2026 Sayan Mukherjee: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	60	3144	3052	1522	1465	222	78038

Overview

Sayan Mukherjee is affiliated with Duke University in the United States and has contributed extensively to research primarily in the field of Computer Science. Their work has a significant emphasis on Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, Statistics and Probability, and Sociology and Political Science.

Their principal research topics include:

Chaos control and synchronization
Complex Systems and Time Series Analysis
Topological and Geometric Data Analysis
Markov Chains and Monte Carlo Methods
Statistical Mechanics and Entropy
Machine Learning and Extreme Learning Machines (ELM)
Neural Networks and Applications

Sayan Mukherjee has authored papers in various publication venues, with a notable concentration on:

arXiv (Cornell University)
UNC Libraries
The European Physical Journal Special Topics
Complexity
Nonlinear Dynamics

Frequent collaborators in their research include Michele Caprio, Santo Banerjee, Hayder Natiq, Parthasakha Das, and Pritha Das. These coauthors have worked alongside Mukherjee on multiple projects.

Some selected recent publications by Sayan Mukherjee are:

"Acceptance of Location-Based Advertising by Young Consumers: A Stimulus-Organism-Response (S-O-R) Model Perspective," 2023, Information Systems Management
"Multistability and chaos in a noise-induced blood flow," 2021, The European Physical Journal Special Topics
"Characterizing chaos and multifractality in noise-assisted tumor-immune interplay," 2020, Nonlinear Dynamics
"Multistability and chaotic scenario in a quantum pair-ion plasma," 2020, Zeitschrift für Naturforschung A
"Dynamical Complexity and Multistability in a Novel Lunar Wake Plasma System," 2020, Complexity

Best Publications

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Aravind Subramanian;Pablo Tamayo;Vamsi K. Mootha;Sayan Mukherjee

57054
Citations
Choosing Multiple Parameters for Support Vector Machines

Olivier Chapelle;Vladimir Vapnik;Olivier Bousquet;Sayan Mukherjee

3480
Citations
Support Vector Method for Multivariate Density Estimation

Vladimir Vapnik;Sayan Mukherjee

2869
Citations
Multiclass cancer diagnosis using tumor gene expression signatures

Sridhar Ramaswamy;Pablo Tamayo;Ryan Rifkin;Sayan Mukherjee

2659
Citations
Feature Selection for SVMs

Jason Weston;Sayan Mukherjee;Olivier Chapelle;Massimiliano Pontil

1498
Citations
Nonlinear prediction of chaotic time series using support vector machines

S. Mukherjee;E. Osuna;F. Girosi

880
Citations
An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis

Alejandro Sweet-Cordero;Sayan Mukherjee;Sayan Mukherjee;Aravind Subramanian;Han You

512
Citations
Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia

Kevin J. Galinsky;Kevin J. Galinsky;Gaurav Bhatia;Po-Ru Loh;Po-Ru Loh;Stoyan Georgiev

452
Citations
Molecular classification of multiple tumor types.

Chen-Hsiang Yeang;Sridhar Ramaswamy;Pablo Tamayo;Sayan Mukherjee

403
Citations
General conditions for predictivity in learning theory

Tomaso Poggio;Ryan Rifkin;Ryan Rifkin;Sayan Mukherjee;Sayan Mukherjee;Partha Niyogi

377
Citations
Estimating dataset size requirements for classifying DNA microarray data.

Sayan Mukherjee;Pablo Tamayo;Simon Rogers;Ryan M. Rifkin

374
Citations
Probability measures on the space of persistence diagrams

Yuriy Mileyko;Sayan Mukherjee;John Harer

320
Citations
Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex

Daniela Tropea;Gabriel Kreiman;Alvin Lyckman;Alvin Lyckman;Sayan Mukherjee;Sayan Mukherjee

289
Citations
Fréchet Means for Distributions of Persistence Diagrams

Katharine Turner;Yuriy Mileyko;Sayan Mukherjee;John Harer

285
Citations
Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization

Sayan Mukherjee;Sayan Mukherjee;Partha Niyogi;Tomaso A. Poggio;Ryan M. Rifkin;Ryan M. Rifkin

282
Citations
Support Vector Machine Classification of Microarray Data

Sayan Mukherjee;Ryan Rifkin

280
Citations
Optimal gene expression analysis by microarrays

Lance D. Miller;Philip M. Long;Philip M. Long;Limsoon Wong;Sayan Mukherjee

220
Citations
Naught all zeros in sequence count data are the same.

Justin D. Silverman;Kimberly Roche;Sayan Mukherjee;Lawrence A. David

196
Citations
Persistent homology transform for modeling shapes and surfaces

Katharine Turner;Sayan Mukherjee;Doug M. Boyer

179
Citations
A New Fully Automated Approach for Aligning and Comparing Shapes

Doug M. Boyer;Jesus Puente;Justin T. Gladman;Chris Glynn

165
Citations
Permutation tests for classification

Polina Golland;Feng Liang;Sayan Mukherjee;Dmitry Panchenko

145
Citations

Frequent Co-Authors

Joseph R. Nevins Duke University

Tomaso Poggio MIT

Terrence S. Furey University of North Carolina at Chapel Hill

Po-Ru Loh Harvard Medical School

Alkes L. Price Harvard University

Jill P. Mesirov University of California, San Diego

David B. Dunson Duke University

Alvin R. Lebeck Duke University

Pablo Tamayo University of California, San Diego

Eric S. Lander Broad Institute

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