Duke Probability Theory and Applications
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Sayan Mukherjee, Assistant Professor of Statistical Science and Computer Science

Contact Info:
Office Location:  223C Old Chemistry Bldg.
Office Phone:  919 684-4608
Email Address: send me a message
Web Page:  http://www.genome.duke.edu/labs/mukherjee/

Teaching (Fall 2008):

  • STA 113.001, PROBABIL/STATIS IN EGR Synopsis
    White Lecture Hall 107, TuTh 01:15 PM-02:30 PM
Education:

PhDMassachusetts Institute of Technology2001
Specialties:

Data Mining and Machine Learning
Nonparametric Statistical Modelling
Stochastic Processes
Bayesian Statistics
High-dimensional Statistical Models
Research Interests: Computational biology, mathematical statistics, machine learning, non-parametric Bayesian statistics

My research interests are in computational biology and machine learning.

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.      
  • 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.      
  • Collaborative Research: Probabilistic models and geometry for high dimensional data, NSF DMS, DMS-0732260, 2007/01-2010/08.      
  • CEER grant to explore ethical, legal, and social issues in genomics, NIH NHGRI, P50 HG 03391-03, 2004/09-2009/07.