Fall 2026 Schedule
Monday
Foundation Seminar (Joint Seminar with BHI): monthly 11:00 am–12:00 pm ET
Colloquium: 4:30–5:30 pm ET
Tuesday
Joint Math/CMSA Geometry and Quantum Theory Seminar: 4:15–6:30 pm ET
Mathematical Economics Seminar: 2:00–4:00 pm ET monthly
Wednesday
CMSA Q&A Seminar: 12:00–1:00 pm ET
New Technologies in Mathematics Seminar: 2:00–3:00 pm ET
AI for the Working Mathematician: 4:30–5:30 pm ET
Thursday
Geometry and Mathematical Physics Seminar: 4:30–5:30 pm ET
Friday
Member Seminar: 12:00–1:00 pm ET
Mike Freedman CMSA Seminar: Monthly 2:00–4:30 pm ET
Category: Geometry and Quantum Theory Seminar |
Title: Geometry and Quantum Theory SeminarJoint Math/CMSA Geometry and Quantum Theory Seminar |
Category: New Technologies in Mathematics Seminar |
Title: New Technologies in Mathematics SeminarNew Technologies in Mathematics Seminar Speaker: tba |
Category: AI for the Working Mathematician |
Title: AI for the Working MathematicianAI for the Working Mathematician Speaker: Mark Selke, Harvard Title: TBA |
Category: Geometry and Mathematical Physics Seminar |
Title: Geometry and Mathematical Physics seminarGeometry and Mathematical Physics seminar Speaker: Paul Feehan, Rutgers University Title: TBA |
Category: Geometry and Mathematical Physics Seminar |
Title: Differential Geometry and Physics SeminarDifferential Geometry and Physics Seminar |
Category: Member Seminar |
Title: Member SeminarMember Seminar |
Category: Colloquium |
Title: ColloquiumColloquium Speaker: Roberto De Leo, Howard University |
Category: Mathematical Economics Seminar |
Title: Mathematical Economics SeminarSpeaker: Karun Adusumilli, University of Pennsylvania, Title: Continuous Time Asymptotic Representations for Adaptive Experiments Abstract: This article develops a continuous-time asymptotic framework for analyzing adaptive experiments—settings in which data collection and treatment assignment evolve dynamically in response to incoming information. Akey challenge in analyzing fully adaptive experiments, where the assignment policy is updated after each observation, is that the sequence of policy rules often lack a well-defined asymptotic limit. To address this, we focus instead on the empirical allocation process, which captures the (normalized) number of observations assigned to each treatment over time. We show that, under general conditions, any adaptive experiment and its associated empirical allocation process can be approximated by a limit experiment defined by Gaussian... |
Category: AI for the Working Mathematician |
Title: AI for the Working MathematicianAI for the Working Mathematician Speaker: Ravi Vakil, Stanford University |