Kristin Bennett

Kristin Bennett

Associate Director of the IDEA

Dr. Bennett is an active researcher in the Mathematical Programming, Operations Research, Machine Learning, Bioinformatics and Data Mining communities. She is currently a Professor in the departments of Mathematical Sciences and Computer Science at Rensselaer. She founded and directs the NIH funded TB-Track Project which examines the molecular epidemiology of tuberculosis. She is co-PI of RPI's NSF Advance project for the advancement of women faculty at RPI and has expertise in gender issues and faculty advancement. She was Program Co-chair of the 2005 SIGKDD Knowledge Discovery and Data Mining Conference. She has served as a program committee member of numerous conferences including SIGKDD Knowledge Discovery and Data Mining Conference, AAAI Conference, International Conference on Machine Learning, Neural Information Processing Systems, IEEE Conference on Data Mining, Computational Learning Theory, and SIAM International Conference on Data Mining. She is a founding associate editor of ACM Transactions on Knowledge Discovery and Data Mining. She has organized multiple data mining and machine learning clusters at INFORMS meetings. She is a former associate editor of Naval Research Logistics, Machine Learning Journal, SIAM Journal of Optimization, and IEEE Transactions on Neural Networks. She serves on the advisory board of the Journal of Machine Learning Research. She has experience developing data mining approaches for chemistry, biology, and public health related applications. She is PI and director of a project of the NIH funded project: Discovering Hidden Groups Across Tuberculosis Patient and Pathogen Genotype Data. She has one patent for database indexing to support data mining earned while she was a visiting researcher at Microsoft Research. She received both the Rensselaer and NSF Early Career Awards, as well as the Boeing Distinguished Educator Award for Women and Minorities.

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

  • 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
  • T. Huang, J. Zaretzki, C. Bergeron, K. Bennett, and C. Breneman, “DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP Mediated Sites of Metabolism, Journal of Cheminformatics Modeling, 53(12):3352-66. 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, to appear, 2011
  • G. Moore, C. Bergeron, and K. P. Bennett, "Model Selection for Primal SVM", Machine Learning, to appear, 2011.