Internal Advisory Board, MISM
Professor in Mathematics, Duke University
Hongkai Zhao, PhD, Internal Advisory Board, is the Ruth F. DeVarney Professor in Mathematics at Duke University. He was formerly the Chancellor's Professor in the Department of Mathematics at the University of California, Irvine. Dr. Zhao is known for his work in scientific computing, imaging, and numerical analysis, including the fast-sweeping method for the Hamilton-Jacobi equation and numerical methods for moving-interface problems. He earned his Bachelor of Science in applied mathematics from Peking University in 1990 and, two years later, his Master's in the same field from the University of Southern California. From 1992 to 1996, Dr. Zhao attended the University of California, Los Angeles, where he got his PhD in mathematics. From 1996 to 1998, he was a Gábor Szegő Assistant Professor in the Department of Mathematics at Stanford University and was then promoted to Research Associate, a position he held until 1999, when he relocated to the University of California, Irvine (UCI). At the same time, he was also a member of the Institute for Mathematical Behavioral Sciences and the Department of Computer Science at UCI. From 2010 to 2013 and from 2016 to 2019, Dr. Zhao was the chair of the Department of Mathematics, and since 2016 has served as a Chancellor's Professor of mathematics. Hongkai Zhao received the Alfred P. Sloan Fellowship in 2002 and the Feng Kang Prize in Scientific Computing in 2007. He was elected as a Fellow of the Society for Industrial and Applied Mathematics in 2022, "for seminal contributions to scientific computation, numerical analysis, and applications in science and engineering".
At Duke, Dr. Zhao’s research interests are in computational and applied mathematics, including modeling, analysis, and the development of numerical methods for problems arising in science and engineering. More specifically, he works on: numerical analysis and scientific computing; multiscale, multiphase, multiphysics problems in wave propagation, fluids, and materials; inverse problems related to medical and seismic imaging; and computer vision and image processing.