Courses
I teach at both ends of the curriculum: a technical course on the mathematics of sequential decision-making, and a first-year course on what happens when those methods meet the world. Course pages below carry the syllabus, schedule, and readings.
Decision-Making under Uncertainty
Undergraduate / graduate · Special Topics
Principled computational techniques for deciding what to do when you cannot be sure what happens next: probabilistic reasoning, Markov decision processes, online and offline planning, partial observability, reinforcement learning, and mathematical programming.
Course page → Fall 2026 · CSCI/COLL 100Artificial Intelligence for Social Impact
First-year seminar · no prerequisites
What does it take to use AI responsibly for the greater societal good? Technical foundations alternate with talks from practitioners deploying AI against real problems: conservation, maternal health, disaster relief, financial inclusion.
Course page →Elsewhere
Guest lectures and tutorials
- 2026Tutorial, Decision-Making under Non-Stationarity: Concepts, Formulations, Algorithms, and Open Challenges, CPS-IoT Week.
- 2025Tutorial, Decision-Making under Non-Stationarity, NSF CPS PI Meeting.
- 2023Tutorial, Algorithms for Optimizing Public Transit Systems, IEEE Conference on Smart Computing.
- 2020Guest lecture, AI and Society, Washington University in St. Louis.
- 2016–17Teaching assistant, Artificial Intelligence and Machine Learning, Vanderbilt University.
Teaching a similar course?
I do not post slides publicly, but I am glad to share them with instructors. If you are teaching a course that overlaps with either of these and would find my materials useful, get in touch with a sentence about your course and I will send them over.