Duke Probability Theory and Applications
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Sayan Mukherjee, Professor of Statistical Science and Computer Science and Mathematics and Professor in Biostatistics and Bioinformatics of Biostatistics & Bioinformatics

Sayan Mukherjee
Contact Info:
Office Location:  112 Old Chemistry Building, 90251, Durham, NC 27708
Office Phone:  (919) 684-4608
Email Address: send me a message
Web Page:  http://www.stat.duke.edu/~sayan

Teaching (Spring 2017):

  • MATH 230.01, PROBABILITY Synopsis
    Old Chem 116, TuTh 01:25 PM-02:40 PM
    (also cross-listed as STA 230.01)
  • CBB 540.01, STAT MTHDS/COMPUTATIONAL BIOLG Synopsis
    Old Chem 025, TuTh 08:30 AM-09:45 AM
    (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. E. Edelman, J. Guinney, J-T. Chi, P.G. Febbo, and S. Mukherjee, Modeling Cancer Progression via Pathway Dependencies, Public Library of Science Computational Biology (Accepted, 2007) [html]
  3. S. Mukherjee, Q. Wu, D-X. Zhou, Learning Gradients and Feature Selection on Manifolds, Annals of Statistics (Submitted, 2007) [html]
  4. Q. Wu, J. Guinney, M. Maggioni, and S. Mukherjee, Learning gradients: predictive models that infer geometry and dependence, Journal of Machine Learning Research (Submitted, 2007) [html]
  5. 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]
  6. F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West, Non-parametric Bayesian kernel models, Biometrika (Submitted, 2007) [html]
  7. 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]
  8. 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]
  9. T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi, Learning Theory: general conditions for predictivity, Nature, vol. 428 (March, 2004), pp. 419-422 [html]