Changnian Han is a Ph.D. candidate in the Department of Applied Mathematics and Statistics, Stony Brook University. He received B.S. degree with double majors in applied mathematics and physics from Stony Brook University in 2013 and M.S. degree in scientific computing from New York University in 2015. His research interests include efficient time stepping algorithms, parallel computing, and machine learning strategies. He is currently working on the adaptive time stepping algorithms for more efficient multiscale simulation by machine learning techniques. He is a co-author of a published book on partial differential equations used in Professor Yuefan Deng's lecture.