-Style Diagnostics for Nonlinear Bayesian Hierarchical Models

    Presenter: Dr. Ryan Giordano, UC Berkeley Statistics Description: Multilevel Regression with Post-stratification (MrP) has become a workhorse method for estimating population quantities using non-probability surveys, and is the primary alternative to traditional survey calibration weights, e.g.~ as computed by raking. For simple linear regression models, MrP methods admit “equivalent weights”, allowing for direct comparisons between […]

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    Gen AI Dev Tools Bootcamp with AWS

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA

    Are you curious about generative AI development tools and vibe coding? Join 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. Here’s the tentative schedule: 1:00 – 1:45 – Intro to AI/ML 1:45 – 2:45 – […]

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    AM Seminar: Variational Inference and Density Estimation with Non-Negative Tensor Train

    Presenter: Dr. Xun Tang, Stanford University Description: 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 […]

  • Statistics Seminar: Hierarchical Clustering with Confidence

    Presenter: Snigdha Panigrahi, Associate Professor, Department of Statistics, University of Michigan Description: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 […]