Past Events

Structure-Preserving Learning of Kinetic Collision Operators from Molecular Dynamics

Data Science Seminar

Huan Lei (Michigan State University)

Science 2.0: Evolving the Scientific Method from (AI-)Descartes, through Hilbert, to Noether

Industrial Problems Seminar

Lior Horesh (IBM)

Will FP64 Emulation Replace Native FP64 Computing?

Industrial Problems Seminar

Kasia Swirydowicz (AMD)

Generative Priors with Tunable Complexity for Inverse Problems

Data Science Seminar

Paul Hand (Northeastern University)

Practical Applications of Category Theory in Industry - A Retrospective

Industrial Problems Seminar

Patrick Campbell (Sabey Corporation)

Momentum accelerated algorithms for nonsymmetric matrices and complex approximation theory

Data Science Seminar

Nicholas Marshall (Oregon State University)

NonLinearities of a Ph.D.

Industrial Problems Seminar

Cristian Chica (Dapper Global)

Large-time behavior of optimal and ensemble Kalman filters in the linear setting

Data Science Seminar

Sangmin Park
Caltech

Math-to-Industry Boot Camp XI

The Math-to-Industry Boot Camp is an intense six-week session designed to provide graduate students with training and experience that is valuable for employment outside of academia. The program is targeted at Ph.D. students in pure and applied mathematics. The boot camp consists of courses in the basics of programming, data analysis, and mathematical modeling. Students work in teams on projects and are provided with training in resume and interview preparation as well as teamwork.

Hamilton-Jacobi-Bellman equations on graphs— the integro-differential viewpoint

Data Science Seminar

Russell Schwab (Michigan State University)