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Professor and Edward Frymoyer Chair of Information Sciences and Technology
Director, Center for Big Data Analytics and Discovery Informatics
Director, Artificial Intelligence Research Laboratory
Professor of Computer Science (Graduate Program)
Professor of Bioinformatics and Genomics (Graduate Program)
Professor of Neuroscience (Graduate Program)
Professor of Operations Research (Graduate Program)
Associate Director, Institute of Cyberscience
Executive Committee Member, North East Big Data Innovation Hub
Council Member, CRA Computing Community Consortium
Member, Electorate Nominating Committee, Section on Information, Computing, & Communication, American Association for the Advancement of Science
Faculty Affiliate, Institute for Cyberscience
Faculty Affiliate, Huck Institutes of the Life Sciences
University of Wisconsin - Madison, Ph.D. in Computer Science and Cognitive Science, 1990
University of Wisconsin - Madison, M.S. in Computer Science, 1989
Drexel University, M.S. in Electrical and Computer Engineering, 1984
Bangalore University, B.E. in Electronics Engineering, 1982
Dr. Vasant Honavar received his Ph.D. in Computer Science and Cognitive Science in 1990 from the University of Wisconsin Madison, specializing in Artificial Intelligence. In September 2013, Honavar joined the faculty of Penn State University where he currently serves as a Professor and Edward Frymoyer Chair of Information Science and Technology. He is also the founding Director of the Center for Big Data Analytics and Discovery Informatics and Associate Director of the Institute for Cyberscience. Honavar serves on the faculty of the Computer Science, Bioinformatics and Genomics, Neuroscience, and the Operations Research Graduate Programs. He is a member of the Computing Community Consortium Council, and the Executive Committe of the NSF North East Big Data Innovation Hub. He is also a member of the Electorate Nominating Committee of the Section on Information, Computing, & Communication of the American Association for the Advancement of Science (AAAS). In addition to research, graduate student supervision and teaching, he is responsible for developing new research and educational initiatives in Data Sciences and contributing to research initiatives in Life Sciences.
From 1990 to 2013, Honavar served on the faculty of Computer Science and of Bioinformatics and Computational Biology at Iowa State University (ISU). At ISU, he directed the Artificial Intelligence Research Laboratory (which he founded in 1990) and the Center for Computational Intelligence, Learning & Discovery (which he founded in 2005) and served as the associate chair (2001-2003) and chair (2003-2005) of the ISU Bioinformatics and Computational Biology Graduate Program, which he helped establish in 1999 with support from an Integrative Graduate Education and Research Training (IGERT) award.
During 2010-2013, Honavar served as a program director in the Information and Intelligent Systems Division of the Computer and Information Sciences and Engineering directorate of the National Science Foundation (NSF) during 2010-2013 while maintaining his research program at ISU. He led the Big Data Science and Engineering Program, established the NSF-OFR collaboration in Computational and Information Processing Approaches to and Infrastructure in support of, Financial Research and Analysis and Management, contributed to Smart and Connected Health, Information Integration and Informatics, Expeditions in Computing, Science of Learning Centers, Integrative Graduate Education and Research Training, Computing Research Infrastructure Programs.
Honavar's current research and teaching interests include Artificial Intelligence, Machine Learning, Bioinformatics, Big Data Analytics, Computational Molecular Biology, Data Mining, Discovery Informatics, Information Integration, Computational Neuroscience, Neuroinformatics, Knowledge Representation and Inference, Semantic Technologies, Social Informatics, Security Informatics, and Health Informatics.
Honavar has led research projects funded by NSF, NIH, and USDA that have resulted in foundational research contributions (documented in over 250 peer-reviewed publications) in Scalable approaches to building predictive models from large, distributed, semantically disparate data (big data); Constructing predictive models from sequence, image, text, multi-relational, graph-structured data; Eliciting causal information from multiple sources of observational and experimental data; Selective sharing of knowledge across disparate knowledge bases; Representing and reasoning about preferences; Composing complex services from components; and Applications in bioinformatics and computational biology (especially analysis and prediction of protein-protein, protein-DNA, and protein-RNA interactions and interfaces, B-cell and T-cell epitopes, post-translational modifications), Social network Informatics, Health Informatics, Energy Informatics, Security Informatics, and related areas.
Honavar has served as a principal or co-principal investigator on grants totaling approximately $20 million during 1990-2014 from the National Science Foundation, the National Institutes of Health, the US Department of Agriculture, and the US Department of Defense. He has extensive curriculum development and teaching experience in Artificial Intelligence, Machine Learning, Big Data Analytics, Data Mining, Knowledge Representation, Semantic Web, Bioinformatics and Computational Systems Biology. He also has substantial industrial consulting experience in Data Mining, Bioinformatics, and related topics.
Honavar has served on, or currently serves on the editorial boards of several journals including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Cognitive Systems Research, Machine Learning, the Journal of Bioinformatics and Biology Insights, the International Journal of Semantic Web and Information Systems, the International Journal of Computational Biology and Drug Design, the International Journal of Computer and Information Security, and the International Journal of Data Mining and Bioinformatics, and Webmed Central. Honavar will serve as a general co-chair of the IEEE International Conference on Big Data in 2014. Honavar has served on the program committees of major research conferences in artificial intelligence, data mining, and bioinformatics including the Conference on Artificial Intelligence (AAAI), International Conference on Machine Learning (ICML), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), SIAM Conference on Data Mining (SDM), IEEE Conference on Data Mining (ICDM), Intelligent Systems in Molecular Biology (ISMB), ACM Conference on Bioinformatics and Computational Biology (ACM-BCB), among others. Honavar has served as a charter member of the National Institutes of Health study section on Biological Data Management and Analysis (2002-2007). Honavar is a senior member of the Association for Computing Machinery (ACM), and of the Institute of Electrical and Electronic Engineers (IEEE) and a member of the Association for Advancement of Artificial Intelligence (AAAI), International Society for Computational Biology (ISCB) Society for Industrial and Applied Mathematics, and the American Association for the Advancement of Science. He currently serves on the Board of Directors for ACM Special Interest Group on Bioinformatics.
Honavar has received many awards and honors during his career including the National Science Foundation Director’s Award for Superior Accomplishment in 2013 for his leadership of the NSF Big Data Program, the National Science Foundation Director’s Award for Collaborative Integration in 2011, the Iowa Board of Regents Award for Faculty Excellence in 2007, the Iowa State University College of Liberal Arts and Sciences Award for Career Excellence in Research 2008, and the Iowa State University Margaret Ellen White Graduate Faculty Award in 2011. However, his proudest accomplishments are the 32 PhD students, 25 MS Students and several undergraduate researchers that he has worked with and mentored during his career.
Artificial Intelligence (Logical, probabilistic, causal, and decision-theoretic knowledge representation and inference, Neural architectures for knowledge representation and inference, Computational models of perception and action. Intelligent agents and multi-agent systems); Machine Learning, Data Mining, and Big Data Analytics: Statistical, information theoretic, linguistic and structural approaches to machine learning, Learning and refinement of Bayesian networks, causal networks, decision networks, neural networks, support vector machines, kernel classifiers, multi-relational models, language models (n-grams, grammars, automata), grammars; Learning classifiers from attribute value taxonomies and partially specified data; Learning attribute value taxonomies from data; Learning classifiers from sequential and spatial data; Learning relationships from multi-modal data (e.g., text, images), Learning classifiers from distributed data, multiple instance data, multiple label data; networked data; multi-relational data, linked open data (RDF), and semantically heterogeneous data; Incremental learning, Ensemble methods, multi-agent learning, curriculum-based learning; selected topics in computational learning theory; Bioinformatics, Computational Molecular Biology, and Computational Systems Biology: Analysis and prediction of protein-protein, protein-RNA, and protein-DNA interactions, interfaces and interaction networks, epitopes); Discovery Informatics: Computational models of scientific discovery; Discovery informatics infrastructure to integrate data, hypothesis, and knowledge-based inference, predictive modeling, experimentation, simulation, and hypothesis testing to provide an orderly formal framework and exploratory apparatus for science; Applications in computational systems biology and health informatics.
Knowledge Representation and Semantic Web: Probabilistic, grammatical, network based, relational, logical, epistemic knowledge representation; knowledge-based, network based, and probabilistic approaches to information integration; description logics, federated data bases – statistical queries against federated databases, knowledge bases – federated reasoning, selective knowledge sharing, services – service composition, substitution, and adaptation; epistemic description logics; secrecy-preserving query answering, representing and reasoning about qualitative preferences, representing and reasoning about causality (causal meta analyses, temporal causality, relational causal models, temporal relational causal models); Health Informatics: Predictive and causal modeling from health data (including electronic health records, genomic and contextual e.g., socio-economic and environmental data); Neuroinformatics: Construction, analysis, and modeling of brain networks (e.g., from fMRI data), with particular emphasis on structural changes in networks resulting from development, learning, aging, and disease processes.
Applied Informatics: Applications of artificial intelligence, machine learning, and big data analytics to problems in educational informatics, security informatics, social informatics and related areas; Other Topics of Interest: Biological Computation – Evolutionary, Cellular and Neural Computation, Complex Adaptive Systems, Sensory systems and behavior evolution, Language evolution, Developmental Robotics, Mimetic evolution; Computational Semiotics – Origins and use of signs, emergence of semantics; Computational organization theory; Computational Neuroscience; Computational models of creativity, Computational models of discovery.
Dr. Honavar's publications can be found at: http://faculty.ist.psu.edu/vhonavar/publist.html