Math @ Duke

Papers Published
 Singleton, KR; Crawford, L; Tsui, E; Manchester, HE; Maertens, O; Liu, X; Liberti, MV; Magpusao, AN; Stein, EM; Tingley, JP; Frederick, DT; Boland, GM; Flaherty, KT; McCall, SJ; Krepler, C; Sproesser, K; Herlyn, M; Adams, DJ; Locasale, JW; Cichowski, K; Mukherjee, S; Wood, KC, Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYCDriven Evolutionary Convergence.,
Cell Reports, vol. 21 no. 10
(December, 2017),
pp. 27962812 [doi] [abs]
 Darnell, G; Georgiev, S; Mukherjee, S; Engelhardt, BE, Adaptive randomized dimension reduction on massive data,
Journal of machine learning research : JMLR, vol. 18
(November, 2017) [abs]
 Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation.,
The Anatomical Record : Advances in Integrative Anatomy and Evolutionary Biology
(October, 2017) [doi] [abs]
 Crawford, L; Wood, KC; Zhou, X; Mukherjee, S, Bayesian Approximate Kernel Regression With Variable Selection,
Journal of the American Statistical Association
(August, 2017),
pp. 112 [doi]
 Bobrowski, O; Mukherjee, S; Taylor, JE, Topological consistency via kernel estimation,
Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, vol. 23 no. 1
(February, 2017),
pp. 288328 [doi]
 Tan, Z; Mukherjee, S, Partitioned tensor factorizations for learning mixed membership models,
34th International Conference on Machine Learning, Icml 2017, vol. 7
(January, 2017),
pp. 51565165, ISBN 9781510855144 [abs]
 SnyderMackler, N; Majoros, WH; Yuan, ML; Shaver, AO; Gordon, JB; Kopp, GH; Schlebusch, SA; Wall, JD; Alberts, SC; Mukherjee, S; Zhou, X; Tung, J, Efficient GenomeWide Sequencing and LowCoverage Pedigree Analysis from Noninvasively Collected Samples.,
Genetics, vol. 203 no. 2
(June, 2016),
pp. 699714 [doi] [abs]
 Zhao, S; Gao, C; Mukherjee, S; Engelhardt, BE, Bayesian group factor analysis with structured sparsity,
Journal of machine learning research : JMLR, vol. 17
(April, 2016),
pp. 147 [abs]
 Galinsky, KJ; Bhatia, G; Loh, PR; Georgiev, S; Mukherjee, S; Patterson, NJ; Price, AL, Fast PrincipalComponent Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia.,
The American Journal of Human Genetics, vol. 98 no. 3
(March, 2016),
pp. 456472 [doi] [abs]
 Munch, E; Turner, K; Bendich, P; Mukherjee, S; Mattingly, J; Harer, J, Probabilistic Fréchet means for time varying persistence diagrams,
Electronic Journal of Statistics, vol. 9 no. 1
(January, 2015),
pp. 11731204 [repository], [doi] [abs]
 Raskutti, G; Mukherjee, S, The information geometry of mirror descent,
Lecture notes in computer science, vol. 9389
(January, 2015),
pp. 359368, ISBN 9783319250397 [doi] [abs]
 Stewart, L; MacLean, EL; Ivy, D; Woods, V; Cohen, E; Rodriguez, K; McIntyre, M; Mukherjee, S; Call, J; Kaminski, J; Miklósi, Á; Wrangham, RW; Hare, B, Citizen Science as a New Tool in Dog Cognition Research.,
PloS one, vol. 10 no. 9
(January, 2015),
pp. e0135176 [doi] [abs]
 Bonnefoi H, Potti A, Delorenzi M, Mauriac L, Campone M, TubianaHulin M, Petit T, Rouanet P, Jassem J, Blot E, Becette V, Farmer P, André S, Acharya CR, Mukherjee S, Cameron D, Bergh J, Nevins JR, Iggo RD., Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: a substudy of the EORTC 10994/BIG 0001 clinical trial.,
Lancet Oncology, vol. 8 no. 12
(December, 2007),
pp. 10711078 [entrez]
 F. Liang, S. Mukherjee, M. West, Understanding the use of unlabelled data in predictive modelling,
Statistical Science, vol. 22 no. 2
(Fall, 2007),
pp. 189205
 JenTsan Chi1, Edwin H. Rodriguez, Zhen Wang, Dimitry S. A. Nuyten, Sayan Mukherjee, Matt van de Rijn, Marc J. van de Vijver, Trevor Hastie, Patrick O. Brown, Gene Expression Programs of Human Smooth Muscle Cells: TissueSpecific Differentiation and Prognostic Significance in Breast Cancers,
PLoS Genet, vol. 3 no. 9
(September, 2007),
pp. 17701784 [pdf]
 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]
 Liang Goh, Susan K. Murphy, Sayan Muhkerjee, and Terrence S. Furey, Genomic sweeping for hypermethylated genes,
Bioinformatics, vol. 23 no. 3
(February, 2007),
pp. 281288 [btl620v1]
 Zhong Wang, Huntington F. Willard, Sayan Mukherjee, Terrence S. Furey, Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation,
Public Library of Science Computational Biology, vol. 2 no. 9
(Winter, 2006),
pp. 979988 [available here]
 S. Mukherjee and Q. Wu, Estimation of Gradients and Coordinate Covariation in Classification,
Journal of Machine Learning Research, vol. 7
(November, 2006),
pp. 24812514 [html]
 S. Mukherjee, DX. Zhou, Learning Coordinate Covariances via Gradients,
Journal of Machine Learning Research, vol. 7
(March, 2006),
pp. 519549 [html]
 Elena Edelman, Alessandro Porrello, Justin Guinney, BalaBalakumaran, Andrea Bild, Phillip G. Febbo, and Sayan Mukherjee, Analysis of Sample Set Enrichment Scores: assaying the enrichment of sets of genes for individual samples in genomewide expression profiles,
Bioinformatics, vol. 22 no. 14
(2006),
pp. e101e116 [e108]
 Daniela Tropea, Gabriel Kreiman, Alvin Lyckman, Sayan Mukherjee, Hongbo Yu, Sam Horng and Mriganka Sur, Gene expression changes and molecular pathways mediating activitydependent plasticity in visual cortex,
Nature Neuroscience, vol. 9
(2006),
pp. 660668 [html]
 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]
 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. Rakhlin, D. Panchenko, S. Mukherjee, Risk Bounds for Mixture Density Estimation,
ESAIM: Probability and Statistics, vol. 9
(June, 2005),
pp. 220229
 SweetCordero, A., Mukherjee, S., You, H., Subramnian, S., Ladd, C., Roix, J., Mesirov, J.P., Golub, T.R., Jacks, T, An oncogenic KRAS2 expression signature identified by crossspecies geneexpression analysis,
Nature Genetics, vol. 37 no. 1
(January, 2005),
pp. 4855 [html]
 A. Rakhlin, S. Mukherjee, T. Poggio, Stability Results In Learning Theory,
Analysis and Applications, vol. 3 no. 4
(2005),
pp. 397–417 [html]
 P. Golland, F. Liang, S. Mukherjee, D. Panchenko, Permutation Tests for Classification,
in Proceedings of Computational Learning Theory 2005, edited by P. Auer and R. Meir
(2005),
pp. 501515, SpringerVerlag
 S. Mukherjee, P. Niyogi, T. Poggio, R. Rifkin, Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization,
Advances in Computational Mathematics, vol. 25 no. 13
(2005),
pp. 161  193 [contribution.asp]
 R. Berger, PG. Febbo, PK. Majumder, JJ. Zhao, S. Mukherjee, T Campbell, WR. Sellers, TM. Roberts, M. Loda, TR. Golub, WC. Hahan, AndrogenInduced Differentiation and Tumorigenicity of Human Prostate Epithelial Cells,
Cancer Research, vol. 64
(December, 2004),
pp. 88678875 [8867]
 T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi, Learning Theory: general conditions for predictivity,
Nature, vol. 428
(March, 2004),
pp. 419422 [html]
 R. Rifkin, S. Mukherjee, P. Tamayo, S. Ramaswamy, CH. Yeang, M. Reich, T. Poggio, ES. Lander, TR. Golub, JP. Mesirov, An Analytical Method for MultiClass Cancer Classification,
SIAM Reviews, vol. 45 no. 4
(Winter, 2003),
pp. 706723 [html]
 S. Mukherjee, P. Tamayo , S. Rogers, R. Rifkin, A. Engle, C. Campbell, TR. Golub, JP. Mesirov, Estimating Dataset Size Requirements for Classifying DNA Microarray Data,
Journal of Computational Biology, vol. 10 no. 2
(April, 2003),
pp. 119142 [10.1089%2F106652703321825928]
 LD. Miller, PM. Long,L. Wong, S. Mukherjee, LM. McShane, ET. Liu, Optimal gene expression analysis by microarrays,
Cancer Cell, vol. 2
(November, 2002),
pp. 353361 [abstract]
 S. Pomeroy, P. Tamayo, M. Gaasenbeek, L. Sturla, M. Angelo, M. McLaughlin, J. Kim, L. Goumnerova, P. Black, C. Lau, J. Allen, D. Zigzag, J. Olson, T. Curran, C. Wetmore, J. Biegel, T. Poggio, S. Mukherjee, R. Rifkin, A. Califano, G. Stolovitzky, D. Louis, Prediction of central nervous system embryonal tumour outcome based of gene expression,
Nature, vol. 415 no. 24
(January, 2002),
pp. 436442 [html]
 Mukherjee, N; Mukherjee, S, Predicting signal peptides with support vector machines,
Lecture notes in computer science, vol. 2388
(January, 2002),
pp. 17, ISBN 354044016X [doi] [abs]
 S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, CH Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, JP. Mesirov, T. Poggio, W. Gerald, M. Loda,, ES. Lander, TR. Golub, Multiclass cancer diagnosis using tumor gene expression signatures,
PNAS, vol. 98 no. 26
(December, 2001),
pp. 1514915154 [15149]
 O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee, Choosing Multiple Parameters for Support Vector Machines,
Machine Learning, vol. 46 no. 13
(March, 2001),
pp. 131159 [contribution.asp]
 Peshkin, L; Mukherjee, S, Bounds on sample size for policy evaluation in Markov environments,
Lecture notes in computer science, vol. 2111
(January, 2001),
pp. 616629, ISSN 03029743 [abs]
 J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik, Feature Selection for SVMs,
in Proceedings of Advances in Neural Information Processing Systems, vol. 14
(2001),
pp. 668674
 CH Yeang, S. Ramaswamy, P. Tamayo, S. Mukherjee, R. Rifkin, M. Angelo, M. Reich, E. Lander, J. Mesirov, T. Golub, Molecular classification of multiple tumor types,
Bioinformatics, vol. 1 no. 1
(2001),
pp. 17 [S316]
 Pontil, M; Mukherjee, S; Girosi, F, On the noise model of support vector machines regression,
in Proceedings of Algorithmic Learning Theory 11th Conference,
Lecture notes in computer science, vol. 1968
(2000),
pp. 316324, Springer, Berlin, ISBN 9783540412373 [abs]
 V. Vapnik, S. Mukherjee, Support vector method for multivariate density estimation,
in Proceedings of Advances in Neural Information Processing Systems, edited by S. A. Solla, T. K. Leen, and K.R. Muller, vol. 12
(2000),
pp. 659665
Papers Accepted
 E. Edelman, J. Guinney, JT. Chi, P.G. Febbo, and S. Mukherjee, Modeling Cancer Progression via Pathway Dependencies,
Public Library of Science Computational Biology
(2007) [html]
Papers Submitted
 Q. Wu, S. Mukherjee, F. Liang, Regularized sliced inverse regression for kernel models.,
Biometrika
(2007) [html]
 F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West, Nonparametric Bayesian kernel models,
Biometrika
(2007) [html]
 J. Guinney, Q. Wu, and S. Mukherjee, Estimating variable structure and dependence in Multitask learning via gradients,
Journal of Machine Learning Research
(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
(2007) [html]
 S. Mukherjee, Q. Wu, DX. Zhou, Learning Gradients and Feature Selection on Manifolds,
Annals of Statistics
(2007) [html]
Chapters
 Huang, B; Jarrett, NWD; Babu, S; Mukherjee, S; Yang, J, Cümülön: MatrixBased data analytics in the cloud with spot instances,
in Proceedings of the VLDB Endowment, vol. 9
(January, 2016),
pp. 156167 [abs]


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

