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

Contact Info:

Office Location:	112 Old Chemistry Building, 90251, Durham, NC 27708
Office Phone:	(919) 684-4608
Email Address:
Web Page:	https://sayanmuk.github.io/

Teaching (Spring 2019):

• STA 561D.001, PROBABILISTIC MACHINE LEARNING Synopsis

  LSRC B101, WF 10:05 AM-11:20 AM

  (also cross-listed as COMPSCI 571D.001, ECE 682D.001)

• STA 561D.002, PROBABILISTIC MACHINE LEARNING Synopsis

  LSRC B101, WF 10:05 AM-11:20 AM

  (also cross-listed as COMPSCI 571D.002, ECE 682D.002)

• STA 561D.003, PROBABILISTIC MACHINE LEARNING Synopsis

  LSRC B101, WF 10:05 AM-11:20 AM

  (also cross-listed as COMPSCI 571D.003, ECE 682D.003)

• STA 561D.01D, PROBABILISTIC MACHINE LEARNING Synopsis

  Soc/Psych 127, M 03:05 PM-04:20 PM

  (also cross-listed as COMPSCI 571D.01D, ECE 682D.01D)

• STA 561D.02D, PROBABILISTIC MACHINE LEARNING Synopsis

  Social Sciences 124, M 10:05 AM-11:20 AM

  (also cross-listed as COMPSCI 571D.02D, ECE 682D.02D)

• STA 561D.03D, PROBABILISTIC MACHINE LEARNING Synopsis

  Social Sciences 124, M 11:45 AM-01:00 PM

  (also cross-listed as COMPSCI 571D.03D, ECE 682D.03D)

• STA 561D.04D, PROBABILISTIC MACHINE LEARNING Synopsis

  Social Sciences 311, M 01:25 PM-02:40 PM

  (also cross-listed as COMPSCI 571D.04D, ECE 682D.04D)

• STA 561D.05D, PROBABILISTIC MACHINE LEARNING Synopsis

  Soc/Psych 127, M 04:40 PM-05:55 PM

  (also cross-listed as COMPSCI 571D.05D, ECE 682D.05D)

• STA 561D.06D, PROBABILISTIC MACHINE LEARNING Synopsis

  Social Sciences 124, M 08:30 AM-09:45 AM

  (also cross-listed as COMPSCI 571D.06D, ECE 682D.06D)

• STA 561D.07D, PROBABILISTIC MACHINE LEARNING Synopsis

  Hudson 216, M 04:40 PM-05:55 PM

  (also cross-listed as COMPSCI 571D.07D, ECE 682D.07D)

• MATH 163FS.01, MATHEMATICS OF DATA SCIENCE Synopsis

  Allen 226, TuTh 11:45 AM-01:00 PM

• STA 790-1.03, SPECIAL TOPICS Synopsis

  Old Chem 101, TuTh 10:05 AM-11:20 AM

  (also cross-listed as MATH 690-40.02)

Education:

PhD

Massachusetts Institute of Technology

2001

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

• 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]

Recent Grant Support

• Gene Expression Programs of Lactic Acidosis in Human Cancers, National Institutes of Health, R01 CA125618-01, 2006/12-2111/11.      
• III: Small: Cumulon: Easy and Efficient Statistical Big-Data Analysis in the Cloud, National Science Foundation, IIS-1320357, 2013/09-2017/08.