Zhimeng Ouyang is currently a Simons Postdoctoral Fellow at IPAM. She did her undergraduate work at Peking University and received her PhD in mathematics from Brown University, where she was advised by Prof. Yan Guo and Prof. Benoit Pausader. Dr. Ouyang is interested in the mathematical analysis of partial differential equations which arise in physical contexts. Her recent research mainly focuses on the kinetic theory and nonlinear dispersive equations.
Kevin Stubbs grew up in Silver Spring, Maryland and joined IPAM as a Simons Postdoctoral fellow in August 2021. He received Bachelors of Science in Mathematics and Computer Engineering from the University of Maryland, College Park in 2015 and a PhD in Mathematics from Duke University in 2021. Kevin’s research interests lie in developing fast algorithms for high dimensional problems with a focus on quantum chemistry and materials science. He is particularly interested in leveraging insights from physics to design provably fast algorithms.
Li Li grew up in Shanghai, China and he received his undergraduate education at Zhejiang University. He moved to the United States in 2016, where he completed his PhD in Mathematics at University of Washington in 2021. His recent work focuses on inverse problems associated with partial differential equations.
Sri grew up in Chennai, India and got his undergraduate degree at UC Berkeley in 2018. He then completed a PhD in mathematics at Vanderbilt, supervised by Jesse Peterson. His research interests include von Neumann algebras, free probability, geometric group theory and model theory, and the interactions between these disciplines.
Elisa Negrini joined IPAM as a Simons Postdoctoral Fellow in August 2022. She received her Bachelors and Master of Science in Mathematics from University of Bologna and her Ph.D. in Mathematical Sciences from Worcester Polytechnic Institute under the supervision of Luca Capogna and Giovanna Citti. Elisa’s research interests are very broad and span from Deep Learning and applications to Analysis and Differential Equations to Optimal Transport and Analysis on Metric Spaces. Her most recent work focuses on developing mathematically-principled deep learning algorithms for forward and inverse problems that are completely data-driven, robust to noise and that can be used to solve a variety of real world problems.
Minh Pham is a Simons Postdoctoral Fellow at IPAM. He received his Ph.D. in 2020 in Applied and Computational Mathematics at UCLA. After graduation, he started his Postdoctoral Fellowship as an Assistant Adjunct Professor and collaborated with professor Miao’s Coherent Imaging group at UCLA. His research lies in inverse problems, image processing, optimization, and deep learning. In collaboration with professor Miao, he develops algorithms to solve Computational Microscopy problems such as CDI, electron and X-ray imaging, Ptychography, and Tomography.