1:40-2:00 pm              “Integrating machine learning in multiscale modeling for blood flow and platelet-mediated thrombosis initiation”

Peng Zhang, Stony Brook University

BIO: Peng Zhang is a research scientist at Stony Brook University. He received his Ph.D. in Applied Mathematics from Stony Brook University, after completing his M.S. in Parallel Computing and B.S. in Mathematics from Nankai University with honors. His research focuses on the development of efficient and accurate multiscale modeling (MSM) and machine learning (ML) approaches for modeling the blood flow and platelet mediated thrombosis. He published 30+ papers in applied mathematics, high performance computing, biomedical engineering, plus three book chapters. He has five awarded patents in US and China. He received two XSEDE Research Awards in 2014 and 2015. He presented 20+ lectures at international conferences and seminars such as BMES and SB3C. His key contributions in multiscale modeling include: (1) Development of multiscale particle-based modeling framework for blood flow and platelet activation, aggregation and adhesion, by interfacing coarse grained molecular dynamics (CGMD) and dissipative particle dynamics (DPD). (2) Development of a semi-unsupervised learning system for platelet segmentation at the submicron resolution and a ML model for synthesizing the sparse segmentation data to predictive model for enhancing the model assessment. (3) Development of multiple time stepping algorithms to handle 3–4 orders of magnitude disparity in the temporal scales between DPD and CGMD. The numerical experiments demonstrated 3000x reduction in computing time over standard methods for solving multiscale models. (4) Development of ML-based methods for adapting time step sizes to the underlying biomedical events in massive multiscale simulations. The computing times can be further cut by 20~75% automatically.