معرفی
Michael Puthawala is an Assistant Professor and CAPITAL Services Scholar in Artificial Intelligence and Machine Learning at South Dakota State University's Department of Mathematics and Statistics. His research focuses on mathematical machine learning, particularly manifold learning, topological/geometric learning, and universality, alongside classical applied mathematics areas like inverse problems 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)
His work explores the intersection of geometry and machine learning, including injective neural networks and globally bijective operators. Recent research trends emphasize theoretical foundations of neural operators and their applications to complex manifolds. Puthawala’s contributions include advancing optimal transport theory for inverse problem diagnostics and unnormalized density modeling. He has held roles such as a Simons Postdoctoral Fellow at Rice University and internships at Google, Oak Ridge National Lab, and MIT Lincoln Lab.
No scientific awards or grants are explicitly listed in the provided materials. His research is affiliated with computational mathematics and engineering teams at SDSU.
