CBIS Research Faculty
Research Faculty at CBIS
Kristin Bennett

Kristin Bennett

Associate Director of the IDEA

Machine Learning, Data Mining, Data Science, Bioinformatics and Cheminformatics, Combining operations research and artificial intelligence problem solving methods. , Mathematical programming approaches to problems in artificial intelligence such as machine learning, neural networks, pattern recognition, and planning. , Application of these techniques to medical, financial and scientific problems. , Mathematical programming approaches to other areas in computer sciences such as genetic algorithms and database query optimization., Innovative pedagogy in data analytics education.

Kristin P. Bennett is the Associate Director of the Institute for Data Exploration and Application and a Professor in the Mathematical Sciences and Computer Science Departments and at Rensselaer Polytechnic Institute. Her research focuses on extracting information from data using novel predictive or descriptive mathematical models and data visualizations, and the applications of these methods to support decision making and to accelerate discovery in science, engineering, public health and business. She has 25 years of experience and over 100 publications in these areas.  As an active member of the machine learning, data mining, and operations research communities, she has served as present or past associate or guest editor for ACM Transactions on Knowledge Discovery from Data, SIAM Journal on Optimization, Naval Research Logistics, Machine Learning Journal, IEEE Transactions on Neural Networks, and Journal on Machine Learning Research. She served as program chair of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. She has a Ph.D. in Computer Sciences from the University of Wisconsin-Madison. She founded and directs the NIH sponsored “TB-Insight” project which provided molecular epidemiology tools and methods to help track and control tuberculosis. She also founded and directs the “Data Analytics Throughout Undergraduate Mathematics” or DATUM which is pioneering highly effective new approaches for data analytics undergraduate education.  She also founded the Data Interdisciplinary Challenges Intelligent Technology Exploration Laboratory (Data INCITE Lab.)   In the Data INCITE Lab, undergraduate and graduate students tackle open applied data analytics problems contributed by industry, foundations, and researchers. 


Ph.D., University of Wisconsin, Madison, 1993

M.S., University of Wisconsin, Madison, 1989

B.S., University of Puget Sound, 1985



Selected Publications

  • R. Zhao, Md Ridwan Al Iqbal, K. Bennett and Q. Ji, “Wind Turbine Fault Prediction Using Soft Label SVM,” International Conference on Pattern Recognition (ICPR), 2016.
  • J. Ryan, J. Hendler, and K. Bennett, “Understanding Emergency Department 72-hour Revisits Among Medicaid Patients Using Electronic Health Care Records”, Journal of Big Data, 3:4:238-248, 2015.
  • Minoo Aminian, David Couvin, Amina Shabbeer, et al., “Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks,” BioMed Research International, Volume 2014 (2014), Article ID 398484, 11 pages http://dx.doi.org/10.1155/2014/398484
  • A. Shabbeer, L. Cowan, J. Driscoll, C. Ozcaglar, S. L. Vandenberg, B. Yener, and K. P. Bennett, “TB-Lineage: an Online Tool for Classification and Analysis of Strains of Mycobacterium tuberculosis Complex”, Infection, Genetics and Evolution, 12:4, 789-97, 2012.
  • T. Huang, J. Zaretzki, C. Bergeron, K. Bennett, C. Breneman, “DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism”, Jnl. of Chemical Information and Modeling, 53:12: 3352-3366, 2013.
  • J. Zaretzki, C. Bergeron, P. Rydberg, T.-W. Huang, K. P. Bennett, and C. Breneman, "RS-Predictor: A new tool for generating and validating models capable of predicting sites of cytochrome P450-mediated metabolism", Journal of Chemical Information and Modeling, 2011
  • G. Moore, C. Bergeron, and K. P. Bennett, "Model Selection for Primal SVM", Machine Learning,2011.