Statistics Department

The Statistics Department at the Baskin School of Engineering has a strong reputation in research and graduate education. Faculty expertise include Bayesian methods, dependent data modeling, machine learning and optimization, data science, and applied statistics. Statistics faculty include four fellows of the American Statistical Association (ASA) and two fellows of the International Society for Bayesian Analysis (ISBA)—several have held leadership roles in both organizations. Graduates of the Statistics Ph.D. program have earned awards for their research and gone on to careers in industry, government, and academia.

Events from this organizer

Today

  • Statistics Seminar: Topological Clustering: from Multilayer Networks to Climate Resiliency and Beyond

    Presenter: Professor Yulia R. Gel, Virginia Tech Description: Multilayer networks continue to gain significant attention in many areas of study, particularly, due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socio-environmental ecosystems. However, clustering of multilayer networks, especially, using the information on higher order interactions of the […]

  • Reboot the Earth Hackathon

    United Nations Reboot the Earth Hackathon

    Silicon Valley Campus 3175 Bowers Avenue, Santa Clara, CA, United States

    The United Nations (UN) and the Baskin School of Engineering at the University of California, Santa Cruz, are collaborating to bring the “Reboot the Earth” hackathon to the West Coast […]

  • Statistics Seminar: Beyond the Average Treatment Effect: Causal Mediation Methods for Understanding Intervention Mechanisms

    Presenter: Hanna Kim, Assistant Professor, Psychology Department, UCSC Description: Understanding how an intervention works is a central question in behavioral and social research, following the demonstration of its overall effect. Traditional mediation analysis techniques often assume a homogeneous mechanism of effects, overlooking both validity concerns and subgroup variation in causal pathways. In this talk, I […]

  • Kathleen Schmidt: Sequential Experimental Design for Materials Strength Model Calibration

    Presenter: Katie Schmidt, UQ & Optimization Group Leader, Lawrence Livermore National Laboratory Description: Due to the time and expense associated with physical experiments, there is significant interest in optimal selection of the conditions for future experiments. Selection based on reduction in parameter uncertainty provides a natural path forward. We consider this type of optimal sequential […]

  • Be Inspired: Explore Graduate Studies in STEM

    Not sure if graduate school is right for you? Join us to learn what graduate school is really about and explore whether it’s the right path for you. We’ll cover topics such as qualifying exams, funding options, common misconceptions, and more! Click the link below to register for the event: https://ucsc.zoom.us/webinar/register/WN_31OHhwc7QPqJ7nSyiuAUNg

  • Statistics Seminar: Heterogeneous Statistical Transfer Learning

    Hybrid Event

    Presenter: Subhadeep Paul, Associate Professor, Ohio State University Description: In the first part of the talk, we consider the problem of Transfer Learning (TL) under heterogeneity from a source to a new target domain for high-dimensional regression with differing feature sets. Most homogeneous TL methods assume that target and source domains share the same feature […]

  • Statistics Seminar: Boosting Biomedical Imaging Analysis via Distributed Functional Regression and Synthetic Surrogates

    Virtual Event

    Presenter: Guannan Wang, Associate Professor, The College of William & Mary Description: Generative AI has emerged as a powerful tool for synthesizing biomedical images, offering new solutions to challenges such as data scarcity, privacy constraints, and modality imbalance. However, the reliable use of synthetic images in scientific analysis requires principled statistical frameworks that can assess […]

  • Statistics Seminar: Inferring Unobserved Trajectories from Multiple Temporal Snapshots

    Hybrid Event

    Presenter: Yunyi Shen, Ph.D. Candidate, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Description: Practitioners often aim to infer an unobserved population trajectory using sample snapshots at multiple time points. E.g. given single-cell sequencing data, scientists would like to learn how gene expression changes over a cell’s life cycle. But sequencing any […]

  • Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective

    Hybrid Event

    Presenter: Jiaqi Li, William H. Kruskal Instructor, University of Chicago Description:Modern machine learning (ML) algorithms achieve remarkable empirical success, yet providing rigorous statistical guarantees remains a major challenge, particularly in distributional theory and online inference methods. In this talk, we will introduce a novel framework to provide mathematical foundations for ML by bringing powerful tools […]

  • Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era

    Hybrid Event

    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, and genomics. These modalities are often high dimensional or unstructured and naturally exhibit blockwise (nonmonotone) missingness where different samples observe different subsets of modalities. Such […]

  • Statistics Seminar: Rotated Mean-Field Variational Inference and Iterative Gaussianization

    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 […]

  • Two students sit in the BE Courtyard and drink coffee

    Transform Your Future Pop-Up (Cookies Included!)

    Jack Baskin Engineering Baskin Engineering 1156 High Street, Santa Cruz, CA

    Join Baskin Engineering to celebrate National Engineers Week with a sweet stop at the Transform Your Future Pop-Up (Cookies Included!) 🍪☕ This year’s Engineers Week theme, Transform Your Future, is a powerful reminder that engineering doesn’t just shape our world—it shapes our opportunities, our communities, and the futures we can imagine for ourselves. Swing by […]

  • BE logo

    Exploring Research Pathways at Baskin Engineering

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA

    Curious how being part of a research lab can supercharge your experience as a Baskin Engineer?   Join us for this informative event to learn about opportunities to solve open-ended problems, build deeper technical skills, and learn how to think like an engineer. We’ll kick things off with a quick overview of the kinds of research […]

  • Rocket Team in Slugworks

    BE Club Bash – Engineers Week

    Jack Baskin Engineering Baskin Engineering 1156 High Street, Santa Cruz, CA

    Discover innovation at the Baskin Engineering Club Bash, an event celebrating National Engineers Week! Mark your calendars for Thursday, February 26, 12–2 PM in the BE Courtyard! The BE Club Bash brings together student organizations across all engineering disciplines to showcase their projects, demos, and interactive activities. Stop by to: Explore hands-on booths and demonstrations from student organizations Learn about engineering opportunities on […]

  • Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean

    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. […]

  • BE logo

    Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data

    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 […]

  • BE logo

    Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data

    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 […]

  • 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 […]

  • Ticknor, B. (STAT) – Clustering and Tractable Multivariate Inference for Extremes

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA
    Hybrid Event

    Modeling environmental extremes often involves large collections of spatial or temporal records where both clustering similar series and modeling dependence among extremes are challenging tasks. This Ph.D. proposal addresses several related problems in extreme value analysis. In particular, we study how to cluster many time series based on their extremal behavior using strategies defined via […]

  • 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 […]