Daniel J. Rosenkrantz

  • Distinguished Institute Professor
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Bio

Dr. Daniel J. Rosenkrantz is a Distinguished Institute Professor with the Biocomplexity Institute and Initiative at the University of Virginia. He has made fundamental contributions to many areas of computer science including formal languages, theory of computation, compilers, analysis of algorithms, database systems, very large-scale integration, fault-tolerant computing, operations research and discrete dynamical systems. He is also listed in several notable “Who’s Who” lists for his many scientific achievements.  

Dr. Rosenkrantz also holds the title of Leading Professor Emeritus in the Department of Computer Science at the University of Albany – State University of New York (SUNY), where he spent 28 years as Leading Professor, including six years as the Department Chair. He was a Visiting Research Scientist at the Biocomplexity Institute of Virginia Tech for eight years before joining the Biocomplexity Institute at the University of Virginia.  Dr. Rosenkrantz has also held positions at several notable companies, including as a Principal Computer Scientist with Phoenix Data Systems and as an Information Scientist at the General Electric Research Laboratory, now known as GE Global Research, based in Schenectady, New York. He served as an Associate Editor and later as the Editor-in-Chief of the Journal of the Association for Computing Machinery (ACM). He was elected a Fellow of ACM in 1995. He also received the ACM SIGMOD Contributions Award in 2001 in recognition of his contributions to the area of Database Systems.

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DBLP Publications Listing

  • Design and analysis of algorithms, Database systems, Complexity theory, Compiler construction, High performance computing, Discrete dynamical systems, Software engineering

    • B.S. Electrical Engineering, Columbia University, 1963 
    • M.S. Electrical Engineering, Columbia University 1964
    • Ph.D. Electrical Engineering, Columbia University, 1967

  • Awards

    Accolades

    • Who’s Who in America, since 1990
    • American Men & Women of Science, since 1992
    • Who’s Who in American Education, since 1996
    • Who’s Who in Science and Engineering, since 1996
    • Who’s Who in the East, since 1997

    Academic Honors

    • Sigma Xi
    • Eta Kappa Nu
    • Tau Beta Pi
    • National Science Foundation Cooperative Graduate Fellow
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Simulations

Simulation-based Forecasts Best, Study Finds

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).

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Epidemic Response

Bold Predictions for a Brave New World

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.