معرفی
Matt Jacobs is an Assistant Professor in the Department of Mathematics at the University of California, Santa Barbara (UCSB), affiliated with the College of Letters and Science. His research focuses on optimal transport, partial differential equations (PDEs), numerical methods, and adversarial learning. He holds a Ph.D. from the University of Michigan (2017) and has held academic positions at UCLA and Purdue University.
Education:
- Ph.D. in Mathematics, University of Michigan, 2017
- B.A. in Mathematics, Columbia University, 2011 (Summa cum Laude)
Research Interests:
- Optimal Transport: Developing numerical methods and analyzing PDEs related to Wasserstein gradient flows.
- Adversarial Learning: Studying robustness in machine learning via multimarginal optimal transport.
- Free Boundary Problems: Investigating tumor growth models with nutrient dynamics.
Publications reflect a blend of theoretical analysis and computational innovation, with recent work emphasizing applications in adversarial robustness, tumor growth, and fluid dynamics. His methods, such as the back-and-forth algorithm for optimal transport, have been widely adopted.
Awards and Honors:
- NTT Research Fellow (2021)
- Simons-Berkeley Research Fellowship (2021)
- Grand Prize MHacks V (2015)
Advising and Service: Mentored students in numerical analysis and PDEs, including postdocs at UCLA and Purdue. Active in promoting equity and diversity in STEM through programs like the UCLA-CSU Summer Bridge.
Labs/Teams: Collaborates with interdisciplinary groups on projects involving optimal transport, including applications to machine learning and materials science.
