Tag: Research
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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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Kraw Lecture: At the Forefront of AI: Innovation and Discovery
Artificial intelligence is transforming how we understand and solve the world’s most complex challenges—while at the same time causing new challenges and concerns. We invite you to join us for a special UC Santa Cruz Kraw Lecture showcasing the faculty whose groundbreaking research in artificial intelligence is transforming science, technology, and society. From advances in…
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25th MARINe Annual Meeting – Public Day
We hope you can join us for the 25th MARINe Annual Meeting at UC Santa Cruz, hosted by the Raimondi Lab. A research project of this magnitude is possible solely through the cooperation of the Multi-Agency Rocky Intertidal Network (MARINe), a large consortium of research groups working together to collect compatible data that are entered…
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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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Planetary Health and Innovation Panel
Join us for an interactive conversation featuring visionary entrepreneurs, investors, and ecosystem experts at the forefront of global sustainability. This panel explores the intersection of environmental stewardship and cutting-edge solutions to drive meaningful impact. Following the discussion, please stay for a networking reception to connect with fellow attendees and industry leaders. It is a premier…
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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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Research Lunch & Learn: SBIR/STTR
Join us on April 8, 2026, 12-1 p.m. for a session led by Benjamin Legum, Director, New Venture Development. Small Business Innovation Research/Small Business Technology Transfer (SBIR/STTR) grants are offered by most federal agencies to foster the translation of high-tech solutions to commercial applications. This session provides a comprehensive introduction to the federal SBIR/STTR programs…
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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…