Michael is a computational mathematician specializing in optimization, signal processing, and simulation, and a contributor in classroom, research, and commercial settings. He assists clients in the development of advanced Python data science applications using Anaconda Distribution. As a recognized subject matter expert in convex optimization, Michael’s open source modeling tools for optimization have twice been recognized with awards from the International Society for Mathematical Programming.
Prior to joining Anaconda, Michael served as a consulting assistant professor in the Information Systems Laboratory, a research associate in the Department of Energy Resources Engineering at Stanford University, and a staff scientist in the Department of Applied and Computational Mathematics at the California Institute of Technology.
Michael received a PhD and MS in Electrical Engineering from Stanford University, and a BS in Electrical and Computer Engineering from The University of Texas at Austin.