
Kristin P. Bennett
Professor
Department of Mathematical Sciences
Department
of Computer Sciences
Lally School of Management
Rensselaer Polytechnic Institute
Troy, New York 121803590
Email: bennek at rpi dot edu
Telephone: (518) 2766899
Fax: (518) 2764824

Ph.D.,
University of Wisconsin, Madison, 1993
Interests: mathematical programming, machine learning, data science,
support vector machines, neural networks, artificial intelligence, parallel
optimization, automated drug discovery, data mining, bioinformatics, cheminformatics, molecular epidemiology, population
biology, complementarity
Check out the TBInsight
Project on Tuberculosis Molecular epidemiology directed by Dr. Bennett.
Table of Contents
Combining
operations research and artificial intelligence problem solving methods. Mathematical programming
approaches to problems in data science, data mining, artificial intelligence
and machine learning such as machine learning, support vector machines, neural
networks, pattern recognition, and planning. Application of
these techniques to medical, financial and scientific problems. Applications of machine learning to systems biology, cheminformatics, bioinformatics, tissue engineering,
molecular epidemiology, and population biology.
This
list is out dated. Please see my Google
Scholar
page for a more recent one.
 J.
Zaretzki, C. Bergeron, P. Rydberg, T.W. Huang, K. P. Bennett, and C.
Breneman, RSPredictor: A new tool for generating and validating models
capable of predicting sites of cytochrome P450mediated 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.
 P. Agius, K. P. Bennett, and M. Zuker, Comparing RNA
Secondary Structures using a Relaxed Base Pair Score, RNA Journal, 16:865878, 2010.
 M. Aminian, A. Shabbeer, and K. P. Bennett, A
Conformal Bayesian Network for Classification of Mycobacterium tuberculosis Complex Lineages, BMC Bioinformatics, 11:Suppl3, S4,
2010.
 A. Shabbeer, C. Ozcaglar, M. Gonzalez, and K. P.
Bennett, Optimal Embedding of Heterogeneous Graph Data with Edge Crossing
Constraints, Neural Information Processing Systems Workshop: Challenges of
Data Visualization, (published online), 2010.
 G. Moore, C. Bergeron, and K. P. Bennett,
Gradienttype Methods for Primal SVM Model Selection, Neural Information
Processing Systems Workshop: Optimization for Machine Learning, (published
online), 2010.
 C. Ozcaglar, A. Shabbeer, S. Vandenberg, B. Yener and
K. P. Bennett, Multiplebiomarker tensor analysis for tuberculosis lineage
identification, NIPS workshop: Tensors, Kernels, and Machine Learning,
(published online), 2010.
 P. Hao, X. Ban, K. Bennett, Q. Ji, and Z. Sun, Signal
Timing Estimation Using Sample Intersection Travel Times, Transformation
Review Board Annual Meeting Compendium of Papers, 2011.
 G. Kunapuli, K. P. Bennett, A. Shabbeer, R. Maclin,
and J. Shavlik, Online KnowledgeBased Support
Vector Machines, Proceedings of
European Conference on Machine Learning, ECML PKDD 2010, Lecture Notes
in Computer Science, 6322, 45116, 2010.
 C. Ozcaglar, M. Aminian, A. Shabbeer, S. Vandenberg,
B. Yener, and K. P. Bennett, Examining Sublineage
Structure of Mycobacterium tuberculosis Complex Strains with
Multiple Biomarker Tensors, IEEE
International Conference on Biology in Bioinformatics and Biomedicine,
2010.
 G. Kunapuli, K. P. Bennett, R. Maclin, and J. Shavlik, The Adviceptron: Giving Advice to the Perceptron, ANNIE (Artificial Neural Networks in
Engineering) Conference, 2010. First
runnerup for best paper award.
 M. Aminian, A. Shabbeer, and K. P. Bennett,
Determination of Major Lineages of Mycobacterium
tuberculosis Complex using Mycobacterial
Interspersed Repetitive Units, IEEE
International Conference on Biology in Bioinformatics and Biomedicine,
338343, 2009.
 K. P. Bennett, G. Kunapuli, J.
Hu, and J.S. Pang. Optimization and Machine Learning, in Computational
Intelligence: Research Frontiers : IEEE World Congress on Computational
Intelligence, WCCI 2008, Hong Kong, China, June 16, 2008 :
Plenary/invited Lectures. Edited By Jacek M. Zurada, Gary G. Yen, Jun Li Jim Wang, Springer, 2948,
2008
 A. Demiriz, K. P. Bennett, and P. S. Bradley, Using
Assignment Constraints to Avoid Empty Clusters in kmeans Clustering, in Constrained Clustering: Advances in
Algorithms, Theory, and Applications, S. Basu,
I. Davidson, and K. Wagstaff, CRC Press,
203219, 2008.
 G. Kunapuli, K. P. Bennett, J. Hu, and J. S. Pang, Bilevel Model Selection for Support Vector Machines,
in Data Mining and Mathematical Programming, P. M. Pardalos
and P. Hansen, CRM Proceedings and
Lecture Notes, AMS, 45 :129158,
2008.
 C. Bergeron, T. Hepburn, M. Sundling,
N. Sukumar, K. P. Bennett, C. Breneman, Prediction of bonding affinity in peptides: using
kernel methods for nonlinear modeling, Protein and Peptide Letters, 2008.
Documents winning entry of 2006 COEPRA Contest.
 J. Hu, J. E. Mitchell, J.S. Pang, K. P. Bennett and G.
Kunapuli. On the Global Solution of Linear Programs with Linear
Complementarity Constraints. SIAM Journal on Optimization, 2008.
 Bulent Yener,
Evrim Acar, Phaedra
Agius, Scott L. Vandenberg, Kristin P Bennett and George E Plopper, Multiway Modeling
and Analysis in Stem Cell Systems, BMC Systems Biology, 2008.
 K. P. Bennett, G. Kunapuli,
J. Hu, and J.S. Pang. Model Selection via Bilevel
Optimization, in Computational Intelligence: Research Frontiers : IEEE
World Congress on Computational Intelligence, WCCI 2008, Hong Kong, China,
June 16, 2008 : Plenary/invited Lectures, Edited By Jacek
M. Zurada, Gary G. Yen, Jun Li Jim Wang,
Springer, 2008.
 A. Demiriz, K. P. Bennett,
and P. S. Bradley, Using Assignment Constraints to Avoid Empty Clusters in
kmeans Clustering. Constrained Clustering: Advances in Algorithms,
Theory, and Applications, S. Basu, I. Davidson,
and K. Wagstaff, CRC Press, pg. 203219, 2008.
 K. P. Bennett, Discussion: Evidence Contrary to the
Statistical View of Boosting by David Mease and
Abraham Wynar, Journal of Machine Learning Research,
2008.
 Daniel L. Silver, and K. P.
Bennett, Guest Editorial: Inductive Transfer Learning, Machine Learning
Journal, 2008.
 A. Demiriz, K. P. Bennett, and P. S. Bradley, Using
Assignment Constraints to Avoid Empty Clusters in kmeans Clustering.
Constrained Clustering: Advances in Algorithms, Theory, and Applications,
S. Basu, I. Davidson, and K. Wagstaff,
CRC Press, pg. 203219, 2008.
 G. Kunapuli, K. P. Bennett, J. Hu, and J.S. Pang,
Classification Model Selection via Bilevel
Programming, Computational Optimization and Applications, 2008.
 K. P.
Bennett, C. Bergeron, E. Acar, R. Klees. S. Vandenberg, B. Yener, and G. Plopper, Proteomics reveals multiple routes to the osteogenic phenotype in mesenchymal
stem cells, BMC Genomics, 8:380, 2007.
 R. Klees, R. Salasznyk, S. Vandenberg, K. Bennett, and G. Plopper, Laminin5 Activates Extracellular Matrix
Production and Osteogenic Gene Focusing in Human
Mesenchymal Stem Cells, Matrix Biology, 2007.
 P. Agius, B. Kreiswirth, N. Kurepina,
and K. P. Bennett, Typing Staphylococcus aureus
using the spa gene and novel distance measures, IEEE Transactions on
Computational Biology and Bioinformatics, 2007.
 K. P. Bennett,
J. Hu, G. Kunapuli, and J.S. Pang, Model Selection via Bilevel Optimization, International Joint Conference
in Neural Networks, Vancouer 2006.
 The
Interplay of Optimization and Machine Learning Research, the
introduction to the JMLR Special Topic
on Machine Learning and Large Scale Optimization, edited by K. P. Bennett
and Emilio ParradoHernandez, 2006.
 Inna Vitol,
Jeffrey Driscoll, Barry Kreiswirth, Natalia Kurepina, Kristin P. Bennett,
Identifying Mycobacterium tuberculosis Complex Strain Families using
Spoligotypes, Infection, Genetics, and Evolution, Nov;6(6):491504, 2006. The
SPOTCLUST program that goes with this can be found at www.rpi.edu/~bennek/EpiResearch.
 Salasznyk, R.M., R. F. Klees, S.Vandenberg, S., K.
P. Bennett, and G.E. Plopper, Gene focusing as a
basis for controlling stem cell differentiation. Stem Cells and
Development, 14(6)608620, 2005. Note this was considered a Defining
Report and was featured in the editorial for that issue.
 Salasznyk, R.M., R. F. Klees, S. Vandenberg, S., K. Bennett and G. E. Plopper, Protein expression profiling using gene
ontologies of human mesenchymal stem cells
during osteogenic differentiation induced by
ascorbic acid2phosphate, ?glycerophosphate,
and dexamethasone. Stem Cells and Development, 14(4):35466, 2005.
 M. Momma and K. Bennett,
Constructing Orthogonal Latent Features for Arbitrary Loss, Feature
Extraction, Foundations and Applications, Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti Zadeh, editors,
Springer, 2006. accepted 2004.
 M. Fukunari,
K. P. Bennett and C. Malmborg, DecisionTree Learning in Dwell Point
Policies in Autonomous Vehicle Storage and Retrieval Systems (AVSRS),
International Conference on Machine Learning and Applications 2005.
 A. Malipani,
Y.F. Huang, S. Andra, and K. P. Bennett, Kernelized SetMembership Approach to Adaptive
Filtering, 2005 IEEE International Conference on Acoustics, Speech, and
Signal Processing, 2005.
 Jinbo Bi, T. Zhang and K.
P. Bennett, ColumnGeneration Boosting Methods for Mixture of Kernels,
Proceedings of SIGKDD International Conference on Knowledge Discovery and
Data Mining, Seattle, 2004, Joydeep Ghosh,
editor, ACM Press, 2004.
 N. Tugcu,
M. Song, C. Breneman, N. Sukumar, K. P. Bennett,
and S. Cramer, Prediction of the effect of mobilephase salt type on
protein retention and selectivity in anion exchange systems, Analytical
Chemistry, 75:14, 2004, pp. 35633572.
 Regression
Error Characteristic Curves , Jinbo Bi and K.
P. Bennett, Proceedings of the 20th International Conference on Machine
Learning, 2003.
 A Geometric Approach to Support Vector Regression,
Jinbo Bi and K. P. Bennett, Neurocomputing, 55,
2003, pp. 79108.
 M. Momma and K.P. Bennett, Sparse Kernel
Partial Least Squares Regression. Proceedings
of Conference on Learning Theory, 2003.
 K. P. Bennett and M. J. Embrechts, An Optimization
Perspective on Partial Least Squares, in J.A.K. Suykens, G. Horvath, S.
Basu, C. Micchelli, J. Vandewalle (Eds.) Advances in Learning Theory:
Methods, Models and Applications, NATO Science Series III: Computer \&
Systems Sciences, Volume 190, IOS Press Amsterdam, 2003,p. 227250.
 J. Bi, K. Bennett, M. Embrechts, C. Breneman, and M.
Song, Dimensionality Reduction via Sparse Support Vector Machines, Journal
of Machine Learning Research, 3, 2003, 12291243.
 M. Song, C. Breneman, J. Bi, N. Sukumar, K. Bennett, S.
Cramer, and N. Tugcu, Prediction of Protein Retention Times in
AnionExchange Chromatography Systems Using Support Vector Regression,
September 2002, Journal of Chemical Information and Computer Sciences.
 K. Bennett, M. Momma, and J. Embrechts, MARK: A
Boosting Algorithm for Heterogeneous Kernel Models, Proceedings of SIGKDD
International Conference on Knowledge Discovery and Data Mining, 2002.
 Exploiting Unlabeled Data in Ensemble Methods, with A.
Demiriz, and R. Maclin, to appear in Proceedings of SIGKDD International
Conference on Knowledge Discovery and Data Mining, 2002. \A> >
 C.
Breneman, K. Bennett, M. Embrechts, S. Cramer, M. Song, and J. Bi,
Descriptor Generation, Selection and Model Building in Quantitative
StructureProperty Analysis, in Chapter 11 of Experimental Design for
Combinatorial and High Throughput Materials Development, J. Crawse editor,
Wiley, 2002.
 A Pattern Search Method for Model Selection of Support
Vector Regression with M. Momma, Proceedings of SIAM
Conference on Data Mining, 2002.
 Duality, Geometry, and Support Vector Regression with
Jinbo Bi, June 2001, Advances in Neural Information Processing Systems 14,
T. Dietterich, S. Becker and Z. Ghahramani editors, MIT Press, Cambridge,
pg 593600. 2002.
 Support Vector Machines: Hype or Hallelujah? with
Colin Campbell, SIGKDD Explorations, 2,2, 2000, 113.
 Support Vector Machine Regression in Chemometrics
with A. Demiriz, C. Breneman, M. Embrechts, Computing Science and
Statistics, 2001
 Sparse regression ensembles in infinite and finite
hypothesis spaces, with Gunnar Raetsch and Ayhan
Demiriz, Machine Learning, 48, 13, 2002, pp 193221.
 Linear Programming Boosting via Column Generation,
with Ayhan Demiriz and John ShaweTaylor, Machine Learning, 46:1, 2001,
225254.
 Constrained KMeans Clustering, with
Paul Bradley and Ayhan Demiriz, Microsoft Research Technical Report
200065, May 2000.
 A Column Generation Algorithm for Boosting ,
with Ayhan Demiriz and John ShaweTaylor, Proceedings of the Seventeenth
International Conference on Machine Learning, Pat Langley Editor, Morgan
Kaufmann, San Francisco, 2000, pp. 6572.
 Duality and Geometry in SVM Classifiers,
with Erin Bredensteiner, Proceedings of the Seventeenth International
Conference on Machine Learning, Pat Langley Editor, Morgan Kaufmann, San
Francisco, 2000, pp. 5764.
 Large Margin
Trees for Induction and Transduction, with Donghui Wu,
Nello Cristianini, and John ShaweTaylor, ICML'99 Proceedings.
 DensityBased Indexing for Approximate Nearest Neighbor
Queries, with Usama Fayyad and Dan Geiger, Microsoft
Research Technical Report 9858, Microsoft Research, Redmond WA, 19998.
Longer version of paper in KDD'99 Proceedings.
 Enlarging Margins in Perceptron Decision Trees ,
with Donghui Wu, Nello Cristianini, and John ShaweTaylor Machine
Learning, 41:3, 295313.
 SemiSupervised Clustering Using Genetic Algorithms,
with Ayhan Demiriz and Mark Embrechts, ANNIE'99 (Artificial Neural
Networks in Engineering), November 1999. Long version: Genetic Algorithm
Approach for Semisupervised Clustering is under review for journal
publication.
 Optimization Approaches to SemiSupervised Learning ,
with Ayhan Demiriz, to appear in Applications and Algorithms of
Complementarity, M. C. Ferris, O. L. Mangasarian, and J. S. Pang, editors.
Kluwer Academic Publishers. Boston 2000.
 Multicategory Classification by Support Vector
Machines. with E. J. Bredensteiner, Computational
Optimizations and Applications, 12, 1999, pp 5379.
 On Mathematical Programming Methods and Support Vector
Machines, in Advances in Kernel Methods  Support
Vector Machines, A. Schoelkopf, C. Burges, and A. Smola, editors, MIT
Press, Cambridge, MA, 1999, pp 307326.
 SemiSupervised Support Vector Machines
with A. Demiriz, Advances in Neural Information Processing Systems, 12,
M. S. Kearns, S. A. Solla, D. A. Cohn, editors, MIT Press, Cambridge, MA,
1998, pp 368374.
 On Support Vector Decision Trees for Database Marketing
with D. H. Wu and L. Auslender, R.P.I Math Report No. 98100, Rensselaer
Polytechnic Institute, Troy, NY, 1998. Long version of IJCNN'99 paper
 A Support Vector Machine Approach to Decision Trees
with J. Blue, R.P.I Math Report No. 97100, Rensselaer Polytechnic
Institute, Troy, NY, 1997.
 Optimal Decision Trees with J. Blue R.P.I
Math Report No. 214, 1996.
 Geometry in Learning with E.
Bredensteiner, Web Manuscript http:\\www.rpi.edu\~bennek\geometry2.ps
September 1996. also in Geometry at Work, C. Gorini editors,
Mathematical Association of America, Washington, D.C, 2000, 132145.
 Hybrid Extreme Point Tabu Search with
J. Blue, R.P.I Math Report No. 240, 1996, appeared in European
Journal of Operation Research 106, 1998, 676688
 An Extreme Point Tabu Search Method for Data Mining. with
J. Blue, R.P.I Math Report No. 228, 1996.
 Feature Minimization within Decision Trees. with
E. Bredensteiner, Computational Optimizations and Applications, 10:2,
1998. 111126
 A Parametric Optimization Method for Machine Learning. with
E. J. Bredensteiner, R.P.I Math Report No. 217, 1995. Final
version appeared in INFORMS Journal on Computing, 9:3, pp.
311318, 1997.
 Global Tree Optimization: A NonGreedy Decision Tree
Algorithm. Computing Science and Statistics,
26, pp. 156160, 1994
 Serial and
Parallel Multicategory Discrimination, SIAM Journal on Optimization,
with O. L. Mangasarian, 4:4, pp. 722734, 1994. Also
appeared as Tech Report 1165, Department of Computer Sciences, University
of Wisconsin Madition, 1994.
 Machine Learning via Mathematical Programming, Ph.D.
Thesis, Department of Computer Sciences, University of Wisconsin Madition,
1994.
 Multicategory Discrimination via Linear Programming,
with O. L. Mangasarian, Optimization Methods and Software 3, pp.
2739, 1993.
 Bilinear Separation of Two Sets in nSpace, with
O. L. Mangasarian, Computational Optimization and Applications,
2, pp. 207227, 1993.
 Decision Tree Construction Via Linear Programming,
Proceedings of the 4th Midwest Artificial Intelligence and Cognitive
Science Society Conference, Utica, Illinois, pp. 97101, 1992.
 Robust Linear Programming Discrimination of Two
Linearly Inseparable Sets. with O. L. Mangasarian, Optimization
Methods and Software, 1, pp. 2334, 1992.
 Neural Network Training Via Linear Programming,
with O. L. Mangasarian, in P. M. Pardalos (ed.), Advances in
Optimization and Parallel Computing, pp. 5667, 1992.
Note
that this research was based partially upon work supported by grants from the
National Science Foundation, Microsoft Research, Office of Naval Research, Air
Force Office of Scientific Research, DARPA and National Institutes of Health.
Last Changed:
April 203