Math @ Duke

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) 6844608  Email Address:   Web Page:  https://sayanmuk.github.io/  Teaching (Fall 2018):
 HOUSECS 59.10, HOUSE COURSE (SP TOP)
Synopsis
 Few Quad 101, M 07:30 PM09:00 PM
 MATH 163FS.01, MATHEMATICS OF DATA SCIENCE
Synopsis
 Bio Sci 113, TuTh 11:45 AM01:00 PM
 (also crosslisted as STA 115FS.01)
 Education:
PhD  Massachusetts Institute of Technology  2001 
 Specialties:

Data Mining and Machine Learning
Nonparametric Statistical Modelling Stochastic Processes Bayesian Statistics Highdimensional 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 highdimensional 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. 17691797 [html] [abs]
 E. Edelman, J. Guinney, JT. 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, DX. 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 Multitask learning via gradients,
Journal of Machine Learning Research
(Submitted, 2007) [html]
 F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West, Nonparametric 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 knowledgebased approach for interpreting genomewide expression profiles,
PNAS, vol. 102 no. 43
(October, 2005),
pp. 152789 [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 NonSmall Cell Lung Carcinoma,
New England Journal of Medicine, vol. 355 no. 6
(2006),
pp. 570580 [pdf]
 T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi, Learning Theory: general conditions for predictivity,
Nature, vol. 428
(March, 2004),
pp. 419422 [html]
 Recent Grant Support
 Gene Expression Programs of Lactic Acidosis in Human Cancers, National Institutes of Health, R01 CA12561801, 2006/122111/11.
 III: Small: Cumulon: Easy and Efficient Statistical BigData Analysis in the Cloud, National Science Foundation, IIS1320357, 2013/092017/08.


dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
 
Mathematics Department
Duke University, Box 90320
Durham, NC 277080320

