Michael Puthawala
CAPITAL Services Scholar in Artificial Intelligence and Machine Learning Assistant Professor
Biography
Michael Puthawala is the CAPITAL Services Scholar in Artificial Intelligence and Machine Learning and an 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 and geometric learning and universality. He also has interests in more classical math and applied math topics like inverse problems, scientific computing and optimal transport.
Education
- Ph.D. in applied mathematics | University of California, Los Angeles | 2019
- M.S. in applied mathematics | University of California, Los Angeles | 2016
- B.S. in mathematics | Rensselaer Polytechnic Institute | 2014
Academic and Professional Experience
Academic Interests
- Machine learning: manifold learning, geometric learning, universality
- Math/applied math: inverse problems, scientific computing, optimal transport
Work Experience
- 2019-22 Simons Postdoctoral Fellow, Rice University Department of Computational Math and Operations Research
- June 2018-September 2018 Summer Software Research Intern, Google LLC. Venice, California
- May 2017 and May 2016 Summer Research Intern, Oak Ridge National Lab. Oak Ridge, Tennessee
- June 2014-August 2014 and June 2013-August 2013 Summer Research Intern, MIT Lincoln Lab, Lexington, Massachusetts
Research and Scholarly Work
Areas of Research
- Manifold learning
- Topological/geometric learning
- Universality
- Optimal transport
- Inverse problems
Publications
- Deep 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
Associated Areas
