Sayan Mukherjee, Research Professor
- Contact Info:
- 112 Old Chemistry Building, 90251, Durham, NC 27708
- sayan.mukherjee@duke.edu
- Personal Web Page:
- https://sayanmuk.github.io/
- Education:
- PhD, Massachusetts Institute of Technology, 2001
- 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.
- STA 270, Stat Mthds/Computational Biolg
- STA 113, Probabil/Statis In Egr
- STA 293, Statistical Learning-Algorithms & Theory
- Representative Publications
(More Publications)
- Natesh Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert. "Characterizing the function space for Bayesian kernel models." Journal of Machine Learning Research 8 (August, 2007): 1769--1797. [html] [abs]
- 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]
- S. Mukherjee, Q. Wu, D-X. Zhou. "Learning Gradients and Feature Selection on Manifolds." Annals of Statistics (Submitted, 2007). [html]
- 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]
- 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]
- F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West. "Non-parametric Bayesian kernel models." Biometrika (Submitted, 2007). [html]
- 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 102.43 (October, 2005): 15278-9. [15545]
- 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 355.6 (2006): 570-580. [pdf]
- T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi. "Learning Theory: general conditions for predictivity." Nature 428 (March, 2004): 419-422. [html]
Typical Courses Taught: