Past Events

Thursday, March 12, 2026

• CS Colloquium

  Modeling Human and Algorithmic Behavior in Healthcare

  60 Fifth Avenue, Room TBA,
  March 12, 2026, 2PM
  Divya Shanmugam, Cornell Tech

Wednesday, March 11, 2026

• CS Colloquium

  Towards More Trustworthy and Efficient Systems

  60 Fifth Avenue, Room TBA,
  March 11, 2026, 2PM
  Hugo Lefeuvre, University of British Columbia

Tuesday, March 10, 2026

• CS Colloquium

  Building Efficient and Scalable Machine Learning Systems

  60 Fifth Avenue, Room TBA,
  March 10, 2026, 2PM
  Qinghao Hu, Massachusetts Institute of Technology

Monday, March 9, 2026

• CS Colloquium

  Computational Complexity and the Nature of Circuits

  60 Fifth Avenue, Room TBA,
  March 09, 2026, 2PM

Friday, March 6, 2026

• CS Colloquium

  Towards a Less Conservative Theory of Machine Learning: Unstable Optimization and Implicit Regularization

  60 Fifth Avenue, Room TBA,
  March 06, 2026, 11AM
  Jingfeng Wu, UC Berkeley

• Computational Mathematics and Scientific Computing Seminar

  Operator learning without the adjoint

  Warren Weaver Hall, Room TBA,
  March 06, 2026, 10AM
  Diana Halikias, New York University

Thursday, March 5, 2026

• CS Colloquium

  Your host is a distributed system!

  60 Fifth Avenue, Room TBA,
  March 05, 2026, 2PM
  Midhul Vuppalapati, Cornell University

• Theory Seminar

  A Polynomial Space Lower Bound for Diameter Estimation in Dynamic Streams

  Warren Weaver Hall, Room TBA,
  March 05, 2026, 11AM
  Erik Waingarten, UPenn

Monday, March 2, 2026

• CS Colloquium

  Rethinking Transaction Scheduling for Database Performance

  60 Fifth Avenue, Room TBA,
  March 02, 2026, 2PM
  Audrey Cheng, UC Berkeley

Friday, February 27, 2026

• Computational Mathematics and Scientific Computing Seminar

  Learning dynamical models from biological data with simulation-based inference

  Warren Weaver Hall, Room TBA,
  Feb. 27, 2026, 10AM
  Aaron Dinner, The University of Chicago

  How do patterns in space and time emerge from molecular interactions in living systems? Answering this question is challenging even when the molecular participants in processes of interest are well-characterized because feedbacks are difficult to intuit and quantitative shifts in molecular features and populations can result in qualitative differences in patterns. Ever-increasing amounts of data now present the opportunity to evaluate models quantitatively, and simulation-based inference provides a principled approach. However, its use remains limited in cell biological contexts. I will discuss my group's recent efforts to use simulation-based inference---including recent advances that incorporate machine learning---to learn dynamical models of cell signaling in circadian and developmental contexts and show that simulation-based inference can reveal unanticipated mechanisms, in addition to quantitative insights.

Special Events

• Special Events Calendar
• Courant Lectures

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