Welcome to CSta
We combine Computer Science, Statistics, AI, Data Science, and Cybersecurity to enhance multidisciplinary learning and research for undergrads and grads. Cross campus and industry collaborations involve faculty, students, scientists, artists, health care researchers, historians, and engineers.
Undergraduate & Graduate Courses
See our courses in Computer Science, Statistics, Data Science, and Cybersecurity ranging from computing foundations to theory and statistics to systems and artificial intelligence.
courses
Announcements
[Talk] Travess Smalley: Generative Systems in Art & Design (2/12/2026) - When: Friday, February 20, 3:00 PM Where: Tyler 055 Abstract Generative Systems in Art and Design is an artist talk that surveys Travess Smalley’s generative practice, including scripting in Adobe Photoshop, creative coding, and building small creative software. It also looks at ways software-based systems can be translated into physical prints and exhibition work. The […]
[Award] Faculty publication awarded IEEE Editor’s Choice (2/12/2026) - IEEE Access designated Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning by Kaleel Mahmood, Ethan Rathbun, Ronak Sahu, Marten Van Dijk, Sohaib Ahmad, and Caiwen Ding as an Editor’s Choice article. The publication is currently listed on the Featured Articles page.
[Talk] Lesia Semenova: Which Model Should You Trust When Many Models Fit? (2/11/2026) - When: Thursday, February 12, 12:00 PM Where: Bliss 190 Abstract In practice, there is rarely a single “golden” answer. In this talk, I argue that trustworthy ML should be set-valued: instead of validating a single model, we should reason over the Rashomon set (the set of models that meet a performance criterion). I’ll present a […]- [Talk] Xiaomeng Ju: Bayesian Modeling for Functional and Matrix Data with Applications to Neuroimaging Analysis (2/9/2026) - When: Wednesday, February 18, 12:00 PM Where: Avedisian 105 Abstract Neuroimaging data present fundamental statistical challenges: they are high-dimensional and exhibit complex structures. In this talk, I present Bayesian methods developed for functional data and matrix-valued data motivated by neuroimaging applications, emphasizing interpretability, scalability, and uncertainty quantification. I first introduce Bayesian methods developed for two […]
[Talk] Jinghao Sun: Panel Data Meets Unmeasured Confounding: A Nonlinear Difference-in-Differences Framework (2/4/2026) - When: Friday, February 13, 3:00 PM Where: Tyler 055 Abstract Difference-in-differences (DiD) is a foundational tool for causal inference in panel data, widely used in policy, economics, and health research. Its appeal lies in its intuitive design and robustness to time-invariant unmeasured confounding. However, standard DiD relies on strong assumptions—particularly parallel trends—that are often violated […]- [Talk] Tian Wang: Modeling of Complex Data (1/30/2026) - When: Friday, February 6, 3:00 PM Where: Tyler 055 Abstract In this talk, we will first discuss the proposed α-separability for functional data. Functional data consist of random samples observed over a continuum, such as curves over a time range. These data often exhibit two kinds of variation: amplitude variation in the vertical direction and […]