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 […]
Come get excited about Baskin Engineering Climate Week at our student pop-up! 🌎 Climate Week is a chance to explore how Baskin Engineering is addressing climate challenges through innovative research, teaching, and hands-on projects. Discover the events happening throughout the week and find ways to get involved! Swing by for FREE BE swag, coffee, cookies, […]
Flow cytometry is a valuable technique in microbial research used to measure the optical properties of single-celled organisms at high throughput. Oceanographers often deploy flow cytometers on research cruises in order to study the characteristics of phytosynthetic microbes—called phytoplankton—in regions and times with diverse environmental conditions. Because cytometers cannot distinguish between subpopulations, researchers typically cluster […]
Ready to explore career pathways that matter? Attend our very special Careers in Climate Tech & Sustainability Panel—celebrating Baskin Engineering Climate Week—for an inside look at careers that will help build a sustainable future. Panelists representing different roles and organizations will share their career journeys and offer practical insights into working in climate tech. There […]
Join Baskin Engineering to explore the frontier of power engineering, where the rapid rise of electrification and digital infrastructure is creating an unprecedented demand for next-generation talent and a critical opportunity for sustainability. This networking event bridges the gap between the classroom and the field, offering students and faculty a front-row seat to the trends […]
Oceanographic data, generated by modern technologies that measure biological systems across time, space, and cell populations, are often rich, high-dimensional, and highly heterogeneous. Such data provide valuable opportunities to study subcellular organization, cellular heterogeneity, and dynamic biological processes in marine environments. However, because marine plankton systems remain relatively understudied and less well characterized than many […]
This is a programming contest, but with a twist! Instead of scoring you based on your speed and solution accuracy, we score you based on your programming quality and solution accuracy. This means that instead of looking at how fast you can program a solution, we look at your number of compiles/runs instead.* The contestant that […]
Presenter: Riddhiman Bhattacharya, Postdoc, UCSC Description: We propose an active learning approach for dynamic fair resource allocation problems. In contrast to prior work that assumes full feedback from all agents on their allocations, we focus on scenarios where feedback is available only from a carefully select subset of agents at each epoch of the online […]
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 […]
Modern scientific studies increasingly rely on complex datasets exhibiting spatial and temporal dependence, particularly in social, environmental, and climate applications. This dissertation develops statistical models and computational methods for analyzing such data, with an emphasis on capturing dependence structures, nonlinear dynamics, and uncertainty quantification. A spatial deep learning framework is developed to extend classical geostatistical […]
Presenter: Yu Zhang, Associate Professor, ECE Department of UC, Santa Cruz Description: Modern cyber-physical systems present statistical learning problems that deviate significantly from standard i.i.d. supervised settings. In particular, two challenges frequently arise: (i) learning under hard structural constraints, and (ii) learning under severe distributional imbalance and rare events. In this talk, I present two […]
The STEM Culture Festival is returning to UC Santa Cruz on Friday, May 15 from 1-4pm in the Baskin Engineering Courtyard. Join us! This year, we’re expanding with even more performances, activities, and creative ways to celebrate UCSC’s vibrant, diverse, and excellent STEM culture! What to expect: Cuban Dance Master Susana Arenas and her troupe […]
Welcome to the premier physical AI hackathon on the West Coast. We are bringing together the top 200 AI, infrastructure, and hardware engineers to build autonomous, agentic applications on the NVIDIA NemoClaw stack. You aren’t just calling APIs, you are building on enterprise-grade hardware. The Tracks: The Edge Track: 40 exclusive teams will be granted […]
Presenter: Peng Ding, Associate Professor, UC Berkeley Description: I will present some recent results on unifying regression-based and design-based causal inference in time-series experiments and crossover experiments. Part I: Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, […]
The annual BE Student Project Showcase celebrates the innovative work and accomplishments of undergraduate engineers in capstone courses and research pathways.
Presenter: Deqian Kong, PhD student, UCLA Description: How does the hippocampus turn experience into a cognitive map that is not just a passive record of space but a representation ready […]
Real-time monitoring of infectious disease outbreaks calls for statistical models that recover interpretable quantities such as the time-varying reproduction number from noisy count data, track posterior uncertainty, and run on time scales compatible with daily updates. Existing methods address these aims through separate model classes. Discretized Hawkes processes, Poisson autoregressions, and distributed lag models each […]
Time series data with dependence arise across a wide range of scientific and engineering disciplines, often presenting challenging inferential problems related to structural change and clustering. This Ph.D. proposal addresses several related problems in statistical inference for multivariate and network-indexed time series. First, we develop a weighted multivariate $U$-statistic procedure for detecting a single changepoint […]
A ubiquitous task in statistical practice is that of variable selection, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models […]
Many scientific applications produce repeated point pattern realizations across subjects, regions, or time. While such point patterns exhibit individual variation, we assume they arise from related point processes that share a common distributional structure. This dissertation develops a Bayesian nonparametric modeling framework built around an interpretable baseline. We work with Poisson processes, such that the […]