Plenary Speakers
Mihai Cucuringu is a Professor in the Department of Mathematics at UCLA. Previously, he was an Associate Professor in the Department of Statistics, an Affiliate Faculty in the Mathematical and Computational Finance group at the Mathematical Institute at University of Oxford, and a Turing Fellow at The Alan Turing Institute in London. He finished his Ph.D. in Applied Mathematics at Princeton University in 2012. His research pertains to the development and mathematical analysis of algorithms that extract information from massive noisy data sets, network analysis and certain inverse problems on graphs, such as clustering and ranking, with an eye towards extracting structure from time-dependent data which can be subsequently leveraged for prediction. His research interests in finance focus on statistical arbitrage, machine-learning for asset pricing, market microstructure, and synthetic data generation.
Natalia Komarova holds an MS in Theoretical Physics from Moscow State University and a PhD in Applied Mathematics from University of Arizona, Tucson, 1998. After being a Member at the Institute for Advanced Studies in Princeton (1999-2003), she became Assistant Professor at the Department of Mathematics at Rutgers, and then worked at UC Irvine from 2004 until 2024, when she joined UCSD. Komarova is interested in Applied Mathematics, and in particular, in Mathematical Biology, evolution, and modeling of complex social phenomena.
She has received numerous honors, including AAAS Fellow (2024), UCI Chancellor’s Professor (2017-2023), UCI Senate Distinguished Mid-Career Faculty Award for Research (2010-11), UCI Senate Distinguished Assistant Professor Award for Research (2006-07), Alfred P. Sloan Research Fellowship (2005-07), Prize for Promise (2002)
Bo Li received his Ph.D. in mathematics and MS in mechanics from University of Minnesota in 1996. He did a postdoc at UCLA and was an assistant professor at the University of Maryland. Since 2004, he has been an associate and then full professor of mathematics at UCSD. Bo Li's research interests include applied PDEs and numerical analysis with application to biology and materials science. In recent years, he has been developing mathematical theories and computational methods to study biomolecular interactions, electrostatics, and bacterial biofilms. His interdisciplinary research has been constantly supported by different funding agencies. Bo Li has trained a dozen of Ph.D. students and postdocs. He has been involved in organizing many scientific activities, and has also served as an editor for several research journals.
Hrushikesh Mhaskar (b. 1956, Pune, India) did his undergraduate studies in Institute of Science, Nagpur, and received his first M. Sc. in mathematics from the Indian Institute of Technology in Mumbai in 1976. He received his Ph. D. in mathematics and M. S. in computer science from the Ohio State University, Columbus, in 1980. He then joined Cal. State L. A., and was promoted to full professor in 1990. After retirement in 2012, he was a visiting associate with California Institute of Technology until 2017, and occasionally served as a consultant for Qualcomm. Since 2012, he is also Research Professor (Distinguished Research Professor since 2024) at Claremont Graduate University. He has authored more than 170 articles in the area of approximation theory, potential theory, neural networks, wavelet analysis, and data processing. His book,“Weighted polynomial approximation”, was published in 1997 by World Scientific, and the book with Dr. D. V. Pai, “Fundamentals of Approximation Theory” was published by Narosa Publishers, CRC, and Alpha Science in 2000. He serves on the editorial boards of Applied and Computational Harmonic Analysis, Frontier Journal on Mathematics of Computation and Data Science, Journal of Approximation Theory, Mathematical Foundations of Computing, SIAM Journal of Numerical Analysis, and Mathematical Foundations of Machine Learning. He was one of the founding editors of Jaen Journal of Approximation. He was awarded the Humboldt Fellowship for research in Germany four times. He was John von Neumann distinguished professor in 2011, and will be August-Wilhelm Scheer Professor in 2020, at Technical University of Munich. His research was supported by the Intelligence Advanced Research Project Agency, the National Science Foundation, the U. S. Army Research Office, the Air Force Office of Scientific Research, the National Security Agency, the Research and Development Laboratories, and Office of Naval Research.
Maziar Raissi is an Assistant Professor of Applied Mathematics at the University of California, Riverside. After receiving his Ph.D. in Applied Mathematics & Statistics, and Scientific Computations from the University of Maryland, College Park, he carried out his postdoctoral research in the Division of Applied Mathematics at Brown University. He then worked at NVIDIA in Silicon Valley as a Senior Software Engineer before moving to Boulder, CO where he was an Assistant Professor of Applied Mathematics at the University of Colorado Boulder. Dr. Raissi’s expertise lies at the intersection of Probabilistic Machine Learning, Deep Learning, and Data Driven Scientific Computing. He has been actively involved in the design of learning machines that leverage the underlying physical laws and/or governing equations to extract patterns from high-dimensional data generated from experiments.