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

Sayan Mukherjee
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 2014):

  • STA 561D.01, PROBABILISTIC MACHINE LEARNING Synopsis
    Social Sciences 139, MW 08:30 AM-09:45 AM
    (also cross-listed as COMPSCI 571D.01)
  • STA 561D.01D, PROBABILISTIC MACHINE LEARNING Synopsis
    Bio Sci 154, F 10:05 AM-11:20 AM
    (also cross-listed as COMPSCI 571D.01D)
  • STA 561D.02D, PROBABILISTIC MACHINE LEARNING Synopsis
    Old Chem 025, F 08:30 AM-09:45 AM
    (also cross-listed as COMPSCI 571D.02D)
  • STA 561D.03D, PROBABILISTIC MACHINE LEARNING Synopsis
    Old Chem 123, F 11:45 AM-01:00 PM
    (also cross-listed as COMPSCI 571D.03D)
  • STA 561D.04D, PROBABILISTIC MACHINE LEARNING Synopsis
    Old Chem 025, F 01:25 PM-02:40 PM
    (also cross-listed as COMPSCI 571D.04D)
  • STA 581.01, STATISTICAL SCIENCE PROSEMINAR Synopsis
    Old Chem 025, W 03:05 PM-04:20 PM
Teaching (Spring 2015):

  • CBB 540.01, STAT MTHDS/COMPUTATIONAL BIOLG Synopsis
    Soc/Psych 127, TuTh 03:05 PM-04:20 PM
    (also cross-listed as STA 613.01)
Education:

PhDMassachusetts Institute of Technology2001
Specialties:

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