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Ph.D.,
University of Wisconsin, Madison, 1993
Interests: mathematical programming, machine learning, support vector
machines, neural networks, artificial intelligence, parallel optimization, tabu
search, automated drug discovery, data mining, bioinformatics, cheminformatics,
molecular epidemiology, population biology, complementarity
Announcement:
PhD positions in Bioinformatics/Molecular Epidemiology of Tuberculosis
AVAILABLE
Funded Ph.D. research assistantships in the area of
Population Biology and Molecular Epidemiology of Tuberculosis using Machine
Learning Methods are available with Prof. Kristin P. Bennett www.rpi.edu/~bennek of Rensselaer
Polytechnic Institute www.rpi.edu in Troy, New
York, USA.
These positions are part of
the NIH funded project “Discovering
hidden groups across tuberculosis patient and pathogen genotype data which
brings together a multi-national collaboration of machine learning researchers
and public health organizations.
The principal
objective of this project is to develop methods that combine tuberculosis
pathogen genotyping and patient epidemiology data that can be used in the
control, understanding, and tracking of tuberculosis. This work focuses on the modeling of large
international collections of patient epidemiology and strain data for the Mycobacterium tuberculosis complex
(MTC), the causative agent of tuberculosis disease (TB), because of the urgent
global need and the unique data availability due to the United States National
TB genotyping program. Specifically, the
project addresses the following problem: given MTC DNA fingerprinting and TB
patient data being accumulated nationally and internationally, identify hidden
groups capturing MTC genetic lineages and TB epidemiology using machine
learning, and use these hidden groups to address problems in the control, understanding, prevention, and
treatment of tuberculosis at city, state, national, and international
levels.
Please
contact Prof. Bennett at bennek@rpi.edu if
interested. Include CV and letter of
interest. Students must satisfy entrance
requirements for RPI Ph.D. program in either mathematical science or computer
science.
Table of Contents
Research
Interests
Combining
operations research and artificial intelligence problem solving methods. Mathematical
programming approaches to problems in artificial intelligence 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. Recent developments appear in the papers
referenced on this page.
Interested in learning more about Support Vector Machines, Here are the slides from an SVM overview talk in powerpoint, Support Vector Machines: Hype or Hallelujah? and the article from SIGKDD explorations
Selected
Recent Publications (a bit behind)--
- G.
Kunapuli, K. P. Bennett, J. Hu, and J.-S. Pang, “Bilevel Model Selection
for Support Vector Machines", Data Mining and Mathematical
Programming, P. M. Pardalos and P. Hansen, CRM Proceedings and Lecture
Notes, American Mathematical Society, volumne 45, 2008, in press.
- 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,
- 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 1-6, 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 k-means Clustering”. Constrained Clustering: Advances in
Algorithms, Theory, and Applications, S. Basu, I. Davidson, and K.
Wagstaff, CRC Press, pg. 203-219, 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 k-means Clustering”.
Constrained Clustering: Advances in Algorithms, Theory, and Applications,
S. Basu, I. Davidson, and K. Wagstaff, CRC Press, pg. 203-219, 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, "Laminin-5 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 Parrado-Hernandez, 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):491-504, 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)608-620, 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 acid-2-phosphate, ?-glycerophosphate, and
dexamethasone. Stem Cells and Development, 14(4):354-66, 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, “Decision-Tree 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 Set-Membership Approach to
Adaptive Filtering”, 2005 IEEE International Conference on Acoustics,
Speech, and Signal Processing, 2005.
- Jinbo Bi, T. Zhang and K.
P. Bennett, “Column-Generation 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 mobile-phase salt type on protein retention and selectivity in
anion exchange systems”, Analytical Chemistry, 75:14, 2004, pp. 3563-3572.
- "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. 79-108.
- 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. 227-250.
- 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, 1229-1243.
- M. Song, C.
Breneman, J. Bi, N. Sukumar, K. Bennett, S. Cramer, and N. Tugcu,
"Prediction of Protein Retention Times in Anion-Exchange 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 Structure-Property 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 593-600. 2002.
- Support
Vector Machines: Hype or Hallelujah? with Colin Campbell, SIGKDD
Explorations, 2,2, 2000, 1-13.
- 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, 1-3, 2002, pp 193-221.
- Linear Programming
Boosting via Column Generation, with Ayhan Demiriz and John Shawe-Taylor,
Machine Learning, 46:1, 2001, 225-254.
- Constrained
K-Means Clustering, with Paul Bradley and Ayhan Demiriz, Microsoft
Research Technical Report 2000-65, May 2000.
- A Column Generation Algorithm for
Boosting , with Ayhan Demiriz and John Shawe-Taylor, Proceedings of
the Seventeenth International Conference on Machine Learning, Pat Langley
Editor, Morgan Kaufmann, San Francisco, 2000, pp. 65-72.
- 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. 57-64.
- Large Margin Trees for Induction and
Transduction, with Donghui Wu, Nello Cristianini, and John
Shawe-Taylor, ICML'99 Proceedings.
- Density-Based
Indexing for Approximate Nearest Neighbor Queries, with Usama Fayyad
and Dan Geiger, Microsoft Research Technical Report 98-58, 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
Shawe-Taylor Machine Learning, 41:3, 295-313.
- Semi-Supervised 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 Semi-supervised Clustering is under review
for journal publication.
- Optimization Approaches to
Semi-Supervised 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 53-79.
- 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 307-326.
- Semi-Supervised
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 368-374.
- On
Support Vector Decision Trees for Database Marketing with D. H. Wu and
L. Auslender, R.P.I Math Report No. 98-100, 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. 97-100, 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, 132-145.
- 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, 676-688
- 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. 111-126
- 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. 311-318, 1997.
- Global
Tree Optimization: A Non-Greedy Decision Tree Algorithm. Computing
Science and Statistics, 26, pp. 156-160, 1994
- Serial and Parallel
Multicategory Discrimination, SIAM Journal on Optimization, with O.
L. Mangasarian, 4:4, pp. 722-734, 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. 27-39, 1993.
- Bilinear
Separation of Two Sets in n-Space, with O. L. Mangasarian, Computational
Optimization and Applications, 2, pp. 207-227, 1993.
- Decision Tree Construction Via Linear
Programming, Proceedings of the 4th Midwest Artificial
Intelligence and Cognitive Science Society Conference, Utica,
Illinois, pp. 97-101, 1992.
- Robust
Linear Programming Discrimination of Two Linearly Inseparable Sets.
with O. L. Mangasarian, Optimization Methods and Software, 1, pp.
23-34, 1992.
- Neural Network Training Via Linear
Programming, with O. L. Mangasarian, in P. M. Pardalos (ed.), Advances
in Optimization and Parallel Computing, pp. 56-67, 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, and National Institutes of Health.
Courses Taught
- Nonlinear Programming (Fall
2008)
- Probability Theory and
Applications (Fall 2008)
- Computational
Optimization (Spring 2008)
- Math in Data Science (Spring
2006)
- Mathematical Models of
Learning and Discovery (Spring 2005)
- Probability Theory and Mathematical
Statistics II (Spring 1997)
- Numerical Optimization (Fall
1996)