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Prakash Shenoy

Ronald G. Harper Distinguished Professor of Artificial Intelligence
Primary office:
785-864-7551
Capitol Federal Hall
Room 3187
University of Kansas


Summary

Academic Areas

Decision Sciences and Business Analytics

Joined the University of Kansas in 1978

Interests

  • Uncertain reasoning, expert systems
  • Bayes nets, influence diagrams, valuation networks
  • Data mining and knowledge discovery in databases

Research Interests

  • Knowledge-based systems
  • Bayesian networks
  • Decision Analysis
  • Game Theory

Recent Projects

  • Inference in hybrid Bayesian networks
  • Copula models for multivariate distributions

Current Activities

Dr. Shenoy is the inventor of Valuation-Based Systems (VBS), a mathematical architecture for knowledge representation and inference that includes many uncertainty calculi such as Bayesian probability, Dempster-Shafer belief functions, Spohn's epistemic beliefs, and Zadeh's possibility theory. It also includes various domains such as Bayesian decision analysis, solving systems of equations, Kalman's filter, database retrieval, propositional logic, optimization using dynamic programming, etc. His VBS architecture is currently being used for multi-sensor fusion in the ballistic missile defense program of the USA.

Education

  • Ph.D., Cornell University (1977)
  • Master's, Cornell University (1975)
  • Bachelor's, Indian Institute of Technology, Bombay, India (1973)

Teaching Interests

  • Uncertainty in artificial intelligence
  • Decision analysis and game theory
  • Probability and statistics
  • Supply chain modeling
  • Data analysis and forecasting

Research Interests

  • Uncertainty in artificial intelligence
  • Knowledge-based systems
  • Decision analysis
  • Game theory

Selected Publications

Jaunzemis, A. Holzinger, M. J., Chan, M. W., & Shenoy, P. P. (2018). Evidence gathering for hypothesis resolution using judicial evidential reasoning. In Proceedings of the 21st International Conference on Information Fusion In Fusion 2018: Proceedings of the 21st International Conference on Information Fusion, (pp. 2626--2633). Piscataway, NJ: IEEE.

Shenoy, P. P. (2018). An Expectation Operator for Belief Functions in the Dempster-Shafer Theory. In Proceedings of the 11th Workshop on Uncertainty Processing In Proceedings of the 11th Workshop on Uncertainty Processing, (pp. 165--176). Prague, Czech Republic: Matfyz Press.

Jirousek, R. & Shenoy, P. P. (2018). A new definition of entropy of belief functions in the Dempster-Shafer theory. International Journal of Approximate Reasoning, 92(1), 49-65. DOI://dx.doi.org/10.1016/j.ijar.2017.10.010

Cobb, B. R., & Shenoy, P. P. (2017). Inference in hybrid Bayesian networks with nonlinear deterministic conditionals. International Journal of Intelligent Systems, 32(12), 1217-1246. DOI://dx.doi.org/10.1002/int.21897

Singha, S. Hillmer, S. & Shenoy, P. P. (2017). On computing probabilities of dismissal of 10b-5 securities class-action cases. Decision Support Systems, 49(C), 29-41. DOI://dx.doi.org/10.1016/j.dss.2016.10.004

Tan, Y. Shenoy, P. P., Chan, M. W., & Romberg, P. M. (2016). On Construction of Hybrid Logistic Regression-Naïve Bayes Model for Classification. In Proceedings of Machine Learning Research: Conference on Probabilistic Graphical Models (Vol. 52, pp. 523--534). In Proceedings of Machine Learning Research, Journal of Machine Learning Research. http://proceedings.mlr.press/v52/

Cinicioglu, E. N., & Shenoy, P. P. (2016). A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores. Annals of Operations Research, 244(2), 385--405. DOI:10.1007/s10479-012-1171-9 http://dx.doi.org/10.1007/s10479-012-1171-9

Jiroušek, R. & Shenoy, P. P. (2016). Entropy of belief functions in the Dempster-Shafer theory: A new perspective. In J. Vejnarová & V. Kratochvíl (Eds.), Lecture Notes in Artificial Intelligence. Belief Functions: Theory and Applications (Vol. 9861, pp. 3-13). Springer International Publishing. DOI:10.1007/978-3-319-45559-4_1 http://dx.doi.org/10.1007/978-3-319-45559-4_1


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