John Grefenstette Receives Evolutionary Computation Pioneer Award

John Grefenstette, PhD, director of GSPH’s Public Health Dynamics Laboratory and professor in the Department of Biostatistics, has been chosen to receive the Evolutionary Computation Pioneer Award from the Institute of Electrical and Electronics Engineers (IEEE) Computational Intelligence Society. He will be presented with the award during the IEEE World Congress on Computational Intelligence, July 18-23, 2010, in Barcelona, Spain.

Grefenstette received his bachelor’s degree in mathematics and philosophy from Carnegie Mellon University and his PhD in computer science from the University of Pittsburgh. He was on the faculty of the Computer Science Department at Vanderbilt University, after which he became section head for machine learning in the Navy Center for Applied Research in Artificial Intelligence at the Naval Research Laboratory (NRL) in Washington DC. In 1997, he joined the Institute for Biosciences, Biotechnology, and Bioinformatics at George Mason University, where he served as chair of the Bioinformatics and Computational Biology Department and as assistant dean of the School for Computational Sciences.

He started working on genetic algorithms under the direction of Ken De Jong at the University of Pittsburgh in 1978. In the early 1980s, he developed one of the first open source software packages for genetic algorithms, called GENESIS. This package has been widely used by numerous researchers in developing genetic algorithms for engineering design optimization problems, combinatorial optimization problems such as routing and scheduling, and biological applications including image procession, molecular docking, and protein folding. As head of machine learning at NRL, he developed methods using models of biological systems to design intelligent systems. As part of this research, he authored a software package called SAMUEL, that uses genetic algorithms and other machine learning methods to evolve rules that control the behavior of autonomous robots. His research demonstrated that evolutionary methods can enable intelligent systems to improve their own performance over time and adapt to complex and changing environmental conditions. His research team received the NRL Technology Transfer Award for this work in 1993, as well as two Alan R. Berman Research Publication Awards.

He has published more than 85 research papers, and has presented many invited talks and keynote addresses. He was the organizer and program chair for the first two International Conferences on Genetic Algorithms, in 1985 and 1987, and served as the editor for the associated proceedings for these two conferences. He was one of the founders of the International Society for Genetic Algorithms, and a founding associate editor for the journal Evolutionary Computation, published by MIT Press since 1993. He is on the editorial board for the journal Adaptive Behavior, and served as an editor of the journal Machine Learning from 1995 through 1997. He has served on numerous review panels for granting agencies including NIH, NSF, ONR and DARPA. He also served on many program committees for major conferences, including the International Joint Conference on Artificial Intelligence, the AAAI Conferences, and the International Conference on Machine Learning.

For the past 12 years, he has concentrated on biological applications of high-performance computing. His recent research activities include the analysis of the evolution and structure of gene regulatory networks, and applied bioinformatics projects such as the International Bovine HapMap Consortium, for which his research team contributed to the analysis of genetic diversity of the world’s cattle breeds. His current research interests focus on modeling and simulation of infectious diseases, machine learning, and high-performance computing applications to public health. In 2009, he became director of the Public Health Dynamics Laboratory at the University of Pittsburgh, where he is using genetic algorithms and other methods to explore the policy space of interventions in global epidemics, including vaccination strategies, social distancing measures, and other public health options.


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