Vasant Honavar

Vasant G. Honavar is an Indian born American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor.

Vasant Honavar
Nationality USA
Alma materUniversity of Wisconsin
Drexel University
B.M.S. College of Engineering, Bangalore University
AwardsAAAS Fellow
Scientific career
FieldsComputer science, Artificial intelligence, Machine learning, Data Science, Bioinformatics, Big data, Causal Inference, Informatics, Knowledge Representation, Computational biology, Cognitive science, Health informatics, Neuroinformatics, Network Science, Biomedical Informatics
InstitutionsIowa State University
National Science Foundation
Pennsylvania State University
Doctoral advisorLeonard Uhr
InfluencesLeonard Uhr
Larry Travis
Deborah Joseph
Helen M. Berman

Early life and education

Vasant Honavar was born at Poona, India to Bhavani G. and Gajanan N. Honavar. He received his early education at the Vidya Vardhaka Sangha High School and M.E.S. College in Bangalore, India. He received a B.E. in electronics engineering from B.M.S. College of Engineering in Bangalore, India in 1982, when it was affiliated with Bangalore University, an M.S. in electrical and computer engineering in 1984 from Drexel University, and an M.S. in computer science in 1989, and a Ph.D. in 1990, respectively, from the University of Wisconsin–Madison, where he studied Artificial Intelligence and worked with Leonard Uhr.

Career

During 1990–2013, Honavar was a professor of computer science at Iowa State University where he led the Artificial Intelligence Research Laboratory which he founded in 1990. From 2006 to 2013, he served as the director of the Iowa State University Center for Computational Intelligence, Learning and Discovery which he founded in 2006. He was instrumental in establishing the Iowa State University interdepartmental graduate program in Bioinformatics and Computational Biology (and served as its Chair during 2003–2005).

During 2010–2013, Honavar served as a Program Director in the Information Integration and Informatics program in the Information and Intelligent Systems Division of the Computer and Information Science and Engineering Directorate of the US National Science Foundation where he led the Big Data Program[1] and contributed to several core and cross-cutting programs.

In 2013, Honavar joined the faculty of Penn State College of Information Sciences and Technology[2] at Pennsylvania State University where he currently holds the Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence [3] and previously held the Edward Frymoyer Endowed Chair in Information Sciences and Technology. He serves on the faculties of the graduate programs in Computer Science, Informatics, Bioinformatics and Genomics, Neuroscience, Operations Research, Public Health Sciences, and of an undergraduate program in Data Science. Honavar serves as the Director of the Artificial Intelligence Research Laboratory,[4] Associate Director of the Institute for Computational and Data Sciences[5] and the Director of the Center for Artificial Intelligence Foundations and Scientific Applications[6] at Pennsylvania State University. Honavar serves on the Leadership Team of the Northeast Big Data Innovation Hub.[7] Honavar served on the Computing Research Association's Computing Community Consortium Council during 2014-2017,[8][9] where he chaired the task force on Convergence of Data and Computing, and was a member of the task force on Artificial Intelligence.

In 2015, Honavar was elected to the Electorate Nominating Committee of the Information, Computing, and Communication Section of the American Association for the Advancement of Science.[10] In 2016, Honavar was selected as the first Sudha Murty Distinguished Visiting Chair of Neurocomputing and Data Science by the Indian Institute of Science, Bangalore, India.[11] In 2018, Honavar was named a Distinguished Member of the Association for Computing Machinery for his outstanding scientific contributions to computing;[12] and elected a Fellow of the American Association for the Advancement of Science for his distinguished research contributions and leadership in data science.[13]

Honavar has held visiting professorships at Carnegie Mellon University, the University of Wisconsin–Madison, and at the Indian Institute of Science.

Research

Honavar has made substantial research contributions in artificial intelligence, machine learning, causal inference, knowledge representation, neural networks, semantic web, big data analytics, and bioinformatics and computational biology. He is program chair of the Association for the Advancement of Artificial Intelligence(AAAI)'s 36th Conference on Artificial Intelligence.[14] He has published over 300 research articles, including many highly cited ones,[15][16] as well as several books on these topics.[17] His recent work has focused on federated machine learning algorithms for constructing predictive models from distributed data and linked open data, learning predictive models from high dimensional longitudinal data, estimating causal effects from complex data, reasoning with federated knowledge bases, detecting algorithmic bias, big data analytics, analysis and prediction of protein-protein, protein-RNA, and protein-DNA interfaces and interactions, social network analytics, health informatics, secrecy-preserving query answering, representing and reasoning about preferences, and causal inference and meta analysis.

Honavar has directly supervised the dissertation research of 38 Ph.D. students.[18]

Honavar has been engaged in fostering national and international scientific collaborations in Artificial Intelligence, Data Sciences, and their applications in addressing national, international, and societal priorities in accelerating science, improving health, transforming agriculture through partnerships that bring together academia, non-profits, and industry.[19][20][21][22][23][24][25]

Honavar is also active in making the science policy case for major national research initiatives such as AI for accelerating science [26] and AI for combating the epidemic of diseases of despair.[27]

Selected publications

Books
  • Vasant Honavar and Leonard Uhr. (Ed.) Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. New York: Academic Press. 1994. ISBN 0-12-355055-6
  • Vasant Honavar and Giora Slutzki (Ed). Grammatical Inference. Berlin: Springer-Verlag. 1998. ISBN 3-540-64776-7
  • Mukesh Patel, Vasant Honavar and Karthik Balakrishnan (Ed). Advances in the Evolutionary Synthesis of Intelligent Agents. Cambridge, MA: MIT Press. 2001. ISBN 0-262-16201-6
  • Ganesh Ram Santhanam, Samik Basu, and Vasant Honavar. Representing and Reasoning with Qualitative Preferences: Tools and Applications. Lecture #31, Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers. 2016. doi:10.2200/S00689ED1V01Y201512AIM031, ISBN 978-1-62705-839-1

Articles

Causal Inference

  • Bui, N., Yen, J., and Honavar, V. (2016). Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network. IEEE Transactions on Computational Social Systems. doi:10.1109/TCSS.2016.2591880.
  • Bareinboim, E., Lee, S., Honavar, V. and Pearl, J. (2013). Transportability from Multiple Environments with Limited Experiments. In: Advances in Neural Information Systems (NIPS) 2013. pp. 136–144.

Machine learning, neural networks, and deep learning

  • Liang, J., Xu, D., Sun, Y., and Honavar, V. (2020). LMLFM: longitudinal multi-level factorization machine. AAAI 2020: pp. 4811–4818
  • Hu, J., Liang, J., Kuang, Y. and Honavar, V. (2018). A user similarity-based Top-N recommendation approach for mobile in-application advertising. Expert Systems With Applications. Vol. 111. pp. 51–60.
  • Silvescu, A. and Honavar, V. (2013). Abstraction Super-structuring Normal Forms: Towards a Theory of Structural Induction. In: Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence (pp. 339–350). Springer Berlin Heidelberg.
  • Koul, N. and Honavar, V. (2010). Learning in the Presence of Ontology Mapping Errors. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. pp. 291–296. ACM Press.
  • Bromberg, F., Margaritis, D., and Honavar, V. (2009). Efficient Markov Network Structure Discovery from Independence Tests. Journal of Artificial Intelligence Research. Vol. 35. pp. 449–485.
  • Silvescu, A., Caragea, C. and Honavar, V. (2009). Combining Super-structuring and Abstraction on Sequence Classification. IEEE Conference on Data Mining (ICDM 2009).
  • Zhang, J.; Kang, D.K.; Silvescu, A.; Honavar, V. (2006). "Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data". Knowledge and Information Systems. 9 (2): 157–179. doi:10.1007/s10115-005-0211-z. PMC 2846370. PMID 20351793.
  • Caragea, D.; Silvescu, A.; Honavar, V. (2004). "A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees". International Journal of Hybrid Intelligent Systems. 1 (2): 80–89. doi:10.3233/HIS-2004-11-210. PMC 2846376. PMID 20351798.
  • Polikar, R., Udpa, L., Udpa, S., and Honavar, V. (2001). Learn++: An Incremental Learning Algorithm for Multi-Layer Perceptron Networks. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 31, No. 4. pp. 497–508.
  • Parekh, R. and Honavar, V. (2001). DFA Learning from Simple Examples. Machine Learning. Vol. 44. pp. 9–35.
  • Silvescu, A., and Honavar, V. (2001). Temporal Boolean Network Models of Genetic Networks and Their Inference from Gene Expression Time Series. Complex Systems.. Vol. 13. No. 1. pp. 54-.
  • Balakrishnan, K., Bousquet, O. and Honavar, V. (2000). Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots, Adaptive Behavior. Vol. 7. no. 2. pp. 173–216.
  • Yang, J., Parekh, R. & Honavar, V. (2000). Comparison of Performance of Variants of Single-Layer Perceptron Algorithms on Non-Separable Data. Neural, Parallel, and Scientific Computation. Vol. 8. pp. 415–438.
  • Yang, J. and Honavar, V. (1999). DistAl: An Inter-Pattern Distance Based Constructive Neural Network Learning Algorithm.. Intelligent Data Analysis. Vol. 3. pp. 55–73.
  • Yang, J. and Honavar, V. (1998). Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems (Special Issue on Feature Transformation and Subset Selection). vol. 13. pp. 44–49.
  • Honavar, V.; Uhr, L. (1993). "Generative Learning structures for Generalized Connectionist Networks". Information Sciences. 70 (1–2): 75–108. doi:10.1016/0020-0255(93)90049-r.

Knowledge representation and semantic web

  • Tao, J.; Slutzki, G.; Honavar, V. (2015). "A Conceptual Framework for Secrecy-preserving Reasoning in Knowledge Bases". ACM Transactions on Computational Logic. 16: 1–32. doi:10.1145/2637477. S2CID 11436585.
  • Tao, J., Slutzki, G., and Honavar, V. (2012). PSpace Tableau Algorithms for Acyclic Modalized ALC. Journal of Automated Reasoning. Vol. 49. pp. 551–582
  • Santhanam, G.; Basu, S.; Honavar, V. (2011). "Representing and Reasoning with Qualitative Preferences for Compositional Systems". Journal of Artificial Intelligence Research. 42: 211–274.

Data and Computational Infrastructure for Collaborative Science

  • Parashar, M., Honavar, V., Simonet, A., Rodero, I., Ghahramani, F., Agnew, G., and Jantz, R. (2020). The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research. Computing in Science and Engineering 22:3:79-92
  • Pathak, J.; Basu, S.; Lutz, R.; Honavar, V. (2008). "MoSCoE: An Approach for Composing Web Services through Iterative Reformulation of Functional Specifications". International Journal on Artificial Intelligence Tools. 17 (1): 109–138. CiteSeerX 10.1.1.301.6753. doi:10.1142/s0218213008003807.

Applied Informatics: Bioinformatics, Health informatics, Materials Informatics

Computer and information security

  • Helmer, G.; Wong, J.; Slagell, M.; Honavar, V.; Miller, L.; Wang, Y.; Wang, X.; Stakhanova, N. (2007). "Software Fault Tree and Colored Petri Net Based Specification, Design, and Implementation of Agent-Based Intrusion Detection Systems". International Journal of Information and Computer Security. 1 (1/2): 109–142. doi:10.1504/ijics.2007.012246.
  • Wang, Y.; Behera, S.; Wong, J.; Helmer, G.; Honavar, V.; Miller, L.; Lutz, R. (2006). "Towards Automatic Generation of Mobile Agents for Distributed Intrusion Detection Systems". Journal of Systems and Software. 79: 1–14. doi:10.1016/j.jss.2004.08.017.
  • Helmer, G.; Wong, J.; Honavar, V.; Miller, L. (2003). "Lightweight Agents for Intrusion Detection". Journal of Systems and Software. 67 (2): 109–122. CiteSeerX 10.1.1.308.7424. doi:10.1016/s0164-1212(02)00092-4.
  • Helmer, G.; Wong, J.; Slagell, M.; Honavar, V.; Miller, L.; Lutz, R. (2002). "A Software Fault Tree Approach to Requirements Specification of an Intrusion Detection System". Requirements Engineering. 7 (4): 207–220. CiteSeerX 10.1.1.101.853. doi:10.1007/s007660200016. S2CID 7414703.

Honors

References
  1. "NSF 12-499 Core Techniques and Technologies for Big Data". Retrieved 29 May 2015.
  2. "Vasant Honavar". faculty.ist.psu.edu.
  3. "Vasant Honavar named Huck Chair in Biomedical Data Sciences and AI". Retrieved 20 October 2021.
  4. "Artificial Intelligence Research Laboratory". Retrieved 29 May 2015.
  5. "Penn State Institute for Computational and Data Sciences". Retrieved 29 May 2015.
  6. "Penn State center to advance AI tools to accelerate scientific progress". Retrieved 12 July 2021.
  7. "Northeast Big Data Innovation Hub". Retrieved 13 July 2021.
  8. "Computing Community Consortium Members". Retrieved 29 May 2015.
  9. "CCC Announces new members". Retrieved 31 May 2015.
  10. "Susan Hockfield Chosen to Serve as AAAS President-Elect". Retrieved 21 December 2015.
  11. "'Sudha Murty' chair launched at IISc". 13 October 2016. Retrieved 13 July 2021.
  12. "2018 ACM Distinguished Members Recognized for Contributions that Have Revolutionized How We Live, Work and Play". Retrieved 13 July 2021.
  13. "AAAS Honors Accomplished Scientists as 2018 Elected Fellows". Retrieved 13 July 2021.
  14. "AAAI 2022: AAAI Conference on Artificial Intelligence". Retrieved 13 July 2021.
  15. "ORCID". orcid.org.
  16. "Vasant Honavar's Google Scholar Page". Retrieved 13 July 2021.
  17. "Library of Congress Catalog Search". Retrieved 29 May 2015.
  18. Vasant Honavar at the Mathematics Genealogy Project
  19. "Northeast Big Data Innovation Hub". Retrieved 20 October 2019.
  20. "Eastern Regional Network". Retrieved 20 June 2021.
  21. "Workshop on Brain, Computation, and Learning". Retrieved 20 October 2019.
  22. "Global Innovation Forum: Transforming Intelligence". Retrieved 20 October 2019.
  23. "US-Serbia and West Balkan Data Science Workshop". 12 August 2018. Retrieved 20 October 2019.
  24. "International Summer School on Deep Learning". Retrieved 20 October 2019.
  25. "AI-Enabled Materials Discovery, Design, and Synthesis (AIMS) Institute". Retrieved 14 February 2022.
  26. "Accelerating Science: A Grand Challenge for AI". Retrieved 14 February 2022.
  27. "Combat the Diseases of Despair Epidemic". Retrieved 14 February 2022.
  28. "Honavar honored for his leadership of the NSF Big Data Program". Retrieved 29 May 2015.
  29. "Inside Iowa State" (PDF). Retrieved 31 May 2015.
  30. "Teaching, service and research awards to LAS faculty, staff". Retrieved 31 May 2015.
  31. "2007 Fall University Convocation & Awards Ceremony". Retrieved 31 May 2015.
  32. "125 People of Impact". Retrieved 25 August 2016. (Accessible through Internet Archive)
  33. "Sudha Murty' chair launched at IISc". 13 October 2016. Retrieved October 14, 2016.
  34. "2018 ACM Distinguished Members Recognized for Contributions that Have Revolutionized How We Live, Work and Play". Retrieved November 8, 2018.
  35. "AAAS Honors Accomplished Scientists as 2018 Elected Fellows". Retrieved November 29, 2018.
  36. "We are proud to announce the first EAI Fellows". Retrieved February 15, 2022.
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