Positions Held: Dr. Vasant Honavar received his Ph.D. in 1990 from the University of Wisconsin Madison, specializing in Artificial Intelligence. Honavar is currently a Professor of Information Sciences and Technology (IST), Computer Science and Engineering, Bioinformatics and Genomics, Neuroscience and Operations Research at Pennsylvania State University. He currently holds the Edward Frymoyer Endowed Chair of IST. He is also the founding Director of the Center for Big Data Analytics and Discovery Informatics, Associate Director of the Institute for Cyberscience, Co-Director of the NIH-funded Biomedical Data Sciences PhD program, and Informatics lead (research) for the NIH-funded Clinical and Translational Sciences Institute. In 2016, Honavar was appointed as the Sudha Murty Distinguished Visiting Chair of Neurocomputing and Data Science at the Indian Institute of Science. Prior to joining Pennsylvania State University, Honavar served on the faculties of Computer Science, Bioinformatics and Computational Biology, Neuroscience, and Human-Computer Interaction at Iowa State University (1990-2013) and as a program director in the Information and Intelligent Systems Division of the National Science Foundation (NSF) (2010-2013) where he led the Big Data Science and Engineering Program and contributed to multiple other programs.
Research: Honavar's current research and teaching interests include artificial intelligence (especially machine learning, causal inference, knowledge representation), computer science, data sciences, cognitive and brain sciences, and applied informatics (especially bioinformatics, health informatics). Honavar's research has resulted in foundational contributions in these areas (documented in over 250 peer-reviewed publications, that have been cited approximately 13,000 times, or over 460 citations on average per year during 1990-2018) (see below for details). Honavar has served as a principal or co-principal investigator on grants totaling over $60 million during 1990-2018 from the National Science Foundation, the National Institutes of Health, the US Department of Agriculture, the US Department of Defense, and the National Security Agency.
Teaching and Curriculum Development, and Student Mentoring: Honavar has developed and taught graduate and undergraduate courses in Computer Science, Artificial Intelligence, Machine Learning, Neural Computation, Data Sciences, Bioinformatics, and related areas. He has directly mentored the research-based training of 10 Postdoctoral Research Associates, 33 PhD students (in Computer Science, Bioinformatics, and Neuroscience) and 30 MS Students, and many undergraduates all of whom have gone on to pursue successful careers in academia or industry. He is currently working with 1 Postdoctoral Research Associate, 8 PhD students and 2 MS Students(drawn from Computer Science and Engineering, Information Sciences and Technology, Bioinformatics and Genomics, and Neuroscience Programs).
Leadership and Professional Service: At Pennsylvania State University, Honavar led the establishment of the Center for Big Data Analytics and Discovery Informatics, an interdisciplinary research center that brings together faculty with complementary expertise in Computing, Informatics, Statistics, and data-rich disciplines (life sciences, health sciences, brain sciences, among others) to pursue fundamental as well as translational research in the data sciences. He co-led the establishment of a new innovative inter-college undergraduate degree program in Data Sciences, that combines foundational training in Computing, Informatics, and Statistics together with exposure to least one application domain (e.g., Life Sciences, Social Sciences, Business, Health Sciences, Physical Sciences); an innovative Biomedical Data Sciences interdisciplinary PhD training program in Biomedical Data Sciences (funded by an NIH BD2K T32 predoctoral training grant), drawing on faculty expertise across 5 different colleges (Science, Information Sciences and Technology, Engineering, Health and Human Development, Medicine) across two different campuses (University Park, Hershey). Honavar served as a member of the Computing Community Consortium Council (CCC) where he chaired the Convergence of Data and Computing Task Force (2015-2017) and served on Artificial Intelligence and Health IT task forces. He continues to serve as an external member of the Data and Computing Task force. Dr. Honavar serves on the Executive Committee 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). Honavar has served on, or currently serves on the editorial boards of several journals including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Springer Open Journal of Big Data, Journal of Computational Systems Biology, 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 among others. Honavar served as a general co-chair of the 2014 IEEE International Conference on Big Data in 2014 and the program co-chair of 2014 IEEE Conference on Bio and Medical Informatics. Honavar regularly serves on the program committees of major research conferences in artificial intelligence, machine learning, 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 on multiple NIH Study Sections and including a charter member of the National Institutes of Health study section on Biological Data Management and Analysis (2002-2007). He currently serves on the Board of Directors for ACM Special Interest Group on Bioinformatics.
Selected Honors and Awards: Honavar was elected as Fellow of the American Association for Advancement of Science (AAAS) for his contributions to research and leadership in data science in 2018. Honavar has received many other awards and honors during his career including the Association for Computing Machinery (ACM) Distinguished Member award for outstanding scientific contributions to computing, 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 Pennsylvania State University College of Information Sciences and Technology Senior Faculty Excellence in Research Award in 2016, the Sudha Murty Distinguished Visiting Chair in Neuroscomputing and Data Science at the Indian Institute of Science in 2016, the Edward Frymoyer Endowed Chair in Information Sciences and Technology at Pennsylvania State University in 2013, 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 33 PhD students, 30 MS Students, and many undergraduate researchers that he has worked with and mentored during his career.
My long-term research intersts span artificial intelligence (especially machine learning, causal inference, knowledge representation), computer science, data sciences, cognitive and brain sciences, and applied informatics (especially bioinformatics, health informatics). My research is driven by fundamental scientific questions or important practical problems in areas of societal or national priority (e.g., health):
- How can we efficiently build predictive models from “big data”)?
- How can we elicit causal relations from disparate observational and experimental studies? How can we ensure that AI systems are fair, explainable, and accountable?
- How can we build predictive models from richly structured (e.g., sequences, images, networks), multi-modal (multi-view), and longitudinal data?
- How can computational abstractions of scientific artifacts and processes mediate scientific collaborations that transcend disciplinary and organizational boundaries and accelerate science?
- How can we efficiently represent and reason about preferences to support individual and collective decision making? How can we query and reason with federated data and knowledge bases, including those that contain sensitive data or knowledge?
- How can we learn to predict health outcomes and perform interventions from clinical, biomedical, environmental, socio-demographic, behavioral, and other types of data? How can we predict protein-protein, protein-RNA, protein-DNA interactions, interfaces, and complexes?
- How can we construct, compare and analyze multi-scale, models of molecular networks that orchestrate cellular development, differentiation, immune response, etc.?
- How can we model, construct, compare, and analyze brain networks to discover features of fMRI-derived brain networks that characterize functional differences associated with aging, development, and disease?
- How can we build robust intelligent agents that incorporate multiple facets of intelligence to augment and extend human intellect and abilities?
My research contributions have spanned Artificial Intelligence, Data Science, Machine Learning, Knowledge Representation, Causal Inference, and applications in Bioinformatics, Health Informatics, Social Informatics, Brain Informatics, Security Informatics, and Cognitive Science. Some of my most recent work has focused on:
- Scalable algorithms for building predictive models from large, distributed, semantically disparate data (big data)
- Algorithms for constructing predictive models from sequence, image, text, multi-relational, graph-structured data
- Algorithms for building predictive models from high dimensional data using feature selection, feature abstraction, and representation learning
- Methods for selective sharing of knowledge across autonomous knowledge bases (including knowledge base federation, secrecy-preserving query answering)
- Theoretically sound yet practically useful approaches to functional and non-functional specification driven composition of complex services from components
- Expressive languages for representing, and model checking approaches to reasoning with, qualitative preferences
- Algorithms for eliciting causal effects from disparate sources of observational and experimental data
- Scalable algorithms and software for comparative analyses of large bio-molecular networks
- Machine learning approaches to analysis and prediction of macromolecular interactions and interfaces
My ongoing research is focused on:
- Computational abstractions of scientific artifacts (e.g., data, knowledge, hypotheses), and universes of scientific discourse (e.g., biology), and scientific processes (e.g., hypothesis generation, predictive modeling, experimentation, simulation, and hypothesis testing)
- Computational infrastructure collaborative, interdisciplinary and transdisciplinary science
- Design and analysis of algorithms for predictive modeling from very large, high dimensional, richly structured, multi-modal, longitudinal data
- Elucidation of causal relationships from disparate experimental and observational studies
- Design and analyses of accountable, explainable, and fair AI systems
- Analysis and prediction of macromolecular interactions, elucidation of complex biological pathways e.g., those involved in immune response, development, and disease;
- Predictive and causal modeling of individual and population health outcomes from behavioral, biomedical, clinical, environmental, socio-demographic data
- Predictive and causal modeling of behavioral and cognitive systems in naturalistic settings
- Modeling the structure, activity, and function of brain networks from fMRI and other types of data
- Representing and reasoning with qualitative multi-stakeholder preferences