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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)—
- 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.
- P. Agius, K. P. Bennett, and M. Zuker, “Comparing RNA Secondary Structures using a Relaxed Base Pair Score”, RNA Journal, 16:865-878, 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, “Gradient-type 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, “Multiple-biomarker 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 Knowledge-Based Support Vector Machines”, Proceedings of European Conference on Machine Learning, ECML PKDD 2010, Lecture Notes in Computer Science, 6322, 451-16, 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 runner-up 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, 338-343, 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 1-6, 2008 : Plenary/invited Lectures. Edited By Jacek M. Zurada, Gary G. Yen, Jun Li Jim Wang, Springer, 29-48, 2008
- A. Demiriz, K. P. Bennett, and P. S. Bradley, “Using Assignment Constraints to Avoid Empty Clusters in k-means Clustering”, in Constrained Clustering: Advances in Algorithms, Theory, and Applications, S. Basu, I. Davidson, and K. Wagstaff, CRC Press, 203-219, 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 :129-158, 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 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 (Courses are now available via rpilms.rpi.edu to class participants).
- 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)