Past Events

Past Math Events

Monday, March 9, 2026

Friday, March 6, 2026

Thursday, March 5, 2026

Monday, March 2, 2026

Friday, February 27, 2026

  • Computational Mathematics and Scientific Computing Seminar

    Learning dynamical models from biological data with simulation-based inference

    Warren Weaver Hall, Room 1302
    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.

Thursday, February 26, 2026

Wednesday, February 25, 2026

Monday, February 23, 2026

Friday, February 20, 2026

  • Computational Mathematics and Scientific Computing Seminar

    Fast Algorithms for Optimal Damping in Mechanical Systems

    Warren Weaver Hall, Room 1302
    Feb. 20, 2026, 10AM
    Francoise Tisseur, University of Manchester

    Optimal damping seeks to determine a vector of damping coefficients that
    maximizes the decay rate of a mechanical system's response. This problem
    can be formulated as the minimization of the trace of the solution to a
    Lyapunov equation whose coefficient matrix depends on the damping
    parameters, which must satisfy nonnegativity constraints for physical
    relevance. We propose two fast algorithms for solving this resulting
    nonlinear optimization problem under nonnegativity constraints and
    demonstrate their computational efficiency.

    This is joint work with Professor Qingna Li at the Beijing Institute of
    Technology.

Special Events

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