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Changnian Han is a Ph.D. candidate in the Department of Applied Mathematics and Statistics, Stony Brook University. He received his BS in double majors, physics and applied mathematics, from Stony Brook University in 2013 and MS degree in scientific computing from New York University in 2015. His research interests include efficient simulation algorithms for multiscale modeling, machine learning, optimization algorithms, and parallel computing. In past, he has worked on the development of the parallel simulated annealing algorithm. He is currently working on AI-guided adaptive time-stepping algorithms for efficient multiscale modeling of the platelet-mediated thrombosis initial formation in shear blood flow.
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