Sayan Mukherjee, Professor of Mathematics and Statistical Science and Computer Science

Sayan Mukherjee
Office Location:  112
Office Phone:  (919) 684-4608
Email Address: send me a message
Web Page:  http://www.stat.duke.edu/~sayan

Teaching (Fall 2017):

Teaching (Spring 2018):

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, 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

Representative 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]
Recent Grant Support