Title
CAPITAL Services Scholar in Artificial Intelligence and Machine Learning Assistant ProfessorOffice Building
Chicoine Architecture, Mathematics and Engineering HallOffice
248Mailing Address
Chicoine Architecture, Math & Engineering Building 248Math & Statistics-Box 2225
University Station
Brookings, SD 57007
Biography
Michael Puthawala is the CAPITAL Services Scholar in Artificial Intelligence and Machine Learning Assistant Professor. He is an applied mathematician working in the field of Machine Learning, with an emphasis on Mathematical Machine Learning, especially Manifold Learning, Topological/Geometric Learning, and Universality. He also has interests in more classical math/applied math topics, for example Inverse Problems, Scientific Computing, and Optimal Transport.Education
PhD, Applied Mathematics University of California, Los Angeles 2019Masters, Applied Mathematics University of California, Los Angeles 2016
BS, Mathematics, Rensselaer Polytechnic Institute 2014
Academic Interests
Machine Learning: Manifold Learning, Geometric Learning, UniversalityMath/Applied Math: Inverse Problems, Scientific Computing, Optimal Transport.
Work Experience
2019 - 2022 Simons Postdoctoral Fellow, Rice University Department of Computational Math and Operations Research6/2018 - 9/2018 Summer Software Research Intern, Google LLC. Venice, CA
5/2017 - 5/2017 & 5/2016 - 5/2016 Summer Research Intern, Oak Ridge National Lab. Oak Ridge, TN.
6/2014 - 8/2014 & 6/2013 - 8/2013 Summer Research Intern, MIT Lincoln Lab. Lexington, MA
Creative Activities
JOURNAL PUBLICATIONSDeep Invertible Approximation of Topologically Rich Maps between Manifolds
M Puthawala, M Lassas, I Dokmanic, P Pankka, M de Hoop
Universal joint approximation of manifolds and densities by simple injective flows
M Puthawala, M Lassas, I Dokmanic, M De Hoop
International Conference on Machine Learning, 17959-17983
Globally injective relu networks
M Puthawala, K Kothari, M Lassas, I Dokmanic, M de Hoop
Journal of Machine Learning Research 23 (105), 1-55
Unnormalized optimal transport
W Gangbo, W Li, S Osher, M Puthawala
Journal of Computational Physics 399, 108940
Area(s) of Research
Manifold Learning, Topological/Geometric Learning, Universality, Optimal Transport, Inverse ProblemsDepartment(s)
Image for Department of Mathematics and Statistics
Department of Mathematics and Statistics