Summary
Sen Na is an Assistant Professor in Industrial and Systems Engineering at Georgia Tech with nine years of research experience spanning high-dimensional statistics, graphical and semiparametric models, optimal control, and large-scale stochastic nonlinear optimization. He earned a Ph.D. in statistics from the University of Chicago and completed a postdoc at UC Berkeley/ICSI, where he worked on mathematically grounding scalable data-science methods under Michael Mahoney. His work bridges theory and practice—developing convergence guarantees for online model predictive control and sensitivity analyses for long-horizon nonlinear programming, with implementations in Julia/JuMP. Sen’s interests extend to applying machine learning in biology and neuroscience, and his background in mathematics from Nanjing University underpins a strong emphasis on rigorous, scalable algorithms.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Mathematics, 3.9/4.0, Bachelor of Science - BS, Mathematics, 3.9/4.0 at Nanjing University
Doctor of Philosophy (PhD), Statistics, 4.0/4.0, Doctor of Philosophy (PhD), Statistics, 4.0/4.0 at The University of Chicago