CCM Scientist Jiequn Han Receives Early Career Prize from Society of Industrial and Applied Mathematics
The Society of Industrial and Applied Mathematics (SIAM) announced that Flatiron Center for Computational Mathematics (CCM) Research Scientist Jiequn Han will receive the 2025 SIAG/CSE Early Career Prize. Established in 2016, the prize is awarded to a post-Ph.D., early-career researcher for recent contributions to the fields of computational science and engineering. Han will accept the award at the 2025 SIAM Conference on Computational Science and Engineering in March.
Han receives the award for his outstanding theoretical, algorithmic and computational software contributions to deep learning, stochastic control, stochastic differential equations and molecular dynamics.
“Ultimately, my goal is to harness machine learning to develop more accurate, reliable and scalable scientific computing methods to address pressing challenges in science and technology,” says Han.
Han conducts research on machine learning for science, drawing inspiration from various scientific disciplines and focusing on solving high-dimensional problems in scientific computing, primarily those related to partial differential equations. He holds a Ph.D. in applied mathematics from Princeton University as well as a B.S. in computational mathematics and a B.A. in economics from Peking University.
“I am deeply honored to receive the SIAM Activity Group on CSE Early Career Prize,” says Han. “The previous recipients have made outstanding contributions to computational science and engineering, making this recognition especially meaningful to me. This award acknowledges my contributions to scientific computing through deep learning techniques, a journey I have pursued since the earliest stages of the field.”
“I have been fortunate to study and work at outstanding universities and research institutions, and I am immensely grateful for the support of my mentors, collaborators and colleagues. Inspired by this recognition, I look forward to continuing interdisciplinary research in scientific machine learning.”
