Network Systems Science and Advanced Computing

Richard E. Stearns

  • Distinguished Institute Professor
Richard Stearns headshot


Dr. Richard Stearns is a Distinguished Institute Professor with the Biocomplexity Institute and Initiative at the University of Virginia. He has made pioneering contributions to several different areas of computer science including theory of computation, formal languages, compilers, analysis of algorithms, database systems and game theory. In 1993, Dr. Stearns received the prestigious ACM Alan M. Turing Award in recognition of ground-breaking joint research with Professor Juris Hartmanis (Cornell University), which established the field of computational complexity.

He also holds the title of Distinguished Professor Emeritus in the Department of Computer Science at the University of Albany – State University of New York (SUNY), where he spent 22 years, including seven years as the Department Chair. Dr. Stearns has held additional academic appointments at several institutions including Hebrew University in Jerusalem; Mathematical Sciences Research Institute in Berkeley, California; and Rensselaer Polytechnic Institute in Troy, New York. He began his professional career in 1961 and spent 17 years with General Electric Research Laboratory, now known as GE Global Research, based in Schenectady, New York. 

Full CV

  • Computational complexity, Automata theory, Analysis of algorithms, Discrete dynamical systems, Game theory

    • B.A. Mathematics, Carleton College, 1958
    • Ph.D. Mathematics, Princeton University, 1961
Awards and Honors

Awards and Honors

  • ACM Alan M. Turing Award, 1993
  • Association for Computing Machinery (ACM) Fellow, 1994
  • Distinguished Professor, State University of New York, 1994
  • Associate Editor of SIAM Journal on Computing, 1972 to 1988


Curious Facts About Nested Canalyzing Functions, 5th Heidelberg Laureate Forum, 2017

Strategies for Extensive Form Games, 4th Heidelberg Laureate Forum, 2016

In the News

Contagions, severe weather, natural disasters, civil unrest – whatever data scientists are forecasting using network models, simulation-based methods are often the most effective, according to researchers at the University of Virginia Biocomplexity Institute and Princeton University, whose findings were published in the paper, “Fundamental Limitations on Efficiently Forecasting Certain Epidemic Measures in Network Models,” by PNAS (Proceedings of the National Academy of Sciences).

Epidemic Response

Researchers from UVA’s Biocomplexity Institute and School of Engineering and Applied Science, working with a team of multi-disciplinary scientists from around the world, have spent the last two years developing highly advanced computational models designed to inform policy makers, save lives and prepare for future global epidemics.