Tag: seminar
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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…
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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…
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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…
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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…
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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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Statistics Seminar: Advancing Statistical Rigor in Single-Cell and Spatial Omics Using In Silico Control Data
Presenter: Guan’ao Yan, Assistant Professor, Michigan State University Description: Single-cell and spatial transcriptomics technologies now let us map cellular diversity and tissue organization at high resolution, but the computational methods built to analyze these data are difficult to evaluate in a rigorous, reproducible way. Two key barriers are the lack of realistic synthetic data with…
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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: 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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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…
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February 25, 2026 | Works-in-Progress with Geoffrey Bowker
On Wednesday, February 25, 2026 at 3:00PM in Humanities 1, Room 210, join SJRC scholars on the death of infrastructure, AI, and underwater network cables and his collaborative comic book on Actor Network Theory.