Section 15. Numerical Analysis and Scientific Computing

Design of numerical algorithms and analysis of their accuracy, stability, convergence and complexity for a wide class of (complex) problems with interests in applications. Numerical methods for high dimensional problems. Multiscale problems and probabilistic numerical methods. Approximation theory andcomputational aspects of harmonic analysis. Numerical reduction and uncertainty quantification. Numerical solution of algebraic, functional, stochastic, differential, and integro-differential equations.
Gang Bao

Zhejiang University, China

Gang Bao is a Chair Professor in the School of Mathematical Sciences, Zhejiang University, received his Ph.D. degree from Rice University in 1991. Previously, he also had tenured positions at the University of Florida and Michigan State University. His research interests include inverse problems for PDEs, wave propagation, and numerical analysis. As one of the world leaders in inverse problems, he is especially known for his contributions to mathematical analysis and computational methods in scattering and inverse scattering problems; modeling and computation in diffractive and nano-optics. He is the author of more than 150 research articles, a Fellow of SIAM, AMS, and CSIAM, and a recipient of the Feng Kang Prize of Scientific Computing.
Marsha Berger

Courant Institute, NYU and Flatiron Institute, USA

Marsha Berger is a Silver Professor of Computer Science and Mathematics at the Courant Institute of New York University as well as a researcher at the Flatiron Institute in New York City. She is a frequent consultant at the NASA Ames Research Center in California. Her early work was on adaptive mesh refinement methods for time-dependent partial differential equations. She then turned to Cartesian embedded boundary methods for solving PDEs in complicated geometries. More recently she turned to computational modeling of tsunamis. She is heavily involved in the software packages Cart3D and GeoClaw, which are widely used by practitioners. She is a member of the National Academy of Sciences, Engineering and the American Academy of Arts and Sciences.
Jan S Hesthaven

EPFL, Switzerland

Jan S Hesthaven is a Professor of Mathematics and holds the Chair of Computational Mathematics and Simulation Science at Ecole Polytechnique Federale de Lausanne, CH. Before joining EPFL, he was a Professor of Applied Mathematics at Brown University, US. His interests evolve around the development, analysis, and application of high-order accurate computational methods for solving time-dependent partial differential equations with a particular emphasis on linear and nonlinear wave problems. He is particularly known for contributions to the development of spectral methods, discontinuous Galerkin methods, and reduced basis methods for model order reduction. He is an Alfred P Sloan Fellow and a Fellow of SIAM.
Nicholas Higham

University of Manchester, UK

Nicholas Higham is a Royal Society Research Professor and Richardson Professor of Applied Mathematics in the Department of Mathematics at the University of Manchester. Much of his research is concerned with the accuracy and stability of numerical algorithms, and a recent focus is mixed precision numerical linear algebra algorithms. He is the author of four SIAM books, including «Functions of Matrices: Theory and Computation» (2008) and an editor of the 1000-page «The Princeton Companion to Applied Mathematics» (2015). He is a Fellow of the Royal Society, a SIAM Fellow, an ACM Fellow, a Member of Academia Europaea, and recipient of the SIAM 2021 George Polya Prize for Mathematical Exposition. He blogs about applied mathematics at
Gitta Kutyniok

Ludwig-Maximilians University Munich, Germany

Gitta Kutyniok currently has a Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at the Ludwig-Maximilians Universität München. She received Ph.D. from the Universität Paderborn in Germany, and her Habilitation in Mathematics in 2006 at the Justus-Liebig Universität Gießen. She held visiting positions at several US institutions, including Princeton University, Stanford University, Yale University, Georgia Institute of Technology, and Washington University in St. Louis, and was a Nachdiplomslecturer at ETH Zurich in 2014. Before moving to München she was a Full Professor at the Universität Osnabrück and Einstein Chair at TU Berlin.

Gitta Kutyniok has received various awards for her research such as an award from the Universität Paderborn in 2003, the Research Prize of the Justus-Liebig Universität Gießen and a Heisenberg-Fellowship in 2006, and the von Kaven Prize by the DFG in 2007. She was invited as the Noether Lecturer at the ÖMG-DMV Congress in 2013, the Hans Schneider ILAS Lecturer at IWOTA in 2016, a plenary lecturer at the 8th European Congress of Mathematics (8ECM) in 2021, and the lecturer of the London Mathematical Society (LMS) Invited Lecture Series in 2022.

Gitta Kutyniok’s research work covers, in particular, the areas of applied and computational harmonic analysis, approximation theory, artificial intelligence, compressed sensing, frame theory, imaging sciences, inverse problems, machine learning, numerical analysis of partial differential equations, and applications to life sciences and telecommunication. The most significant of Gitta Kutyniok’s contributions is perhaps the introduction of the directional multiscale system of shearlets ( and a comprehensive theoretical approach to analyzing sparse regularization of inverse problems using harmonic analysis and microlocal analysis.

Eric Vanden-Eijnden

Courant Institute of Mathematical Sciences/NYU, USA

Lecture on the computational aspects of statistical mechanics

Jointly in sections 11, 12, 18

Eric Vanden-Eijnden is a Professor of Mathematics at the Courant Institute of Mathematical Sciences,New York University. His research focuses on the mathematical and computational aspects of statistical mechanics, with applications to complex dynamical systems arising in molecular dynamics, materials science, atmosphere-ocean science, fluid dynamics, and neural networks. He is also interested in the mathematical foundations of machine learning (ML) and the applications of ML in scientific computing. He is known for the development and analysis of multiscale numerical methods for systems whose dynamics span a wide range of spatio-temporal scales. He is the winner of the Germund Dahlquist Prize and the J.D. Crawford Prize,and a recipient of the Vannevar Bush Faculty Fellowship.
Rachel Ward

University of Texas at Austin, USA

Rachel Ward is the W.A. «Tex» Moncrief Distinguished Professor in Computational Engineering and Sciences — Data Science and Professor of Mathematics at UT Austin. She is recognized for her contributions to sparse approximation, stochastic gradient methods, and machine learning. Among her awards are the Sloan Research Fellowship, the NSF CAREER award, and the 2016 IMA Prize in Mathematics and its Applications.
Lexing Ying

Stanford University, USA

Lexing Ying is a Professor of Mathematics at the Department of Mathematics of Stanford University. His interests include numerical analysis and scientific computing. In particular, he has contributed to the development of fast and multiscale algorithms in high-frequency wave propagation, computational chemistry, and inverse problems. He is a recipient of the Feng Kang Prize of Scientific Computing, the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing, and the Silver Morningside Medal in Applied Mathematics.
Thu Nov 18 2021 13:44:19 GMT+0300 (Moscow Standard Time)