Prakash P. Shenoy is the Ronald G. Harper Distinguished Professor of Artificial Intelligence in Business, University of Kansas at Lawrence. He received a B.Tech. in Mechanical Engineering from the Indian Institute of Technology, Bombay, India, in 1973, and an M.S. and a Ph.D. in Operations Research from Cornell University in 1975 and 1977, respectively.
His research interests are in the areas of artificial intelligence and decision sciences. He is the inventor of valuation-based systems, an abstract framework for knowledge representation and inference that includes Bayesian probabilities, Dempster-Shafer belief functions, Spohn's kappa calculus, Zadeh's possibility theory, propositional logic, optimization, solving systems of equations, database retrieval, and other domains. He is also the co-author (with G. Shafer) of the so-called Shenoy-Shafer architecture for computing marginals of joint distributions using local computation. He has published many articles on the management of uncertainty in expert systems, on decision analysis, and on the mathematical theory of games. His articles have appeared in journals such as Operations Research, Management Science, International Journal of Game Theory, Artificial Intelligence, and International Journal of Approximate Reasoning. He has received several research grants/contracts from the Database and Expert Systems (DES), and Decision, Risk and Management Science (DRMS) programs of the National Science Foundation, the Research Opportunities in Auditing program of the Peat Marwick Main Foundation, the Higher Education Academic Development Donations program of Apple Computer, Inc., the Information Sciences Department of Hughes Research Laboratories, Space Dynamics Laboratory of Utah State University, Information Extraction and Transport, Inc., Science Applications International Corp., Sparta, Inc., and Raytheon Missile Systems, Inc.
He serves as an Associate Editor of International Journal of Approximate Reasoning, and as an ad-hoc referee for over 30 journals and conferences in Artificial Intelligence and Management Science/Operations Research. He has served as an Area Editor for International Journal of Fuzziness and Knowledge-Based Systems, as an Associate Editor of Operations Research, as an Associate Editor of Management Science, as Program Co-Chair of the Thirteenth Conference on Uncertainty in Artificial Intelligence held at Brown University, Providence, 1997, and as Conference Chair of the Fourteenth Conference on Uncertainty in Artificial Intelligence held at University of Wisconsin-Madison in 1998.
His teaching interests are in the areas of uncertain reasoning, decision analysis, and statistics. He has taught undergraduate and graduate courses on linear programming, non-linear programming, game theory, management information systems, decision support systems, uncertain reasoning, probability, statistics, multivariate statistics, supply chain modeling & optimization, and data analysis & forecasting. He has served on doctoral dissertation committees of forty PhD students in Management Science, Marketing, Accounting, Economics, Electrical Engineering and Computer Science, Geography, Civil Engineering, and Philosophy, ten as chairperson. He has received the Outstanding Mentor Award from the Association of Business Doctoral Students five times, an Excellence in Teaching Award from the Center for Teaching Excellence, and an Outstanding Mentor Award from the Graduate and Professional Association of the University of Kansas.
In Summer 2012, with the help of Dean Neeli Bendapudi and his colleagues in Decision Sciences, Marketing, and Finance, he formed the Center for Business Analytics Research (CBAR). In Fall 2013, DST Systems, Inc. joined CBAR as a founding corporate sponsor. In Spring 2015, AIG, Inc. joined CBAR as a corporate sponsor. He currently serves as the academic faculty Director of CBAR.
Ph.D., Operations Research, Cornell University
M.S., Operations Research, Cornell University
B.Tech., Mechanical Engineering, Indian Institute of Technology
- Uncertainty in artificial intelligence
- Decision analysis and game theory
- Probability and statistics
- Supply chain modeling
- Data analysis and forecasting
- Uncertainty in artificial intelligence
- Knowledge-based systems
- Decision analysis
- Game theory
Singha, S. & Shenoy, P. (2018). An adaptive heuristic for feature selection based on complementarity. Machine Learning, 107(12), 2027-2071.
Jaunzemis, A. Holzinger, M. J., Chan, M. W., & Shenoy, P. P. (2018). Evidence gathering for hypothesis resolution using judicial evidential reasoning. Information Fusion, 49(9), 26-45. DOI://doi.org/10.1016/j.inffus.2018.09.010
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 (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
Shenoy, Prakash, (Principal), Measuring Information Quality in Multi-Sensor Data Fusion Applications, UK-001, Lockheed Martin Space Systems Company, Sunnyvale, CA, $24,675, (06/01/2015 - 09/30/2015). For Profit (company/corporation). Status: Funded.
Shenoy, Prakash P, (Principal), Hillmer, Steven, (Co-Principal), Predicting Probabilities of Dismissal of 10b-5 Securities Class Action Cases, American International Group, Inc., $125,000, (04/22/2015 - 09/30/2015). The primary goal of this project is to propose a new method for computing probabilities of dismissal of 10b-5 securities class-action cases filed in United States Federal district courts. The new method is a hybrid of two widely-used methods: logistic regression (LR) and naive Bayes (NB). We call this new method hybrid logistic regression-naive Bayes, or in short, an hybrid method. The hybrid method combines the strengths of both constituents methods, and offers a more versatile, and yet simple, alternative to LR and NB in many domains. Using a dataset of 10b-5 securities class-action cases filed between 2002 and 2010, we show that a hybrid model has the potential of making better predictions than either LR or NB models. By better, we mean lower classification errors and also lower root mean square errors of probabilities of dismissal. For Profit (company/corporation). Status: Funded.
Shenoy, Prakash P, (Principal), A Principled Approach to Fusion and Inference Using the Valuation-based Systems Framework, Lockheed-Martin Space Systems Company, Sunnyvale, CA, $63,181, Submitted 09/13/2013 (11/15/2013 - 08/15/2014). For Profit (company/corporation). Status: Funded.
Shenoy, Prakash P., (Principal), Information Fusion, Raytheon Missile Systems, Tucson, AZ, $50,027, (08/01/2007 - 12/31/2007). For Profit (company/corporation). Status: Funded.