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 […]
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, […]
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 […]
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 […]
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 […]
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.
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. […]
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 […]
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 […]
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 […]
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 […]
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, […]
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 […]
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 […]
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 […]
Please join the April 8th Archaeology and Biological Anthropology Lunch Talk, “The Emergence of Maritime Archaeology in the Republic of Benin: Research, Challenges, and Ongoing Initiatives,” presented by Affolabi Angelo Ayedoun (Anthropology, UC Santa Cruz).
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 […]
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 into a centralized database.
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 […]
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 […]