Sayan Mukherjee, Associate Professor of Statistical Science and Mathematics

Contact Info:

Office Location:	112 Old Chemistry
Office Phone:	(919) 684-4608
Email Address:
Web Page:	http://www.genome.duke.edu/labs/mukherjee/

Teaching (Fall 2013):

• STA 790.01, SPECIAL TOPICS Synopsis

  Social Sciences 119, Tu 04:40 PM-07:40 PM

Education:

PhD

Massachusetts Institute of Technology

2001

Specialties:

Data Mining and Machine Learning
Nonparametric Statistical Modelling
Stochastic Processes
Bayesian Statistics
High-dimensional Statistical Models
Graphical Models

Research Interests: Computational biology, geometry and topology, machine learning

My research interests are in computational biology and machine learning. In both contexts I am interested in using geometry to improve statistical models for high-dimensional data.

Postdocs Mentored

• Qiang Wu (2005 - present)  

Representative Publications   (More Publications)

1. Natesh Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert, Characterizing the function space for Bayesian kernel models, Journal of Machine Learning Research, vol. 8 (August, 2007), pp. 1769--1797 [html]  [abs]
2. S. Mukherjee, Q. Wu, D-X. Zhou, Learning Gradients and Feature Selection on Manifolds, Annals of Statistics (Submitted, 2007) [html]
3. J. Guinney, Q. Wu, and S. Mukherjee, Estimating variable structure and dependence in Multi-task learning via gradients, Journal of Machine Learning Research (Submitted, 2007) [html]
4. F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West, Non-parametric Bayesian kernel models, Biometrika (Submitted, 2007) [html]
5. A. Subramanian, P. Tamayo, VK. Mootha, S. Mukherjee, BL. Ebert, MA. Gillette, A. Paulovich, SL. Pomeroy, TR. Golub, ES. Lander, JP. Mesirov, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, PNAS, vol. 102 no. 43 (October, 2005), pp. 15278-9 [15545]
6. A. Potti, S. Mukherjee, R. Petersen, HK. Dressman, A. Bild, J. Koontz, R. Kratzke, MA. Watson, M. Kelley, A Genomic Strategy to Refine Prognosis in Early Stage Non-Small Cell Lung Carcinoma, New England Journal of Medicine, vol. 355 no. 6 (2006), pp. 570-580 [pdf]
7. T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi, Learning Theory: general conditions for predictivity, Nature, vol. 428 (March, 2004), pp. 419-422 [html]

Recent Grant Support

• Gene Expression Programs of Lactic Acidosis in Human Cancers, National Institutes of Health, R01 CA125618-01, 2006/12-2111/11.      
• EMSW21-RTG: Geometric, Topological and Statistical Methods for Analyzing Massive Datasets, National Science Foundation, DMS-1045153, 2011/08-2016/07.      
• Inferring Network Controls from Topology Using the CHomP Database, Air Force Office of Scientific Research, FA9550-10-1-0436, 2010/09-2015/09.      
• Knowledge Enhanced Exapixel Photography, Defense Advanced Research Projects Agency, N66001-11-1-4002-P00005, 2012/10-2013/09.      
• mTOR Therapy in Prostate Cancer: Signatures of Response and Biology of Resistance, National Institutes of Health, R01 CA123175-01, 2007/09-2012/07.      
• Duke Systems Biology Center Grant, National Institutes of Health, P50 GM 081883, 2007/07-2012/06.