Prof. William M. (Bill) Tang of Princeton University is Lecturer with Rank of Professor in the Department of Astrophysical Sciences at Princeton University. He is also Participating Faculty at the Center for Statistics and Machine Learning, Executive Committee member for the Princeton Institute for Computational Science & Engineering (PICSciE), and Principal Research Physicist at the Princeton Plasma Physics Laboratory, the DOE national laboratory for Plasma Physics and Fusion Energy research -- where he served as Chief Scientist from 1997 to 2009. A Fellow of the American Physical Society, he has received awards including NVIDIA Corporation’s 2018 Global Impact Award “for groundbreaking work in using GPU-accelerated computing to unleash deep learning neural networks for dramatically increasing the accuracy and speed in predicting dangerous disruptions in fusion systems,” and the IEEE Computer Society’s 2024 Sidney Fernbach Memorial Award – “for pioneering contributions to fusion energy research accelerated by high-performance computing and deep learning.” He was also previously honored with the Distinguished Achievement Award (2006) for “outstanding leadership in fusion research and contributions to fundamentals of plasma science” by the Chinese Institute of Engineers-USA. His scientific leadership roles have included serving on the International Scientific Advisory Committee for Switzerland’s National Supercomputing Center (CSCS) and as the current Chairman for their “PASC” Scientific Advisory Board. He is also the PI (principal investigator) for projects including the “Accelerated Deep Learning Discovery in Fusion Energy Science” Early Science Project at the Argonne National Laboratory on their current Exascale system AURORA. He was also the PI for the Intel Parallel Computing Center of Excellence awarded to “PICSciE” at Princeton University (2014-2018). Professor Tang has been the author of more than 200 peer-reviewed journal publications with over 17,000 Google Scholar citations, and his PhD students include recipients of the US Presidential Early Career Award for Scientists and Engineers in 2000 and 20005. He is internationally recognized for expertise in the mathematical formalism as well as associated computational applications dealing with electromagnetic kinetic plasma behavior in complex geometries. Professor Tang has been the author of more than 200 peer-reviewed journal publications with over 17,000 Google Scholar citations, and his PhD students include recipients of the US Presidential Early Career Award for Scientists and Engineers in 2000 and 2005. He was also the U.S. PI for the G8 Research Council’s “Exascale Computing for Global Scale Issues” Project in Fusion Energy - an NSF-funded international HPC collaboration (2011-2014), and is currently Distinguished Visiting Professor, Center for High Performance Computing and NVIDIA Center of Excellence, Shanghai Jiao Tong University, Prof. Tang has taught for over 30 years at Princeton U. and has supervised numerous Ph.D. students, including recipients of the Presidential Early Career Award for Scientists and Engineers in 2000 and 2005. He is currently Distinguished Visiting Professor, Center for High Performance Computing and NVIDIA Center of Excellence, Shanghai Jiao Tong University, Co-PI of the Early Science GTC Application Project (2015-present) in Oak Ridge National Laboratory’s CAAR Program leading to the 200 PF “Summit” Supercomputing System, and the Principal Investigator (PI) and Head of the Intel Parallel Computing Center that was recently awarded to the Princeton Institute for Computational Science & Engineering (PICSciE) at Princeton University (May, 2016). Selected Publications [1] "Scientific Discovery in Fusion Plasma Turbulence Simulations at Extreme Scale” William Tang, Bei Wang & Stephane Ethier, Comput. Sci. Eng. 16, 44 (2014). [2] Julian Kates-Harbeck, Alexey Svyatkovskiy, and William Tang, "Predicting Disruptive Instabilities in Controlled Fusion Plasmas Through Deep Learning," NATURE 568, 526 (2019) [3] William Tang, Ge Dong, Jayson Barr, Keith Erickson, Rory Conlin, Dan Boyer, Julian Kates-Harbeck, Kyle Felker, Cristina Rea, N. C. Logan, et al., “Implementation of Ai/Deep Learning Disruption Predictor into a Plasma Control System,” arXiv preprint arXiv:2204.01289, 2021; Updated Peer-Reviewed Journal Publication with “Explainable AI/ML Focus” in CONTRIBUTIONS TO PLASMA PHYSICS, Special Issue dedicated to Machine Learning accepted for publication (May, 2023). [4] Ge Dong, et al., Deep Learning-based Surrogate Model for First-principles Global Simulations of Fusion Plasmas, NUCLEAR FUSION 61 126061 (2021).