Course syllabus
Graduate Seminar in Statistics
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].
Catalog Description: Topics in advanced statistics selected by the faculty. Discussion of current research papers in statistics and implementation of methods.
Learning outcomes
The goal of the graduate seminar is to enable successful students to:
[L1] Investigate and discuss current research in the statistics field
[L2] Implement current statistical methods in a modern computing language
[L3] Solve statistical problems in current research
[L4] Communicate statistical ideas related to current research
Assessments
As this is a discussion-oriented class, you will be assessed based on your preparation, participation, and attendance as outlined below.
Participation. Responsibility for leading discussions will rotate among students in the class and will consist of presenting a short overview — no more than 15 minutes — of the reading(s) summarizing the main ideas. In this leadership role you may choose whether to prepare slides, a handout, or other material as appropriate, but you should prepare some concrete reference that the group can use to follow your presentation. Presentations need not be comprehensive, but should aim to convey central ideas clearly. Besides leading discussion, I expect you to contribute by (a) asking questions, sharing thoughts, and the like during class and (b) commenting on shared copies of readings in advance of class.
Minimum effort: participate in discussion occasionally; comment on papers occasionally; when serving as discussion leader, prepare and present an introduction to the assigned reading(s).
Satisfactory effort: participate in discussion often and comment on papers often; when serving as discussion leader, prepare and present a clear introduction that provides a set of starting points for discussion by identifying a few main ideas or contributions in the assigned reading(s).
Strong effort: participate in discussion often and comment on most papers with thoughtful and helpful contributions; when serving as discussion leader, prepare and present a clear introduction that effectively identifies a few central ideas in the assigned reading(s), contextualizes them by drawing connections with prior/related/familiar work/applications/methods, and engages the group.
Reflections. Each student will be expected to prepare two written reflections of no more than one page each and upload these to the class folder on two occasions of their choosing during the quarter. The first reflection should be a response to one of the class discussions of your choosing that you did not lead. The second should be a response to one of the research talks from visiting speakers this quarter. I will provide and maintain a schedule of these talks on the drive folder. In each response, you should (a) briefly describe the focus of the discussion or talk, (b) identify a specific idea that you found engaging and explain that idea in detail, and (c) find 1-2 references that provide either further background or extension of the specific idea you identified.
Minimum effort: at least one reflection handed in nominally satisfying (a).
Satisfactory effort: both reflections handed in satisfying (a)-(b) and demonstrating genuine engagement with the discussion/talk.
Strong effort: both reflections handed in satisfying (a)-(c) and demonstrating genuine engagement with the discussion/talk.
Attendance. Attendance will be recorded each meeting. You are expected to attend each meeting, but must not miss more than two meetings. Exceptions to this all-but-two policy will be granted for excusable absences only.
Minimum effort: attend at least 8 meetings.
Satisfactory effort: attend at least 8 meetings.
Strong effort: attend at least 8 meetings.
Your grade will be based on the extent to which you meet the expectations above. Consistently minimum efforts will earn you a C or better; consistently satisfactory efforts will earn you a B or better; consistently strong efforts will earn you an A.
Readings and schedule
The schedule of presentations will be established during week one, and readings will be added on a timely ongoing basis. You may select a paper related to your own work OR choose from last year’s reading list (below). Selected papers must meet the following criteria:
Recent. Published 2010 or later, and ideally within the last 5 years.
Focus on theory, methods, or applications of statistics. Applications should have a strong statistical or quantitative emphasis (i.e., don’t pick a chemistry paper that happens to have some data analysis). A good rule of thumb is that the methods section should include a substantial portion on quantitative methodology.
From a top-tier journal. In statistics, leading journals include:
Journal of the American Statistical Association (JASA)
Annals of Statistics (AoS)
Annals of Applied Statistics (AoAS)
Journal of the Royal Statistical Society, Series B: Methodology (JRSSB)
Journal of Computational and Graphical Statistics (JCGS)
Electronic Journal of Statistics (EJS)
Statistical Science
Annual Review of Statistics and its Application
Bernoulli
Biometrika
Communications in Statistics – Theory and Methods
Other journals in specialized areas (e.g., Journal of Time Series Analysis) or application fields (e.g. PLOS Comp Bio) are also acceptable as long as they are of high quality, as are high-impact conference papers (e.g., NIPS, ICML, AISTATS, etc.).
Ideally, you can choose a paper related to your thesis work that occupies a central role in your literature review or the methodological/application background for your work. However, you can propose any paper that meets the above criteria, and you may also choose from last year’s reading list:
Interpretable ML.
Allen, G. I., Gan, L., & Zheng, L. (2023). Interpretable machine learning for discovery: Statistical challenges and opportunities. Annual Review of Statistics and Its Application.
Koh, P. W., & Liang, P. (2017). Understanding black-box predictions via influence functions. Proceedings of the 34th International Conference on Machine Learning.
Clustering methods for high-dimensional data.
Bouveyron, C., Girard, S., & Schmid, C. (2007). High-dimensional data clustering. Computational Statistics & Data Analysis.
Soltanolkotabi, M., Elhamifar, E., & Candès, E. J. (2014). Robust subspace clustering. The Annals of Statistics.
Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association.
Statistics and society.
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., & Lum, K. (2021). Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application.
Schneider, C. R., Kerr, J. R., Dryhurst, S., & Aston, J. A. (2023). Communication of Statistics and Evidence in Times of Crisis. Annual Review of Statistics and Its Application.
Estimation from nonrandom samples with big data.
- Meng, X. L. (2018). Statistical paradises and paradoxes in big data (i) law of large populations, big data paradox, and the 2016 us presidential election. The Annals of Applied Statistics.
Causal inference.
D’Agostino McGowan, L., Gerke, T., & Barrett, M. (2024). Causal inference is not just a statistics problem. Journal of Statistics and Data Science Education.
Ding, P., & Li, F. (2018). Causal inference. Statistical Science.
Imbens, G. W. (2024). Causal inference in the social sciences. Annual Review of Statistics and Its Application.
Conformal prediction: distribution free inference.
Lei, J., Robins, J., & Wasserman, L. (2013). Distribution-free prediction sets. Journal of the American Statistical Association.
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-free predictive inference for regression. Journal of the American Statistical Association.
Variable selection with knockoffs
Barber, R. F., & Candès, E. J. (2015). Controlling the false discovery rate via knockoffs. The Annals of Statistics.
Barber, R. F., Candès, E. J., & Samworth, R. J. (2020). Robust inference with knockoffs. The Annals of Statistics.
Policies
Time commitment
STAT590 is a one-credit course, which corresponds to a minimum time commitment of 3 hours per week, including class meetings, reading, assignment, and other preparations.
Because you can expect to take leadership roles with respect to class discussions at certain scheduled times during the quarter, you should also expect the distribution of workload to be a little uneven. Please take this into consideration when planning ahead.
Attendance and absences
Regular attendance is essential for success in the course and required per University policy. Absences should be excusable, but you do not need to notify me unless you will miss a meeting for which you are in a leadership role; if so, please email me with as much notice as possible.
In general, you may not miss more than two class meetings for the quarter; please get in touch with me if extenuating circumstances arise that require an exception to this policy.
Classroom environment
I support Cal Poly’s commitment to building an inclusive learning environment where all students can succeed. To that end, I strive to create a classroom in which every student is treated with respect and dignity, regardless of background, beliefs, opinions, identity, or the many visible and nonvisible differences within our community. I want you to feel comfortable in class, especially when sharing your perspective, asking questions, and engaging in discussion with me and with your peers. All members of this class are therefore expected to contribute to a respectful, supportive, and inclusive climate. I expect you to treat others with respect, even (and especially) when you disagree with or do not understand their perspective. I hold myself to the same standard, and I expect the same of every other student in the class. If you experience any form of disrespect or discrimination, small or large, please speak with me.
Communication and email
I encourage you to utilize class meetings and office hours to ask questions or discuss matters related to the course, since that is the only certain means of obtaining a response within a guaranteed time frame.
I respond to most email within 24 weekday hours, but I cannot guarantee this response time and I occasionally miss messages altogether (though I try not to). I don’t answer emails at night or on weekends, so while you are welcome to write me outside of business hours, please don’t expect a reply until the following business day. I also sometimes get behind on answering emails, so please wait a few days (preferably one week if it’s not pressing) before sending a follow up or reminder.
Grades and assessments
Per University policy, faculty have final responsibility for grading criteria and grading judgment and have the right to alter student assessment or other parts of the syllabus during the term. It is not appropriate to attempt to negotiate scores or final grades. Once the term has concluded, final grades will only be changed in the case of clerical errors, without exception. If you feel your grade is unfairly assigned at the end of the course, you have the right to appeal it according to the procedure outlined here.
Accommodations
It is University policy to provide, on a flexible and individualized basis, reasonable accommodations to students who have disabilities that may affect their ability to participate in course activities or to meet course requirements. Accommodation requests should be made through the Disability Resource Center (DRC).
Conduct and Academic Integrity
You are expected to be aware of and adhere to University policy regarding academic integrity and conduct. Detailed information on these policies, and potential repercussions of policy violations, can be found via the Office of Student Rights & Responsibilities (OSRR).