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Rensselaer Exploratory Center for Cheminformatics Research (RECCR)

Kristin P Bennett

Kristin P Bennett
Sun, 2010-06-13 02:05 — bennek
Name: Kristin P Bennett
Title:Professor
Department Computer Science Lally School of Management and Technology Mathematical Sciences
School Lally School of Management and Technology Engineering Science
Center Center for Biotechnology and Interdisciplinary Studies (CBIS) Data Science Research Center (DSRC) Rensselaer Exploratory Center for Cheminformatics Research (RECCR)
Website:http://homepages.rpi.edu/~bennek/
Bio 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.
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Education Ph.D., University of Wisconsin, Madison, 1993
Scholarly Works:
  • 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.

Curt Breneman

Curt Breneman
Sun, 2010-06-13 02:05 — brenec
Name: Curt Breneman
Title:Professor & Acting Dept. Head
Department Chemistry and Chemical Biology
School Science
Center Rensselaer Exploratory Center for Cheminformatics Research (RECCR)
Website:http://rpi.edu/dept/chem/chem_faculty/profiles/breneman.html
Bio Curt Breneman was born in Santa Monica, California in 1956, and went on to earn a B.S. in Chemistry at UCLA in 1980 followed by a Ph.D. in Chemistry at UC Santa Barbara (with an emphasis on Physical Organic and Computational Chemistry) in 1987. Following two years of post-doctoral research at Yale University, Dr. Breneman joined the faculty of the Department of Chemistry at Rensselaer Polytechnic Institute (RPI) and began a program in molecular recognition and computational chemistry based on his concept of "Transferable Atom Equivalents", or TAEs, as building blocks for describing the electronic and reactive character of molecules. Dr. Breneman currently holds the rank of Full Professor in the RPI Department of Chemistry and Chemical Biology, and is the Director of the NIH RECCR Center.

The Breneman research group primarily specializes in the development of new molecular property descriptors and machine learning methods that can be applied to a diverse set of physical and biochemical problems. Of paramount interest are methods that can increase the information content of molecular descriptors, and machine learning techniques that can exploit this data for the creation of fully validated, predictive property models. Current application areas include pharmaceutical ADME prediction, virtual high-throughput screening of drug candidates, protein chromatography modeling (HIC and ion-exchange), as well as polymer property prediction.



Details
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Education Ph.D. in Chemistry at UC Santa Barbara (with an emphasis on Physical Organic and Computational Chemistry) 1987
Scholarly Works:
  • Zaretzki, J.; Bergeron, C.; Rydberg, P.; Huang, T.-w.; Bennett, K. P.; Breneman, C. M. "Rs-Predictor: A New Tool for Predicting Sites of Cytochrome P450-Mediated Metabolism Applied to Cyp 3a4" J. Chem. Inf. Model. 2011, 51, 1667-1689.
  • Das, S.; Krein, M. P.; Breneman, C. M. "Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures" J. Chem. Inf. Model. 2010, 50, 298-308.
  • Das, S.; Krein, M. P.; Breneman, C. M. "Pesdserv: A Server for High-Throughput Comparison of Protein Binding Site Surfaces" Bioinformatics 2010, 26, 1913-1914.
  • Morrison, C. J.; Breneman, C. M.; Moore, J. A.; Cramer, S. M. "Evaluation of Chemically Selective Displacer Analogues for Protein Purification" Anal. Chem. 2009, 81, 6186-6194.
  • Das, S.; Kokardekar, A.; Breneman, C. M. "Rapid Comparison of Protein Binding Site Surfaces with Property Encoded Shape Distributions" J. Chem. Inf. Model. 2009, 49, 2863-2872.
  • Sukumar, N.; Krein, M.; Breneman, C. M. "Bioinformatics and Cheminformatics: Where Do the Twain Meet?" Curr. Opin. Drug Discovery Dev. 2008, 11, 311-319.
  • Yang, T.; Sundling, M. C.; Freed, A. S.; Breneman, C. M.; Cramer, S. M. "Prediction of Ph-Dependent Chromatographic Behavior in Ion-Exchange Systems" Anal. Chem. 2007, 79, 8927-8939.

Mohammed J. Zaki

Mohammed J. Zaki
Sun, 2010-06-13 02:05 — zakim
Name: Mohammed J. Zaki
Title:Professor
Department Computer Science
School Science
Center Center for Biotechnology and Interdisciplinary Studies (CBIS) Network Science and Technology Center (NeST) Rensselaer Exploratory Center for Cheminformatics Research (RECCR)
Website:http://www.cs.rpi.edu/~zaki/
Bio Mohammed J. Zaki is a Professor of Computer Science at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining techniques, especially in bioinformatics. He has published over 200 papers and book-chapters on data mining and bioinformatics. We was the founding co-chair for the BIOKDD series of workshops. He is currently an Executive Editor for Statistical Analysis and Data Mining, and an Associate Editor for Data Mining and Knowledge Discovery, ACM Transactions on Knowledge Discovery from Data, Knowledge and Information Systems, ACM Transactions on Intelligent Systems and Technology, Social Networks and Mining, and International Journal of Knowledge Discovery in Bioinformatics. He was the program co-chair for SDM'08, SIGKDD'09 and PAKDD'10. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He also received the ACM Recognition of Service Award in 2003 & 2009, and an IEEE Certificate of Appreciation in 2005. He received the HP Labs Innovation Award in 2010. He is a Senior Member of IEEE, and was recently designated as an ACM Distinguished Scientist.
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Education B.S., Computer Science and Mathematics (dual), May 1993, Angelo State University, San Angelo, Texas M.S., Computer Science, May 1995, University of Rochester, Rochester, New York Ph.D., Computer Science, July 1998, University of Rochester, Rochester, New York
Scholarly Works:
  • Mohammed J. Zaki, Naren Ramakrishnan, Lizhuang Zhao, Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling, International Journal of Knowledge Discovery in Bioinformatics, Jason T.L. Wang (ed.), 2010 (accepted, to appear)
  • Hilmi Yildirim, Vineet Chaoji, Mohammed J. Zaki, GRAIL: Scalable Reachability Index for Large Graphs, Proceedings of the VLDB Endowment, Vol3 ̇, No1 ̇, pp mm-nn, 2010 (Proceed- ings of the 36th International Conference on Very Large Data Bases, Singapore, September 2010).
  • Karam Gouda, Mosab Hassaan, Mohammed J. Zaki, PRISM: An Effective Approach for Frequent Sequence Mining via Prime-Block Encoding, Journal of Computer and Systems Sciences, special issue on Intelligent Data Analysis, Radim Belohlavek and Rudolph Kruse (eds.), Vol. 76, No. 1, pp 88-102, February 2010.
  • Saeed Salem, Mohammed J. Zaki and Chris Bystroff, FlexSnap: Flexible Non-Sequential Pro- tein Structure Alignment, Algorithms in Molecular Biology, Vol. 5, Article 12, 2010.
  • Mohammed J. Zaki, Christopher D. Carothers, and Boleslaw K. Szymanski, VOGUE: A Vari- able Order Hidden Markov Model with Duration based on Frequent Sequence Mining, ACM Transactions on Knowledge Discovery in Data, Vol. 4, No. 1, Article 5, January 2010.
  • Vineet Chaoji, Mohammad Hasan, Saeed Salem, and Mohammed J. Zaki, SPARCL: An Ef- fective and Efficient Algorithm for Mining Arbitrary Shape-based Clusters, Knowledge and Information Systems, invited as one of the best papers of IEEE Int’l Conference on Data Mining (ICDM’08), Vol. 21, No. 2, pp 201-229, November 2009.
  • Mohammad Al Hasan, Mohammed J. Zaki, Output Space Sampling for Graph Patterns, Pro- ceedings of the VLDB Endowment, Vol2 ̇, No1 ̇, pp 730-741, 2009 (Proceedings of the 35th International Conference on Very Large Data Bases, Lyon, France, August 2009).
  • Saeed Salem, Mohammed J. Zaki and Chris Bystroff, Iterative Non-Sequential Protein Struc- tural Alignment, Journal of Bioinformatics and Computational Biology, special issue on the best of CSB’08, Ying Xu and Peter Markstein (eds.), Vol. 7, No. 3, pp 571-596, June 2009.
  • Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Mohammed J. Zaki, An integrated, generic approach to pattern mining: data mining template library, Data Mining and Knowledge Discovery, Vol. 17, No. 3, pp. 457-495, December 2008.
  • Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Jeremy Besson, Mohammed J. Zaki, ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Pat- terns, Statistical Analysis and Data Mining, Vol. 1, Issue 2, pp. 67-84, (DOI: 10.1002/sam.10004) June 2008.
Recognitions:
  • HP Labs Innovation Award, 2010.
  • ACM Distinguished Scientist, 2010-present.
  • ACM SIGKDD PhD Dissertation Award for my student Mohammad Al Hasan, 2010.
  • Program Co-chair, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hy- derabad, India, June 2010.
  • Senior Member, IEEE, 2010-present.
  • Program Co-chair, 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, August 2009.
  • Association of Computing Machinery (ACM) Recognition of Service Award, 2009.
  • Best Paper Award, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand, April 2009.
  • IEEE Computer Society, Certificate of Appreciation, 2005.
  • Association of Computing Machinery (ACM) Recognition of Service Award, 2003.
  • DOE Office of Science Early Career Principal Investigator Award in Applied Mathematics, Computer Science and High-Performance Networks, 2002.
  • NSF Faculty Early Development Award (CAREER Award), 2001.