Department of Applied Mathematics and Statistics
Physics A-135
Stony Brook, NY 11794-2424
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Biography
Yuefan Deng received his BA (1983) in Physics from Nankai University and his Ph. D. (1989) in Theoretical Physics from Columbia University. He is currently a professor of applied mathematics (since 1998) at Stony Brook University in New York. As an adjunct or visiting professor, he has worked at Columbia University, National University of Singapore, Nanyang Technological University, and IBM T. J. Watson Research Laboratory. Prof. Deng’s research covers parallel computing, molecular dynamics, Monte Carlo methods, and biomedical engineering. The latest focus is on the multi-scale modeling of platelet activations and aggregations (funded by US NIH, New York State, and IBM) on supercomputers, parallel optimization algorithms, and supercomputer network topologies. He publishes widely in diverse fields of physics, computational mathematics, and biomedical engineering. He has supervised nearly 30 doctoral theses and taught more than 15,000 students (as of 2021) ordinary differential equations and numerical analysis. He is the recipient (2016) of the State University of New York Chancellor’s Award for Excellence in Teaching. During the AY ’21-’22, he is a visiting professor of mathematics at New York University in Abu Dhabi.
Research
Yuefan Deng’s research spans two highly synergistic aspects of computational science: algorithm development and scientific applications. At the forefront of data science and machine learning, he designs algorithms to manage multiple spatial and temporal scales, enabling optimal simulations of complex phenomena such as human platelet dynamics and the structures and functions of proteins of varying sizes. He also implements these algorithms on modern supercomputers equipped with tens of thousands of CPUs and GPUs. For years, Deng has worked on advancing and accelerating simulated annealing algorithms—both sequential and parallel—expanding their applications across engineering, finance, and medicine.