Bio

Ph.D., University of Cambridge, 1967

"In the next 10 years, I think we are in for an incredible revolution in the way science is done. There will be a huge number of new methods for discovery, and I am driven to make and help make these discoveries. I want to make progress with theoretically innovative advanced computing methods."

 

Research interests include:

 

Network Systems Science, High Performance Computing and Clouds, AI for Science, Deep Learning for data analytics and simulation surrogates, The Interface of Data Engineering and Data Science with Data Systems

 

 

Fox received a Ph.D. in Theoretical Physics from Cambridge University, where he was Senior Wrangler. He is now a Professor in the Biocomplexity Institute & Initiative and Computer Science Department at the University of Virginia.  He previously held positions at Caltech, Syracuse University, Florida State University, and Indiana University. after being a postdoc at the Institute for Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge. He has supervised the Ph.D. of 75 students. He has an hindex of 85 with over 41,000 citations. He received the High-Performance Parallel and Distributed Computing (HPDC) Achievement Award and the ACM - IEEE CS Ken Kennedy Award for Foundational contributions to parallel computing in 2019. He is a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface between computing and applications. He is currently active in the Industry consortium MLCommons/MLPerf.
 

Awards

  • ACM-IEEE CS Ken Kennedy Award 2019
  • High-Performance Parallel and Distributed Computing Achievement Award 2019
  • ACM Fellow 2011
  • Fellow of the American Physical Society 1990
  • Alfred P. Sloan Foundation Fellowship 1973-1975
  • Mayhew Prize, Applied Mathematics, Cambridge 1964
  • Senior Wrangler, Part III Mathematics, Cambridge 1964

Research Interests

  • Network Systems Science
  • High Performance Computing and Clouds
  • AI for Science
  • Deep Learning for data analytics and simulation surrogates
  • The interface of Data Engineering and Data Science with Systems

Selected Publications

  • "Earthquake Nowcasting with Deep Learning", GeoHazards 2022, 3(2), 199-226 ABS G. C. Fox, John B. Rundle, A. Donnellan, B. Feng
  • "AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates." arXiv:2010.03757v1. Journal data science 19(2021), no. 2, 293-313, DOI 10.6339/21-JDS1007 G.C. Fox, G. von Laszewski, F. Wang, S. Pyne (2020)
  • “HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks”, 2021 IEEE 14th International Conference on Cloud Computing (CLOUD) page 228 S. Kamburugamuve, C. Widanage, N. Perera, V. Abeykoon, A. Uyar, T. . Kanewala, G. Von Laszewski, G. Fox
  • “Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations” Frontiers in Physiology 12 , 908. 24 June 2021 ABS J. Q. Toledo-Marín, G. C. Fox, J. P. Sluka, J. A. Glazier
  • “ Parallel Computing Works!” San Mateo: Morgan Kaufman; 1994 G. Fox, P. Messina, R. Williams
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