Math @ Duke
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Sayan Mukherjee, Professor of Statistical 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)
- Education:
PhD | Massachusetts Institute of Technology | 2001 |
- Specialties:
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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)
- 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]
- 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, vol. 102 no. 43
(October, 2005),
pp. 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, vol. 355 no. 6
(2006),
pp. 570-580 [pdf]
- 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.
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dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
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Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320
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