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


  • Tuesday, December 1, 2026 04:15 PM
Category: Geometry and Quantum Theory Seminar
Title: Geometry and Quantum Theory Seminar
Joint Math/CMSA Geometry and Quantum Theory Seminar  
  • Wednesday, December 2, 2026 02:00 PM
Category: New Technologies in Mathematics Seminar
Title: New Technologies in Mathematics Seminar
New Technologies in Mathematics Seminar Speaker: tba
  • Wednesday, December 2, 2026 04:30 PM
Category: AI for the Working Mathematician
Title: AI for the Working Mathematician
AI for the Working Mathematician Speaker: Mark Selke, Harvard Title: TBA
  • Thursday, December 3, 2026 04:30 PM
Category: Geometry and Mathematical Physics Seminar
Title: Geometry and Mathematical Physics seminar
Geometry and Mathematical Physics seminar Speaker: Paul Feehan, Rutgers University Title: TBA
  • Thursday, December 3, 2026 04:30 PM
Category: Geometry and Mathematical Physics Seminar
Title: Differential Geometry and Physics Seminar
Differential Geometry and Physics Seminar  
  • Friday, December 4, 2026 12:00 PM
Category: Member Seminar
Title: Member Seminar
Member Seminar  
  • Monday, December 7, 2026 04:30 PM
Category: Colloquium
Title: Colloquium
Colloquium Speaker: Roberto De Leo, Howard University
  • Tuesday, December 8, 2026 02:00 PM
Category: Mathematical Economics Seminar
Title: Mathematical Economics Seminar
Speaker: 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...
  • Wednesday, February 3, 2027 04:30 PM
Category: AI for the Working Mathematician
Title: AI for the Working Mathematician
AI for the Working Mathematician Speaker: Ravi Vakil, Stanford University