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 […]
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 […]
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.
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: 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: 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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
Ready to explore career pathways that matter? Attend our very special Careers in Climate Tech & Sustainability Panel for an inside look at careers that will help build a sustainable future. […]
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 […]
Interested in careers in AI and cybersecurity? Then don’t miss this highly informative workshop covering today’s most relevant trends in this space. In this 2-part session, you’ll get expert […]