Graduate Seminar in Statistics

Term

Winter 2026

Updated

March 09, 2026

From the [course syllabus]:

Graduate seminar provides a venue for MS students to explore current research in statistics and its application through reading and discussion of recent papers; readings are selected based on relevance, influence, and student and faculty research interests.

Instructor: Trevor Ruiz (he/him) [email].

Class meetings: 12:10pm — 1:00pm W in 180-331.

Office hours: MW 1:00pm–2:30pm and [by appointment] in 25-236 or via Zoom; drop-ins are welcome but appointments are recommended/appreciated.

Readings: upload to [shared folder].

Week 1 (1/7/26)

Introductions & logistics; no reading.

Week 2 (1/14/26)

Meet in 10-223.

Visiting speaker: Dr. Ali Abuzaid, Visiting Professor, UCSB, Building Reliable Models for Complex Data.

Also this week: Monday 1/12 12:10pm–1:00pm in 10-124, Ethan Marzban, PhD Candidate, UCSB, An Empirical Bayes Approach to Nonparametric Regression with Correlated Errors.

Week 3 (1/21/26)

Meet in 10-223.

Visiting speaker: Dr. Amanda (Kun) Bu, Postdoctoral Scholar, University of South Florida. From Linear Models to Bayesian Networks: A Genomics-Inspired Framework for Understanding Financial Markets.

Week 4 (1/28/26)

Dillon Murphy and Lucas Kantorowski. Conformal prediction; ML applications with acoustic data.

  • Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B. and Snoek, J. (2019). Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS).

  • Hildebrand, J. A., Frasier, K. E., Helbe, T. A., and Roch, M. A. (2022). Performance metrics for marine mammal signal detection and classification. Journal of the Acoustical Society of America, 151(1), 414–427.

Also this week: Monday 1/26 12:10pm-1:00pm in 2-214, Katie Herder, PhD Candidate, University of Arizona, Improving Comparability in Network Meta-Analysis: Dose, Heterogeneity, and Mixed Treatments in Depression research.

Week 5 (2/4/26)

Alisa Krasilnikov and Jett Palmer. Interactive graphics; statistical communication.

  • VanderPlas, S., & Hofmann, H. (2017). Clusters beat trend!? Testing feature hierarchy in statistical graphics. Journal of Computational and Graphical Statistics, 26(2), 231-242.

  • Schneider, C. R., Kerr, J. R., Dryhurst, S., & Aston, J. A. (2024). Communication of statistics and evidence in times of crisis. Annual Review of Statistics and its Application, 11.

Also this week: Monday 2/2 12:10pm–1:00pm in 02-214, Dr. Connor Celum, Eli Lilly, Statistical decision-making for structured groups in clinical trials.

Week 6 (2/11/26)

Faran Igani and Cameron An. Permutation inference and sports applications.

  • Miller, J. B., & Sanjurjo, A. (2018). Surprised by the hot hand fallacy? A truth in the law of small numbers. Econometrica, 86(6), 2019–2047.

  • Bartoš, F., Sarafoglou, A., Godmann, H. R., Sahrani, A., Klein Leunk, D., Gui, P. Y., and others (2025). Fair coins tend to land on the same side they started: Evidence from 350,757 flips. Journal of the American Statistical Association, 120(552), 2118-2127.

Week 7 (2/18/26)

Jose Garcia and Ruben Jimenez. Statistical models with latent structure: state space and hidden Markov models.

  • Zuur, A. F., Fryer, R. J., Jolliffe, I. T., Dekker, R., & Beukema, J. J. (2003). Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics, 14(7), 665-685.

  • Calvo, G., Armero, C., & Spezia, L. (2025). Can the hot hand phenomenon be modelled? A Bayesian hidden Markov approach. Computational Statistics, 40(4), 2195-2222.

Week 8 (2/25/26)

Jasmine Cabrera and Allen Choi. Cluster selection in unsupervised learning.

  • Ben-Hur, A., Elisseeff, A., & Guyon, I. (2001). A stability based method for discovering structure in clustered data. Proceedings of the 2002 Pacific Symposium on Biocomputing, 6-17.

  • Fahim, A. (2021). K and starting means for k-means algorithm. Journal of Computational Science, 55, 101445.

Week 9 (3/4/26)

Alex Yuan and Hannah Pawig. Statistical inference: M-estimation; inference with discrete data.

  • Zhao, P., & Yu, B. (2006). On model selection consistency of Lasso. The Journal of Machine Learning Research, 7, 2541-2563.
  • Schilling, M. F., & Doi, J. A. (2014). A Coverage Probability Approach to Finding an Optimal Binomial Confidence Procedure. The American Statistician, 68(3), 133–145.

Week 10 (3/11/26)

Tyler Stoen. Differential correlation mining.

  • Gómez, J. Á. S., Zhang, E., & Liu, Y. (2025). Effective Permutation Tests for Differences Across Multiple High-Dimensional Correlation Matrices. Journal of Computational and Graphical Statistics, 1-10.

  • Practice the Greek alphabet!