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Research Interests for Sayan Mukherjee

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.

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]
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320