BIOGRAPHICAL SUMMARY

After receiving a Ph.D. in Physics from the University of California, Davis in 1972 with dissertation research carried out at the Lawrence Livermore National Laboratory within the Department of Applied Science founded by Edward Teller, he advanced to the Principal Research Physicist rank at PPPL at an unprecedented pace (5 years) to become Lecturer with Rank & Title of Professor in the Department of Astrophysical Sciences and also a Fellow of the American Physical Society by 1979.  He was Head of the Theory Department at the Princeton Plasma Physics Laboratory (1992-2004) and was PPPL Chief Scientist (1997-2009).  Under his leadership, theoretical research at PPPL assimilated the rapidly emergent high performance computing capability.  At the national level, he led the Basic Science component of DOE’s Scientific Simulation Initiative (1997-98) which – together with the Computer Science & Enabling Technology component led by Prof. Rick Stevens of ANL and the University of Chicago -- provided the foundation for the establishment in 2000 of DOE’s interdisciplinary Scientific Discovery through Advanced Computing (SciDAC) Program which has continued into today. He guided the SciDAC portfolio on behalf of the Office of Fusion Energy Sciences as the Director of the Plasma Science Advanced Computing Institute (PSACI) for 7 years before then leading it’s transition into the FES/ASCR FSP Fusion Simulation Program -- a national multi-disciplinary, multi-institutional team of plasma scientists, computer scientists, and applied mathematicians which delivered for DOE the associated FSP program definition and plan.  From 2009 - 2012, he was appointed by U.S. Energy Secretary Steven Chu to be the first FES scientist to serve on DoE’s Advanced Scientific Computing Advisory Committee (ASCAC) from 2009-2012.  

Prof. Tang is internationally recognized for his distinguished record of scientific achievements over 40 years, including peer-reviewed publications highlighting physical science discoveries, mathematical physics formalism, and associated innovative computational applications dealing with electromagnetic kinetic plasma behavior in complex geometries – that presently include over 200 papers in Nature, Science, Phys. Rev. Letters, Phys. Fluids/Plasmas, Nuclear Fusion, etc. and an “h-index” or “impact factor” of 61 on Google Scholar Citations, including over 15,000 total citations.  He received the Chinese Institute of Engineers-USA Distinguished Achievement Award “for his outstanding leadership in fusion research and contributions to fundamentals of plasma science” (Oct. 2005).   In the HPC area, he received the "High Performance Computing (HPC) Innovation Excellence Award" from the International Data Corporation (IDC) “for using high-end supercomputing resources to carry out advanced simulations for the first time of confinement physics in large-scale magnetic fusion energy (MFE) plasmas with unprecedented phase-space resolution and long temporal duration to deliver important new scientific insights.”  More recently, he was the recipient of the 2018 NVIDIA Global Impact Award in March, 2018 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.”  Since then he led the Artificial Intelligence/Deep Learning Project that produced the high-profile publication in NATURE (April, 2019) on "Predicting Disruptive Instabilities in Controlled Fusion Plasmas Through Deep Learning" -- a seminal contribution of the first US FES AI/DL/code validated vs. extensive experimental data and highlights the timely emergence of the major R&D growth area of AI/DL/ML with an exemplar from the grand challenge area of clean energy via magnetic fusion.  This recent example of Tang’s pioneering scientific leadership in FES is also reminiscent of his key role in mentoring and participating in the high-profile breakthrough PIC simulation Science paper in 1998 on “Turbulent Transport Reduction by Zonal Flows: Massively Parallel Simulations” led by his former PhD student Prof. Zhihong Lin of UC Irvine which ushered in the emergence of plasma physics/FES as a major tool for scientific discovery using high performance computing.  He was also the Director and Principal Investigator (PI) of the Intel Parallel Computing Center that was awarded to Princeton University’s “PICSciE” -- the interdisciplinary institute for computational science & engineering that he helped co-found (2010), served as Associate Director (2003-2009) and continues on it’s current Executive Board.  He is currently the PI of the Early Science Project (ESP) on “Accelerated Deep Learning Discovery in Fusion Energy Science” that was selected for Argonne National Laboratory’s targeted AURORA Exascale system.

With regard to academic contributions, Prof. Tang has taught for nearly 40 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 (Prof. Zhihong Lin) and 2005 (Prof. Hong Qin).  Moreover, he has personally mentored/supervised -- outstanding recipients from DOE-SC’s premier Computational Science Graduate Fellowship Program, including Hal Finkel (Yale), Julian Kates-Harbeck (Harvard), Kyle Felker (Princeton), and currently Jesse Rodriguez (Stanford) and Ian Desjardin (Maryland).

He is internationally recognized for his continuing contribution of new ideas which have fostered creativity and promoted cross-disciplinary fertilization in multiple areas of research and high-performance computing technology as well as for his fundamental contributions in educating a new generation of researchers for which he is an example to emulate.  Prof. Tang has supervised many prominent Ph.D students including Zhihong Lin, Hong Qin, Mehmet Artun, Lei Shi, Leland Ellison,  ..... and postdocs including Bei Wang, Kyle Felker, Ge Dong, Greg Rewoldt, J. C. Adam, Swadesh Mahajan, Bruce Cohen, Robert Laquey,..... They have helped to contribute to a school of thought that has been shaping a generation of cross-disciplinary theoretical/computational plasma/fusion energy scientists.

William Tang's CV

Bill Tang's current h-index on Google Scholar Citations is 62 with over 17,000 citations -- including 43 papers each with at least 100 citations

William Tang  -- Google Scholar Citations

15 most impactful publications 

NOTABLE AWARDS RECEIVED

Professor William Tang has made original contributions in basic plasma physics, fusion energy science, interdisciplinary computational science, and now Artificial Intelligence/Deep Learning.  Associated prominent awards include:

• the 2005 Chinese Institute of Engineers-USA Distinguished Achievement Award "for outstanding leadership in fusion research and contributions to fundamentals of plasma science."

• the 2018 Global Impact Award from NVIDIA Corporation for "groundbreaking work in using GPU-accelerated computing to unleash deep learning neural networks for dramatically increasing the accuracy and speed in predicting disruptions in fusion systems." 

• the 2013 High Performance Computing Innovation Excellence Award from International Data Corporation for "using high-end supercomputing resources to carry out advanced simulations for the first time of confinement physics in large-scale magnetic fusion energy plasmas with unprecedented phase-space resolution and long temporal duration to deliver important new scientific insights."