معرفی
Sayan Mukherjee is a Professor of Statistical Science, Computer Science, and Mathematics at Duke University, with affiliations at the Information Initiative at Duke. His academic work spans multiple departments, reflecting his interdisciplinary research approach.
His research focuses on three main areas: Geometry and topology in probabilistic modeling, where he develops methods for modeling geometric and topological properties of objects/data with uncertainty; Modeling massive data, building statistical models and algorithms for dimension reduction, classification, and matrix completion that scale to huge datasets; and Applied Bayesian models in computational biology, collaborating with biologists on projects ranging from functional genomics to population biology.
Mukherjee has mentored numerous graduate students and postdoctoral fellows who have gone on to positions at institutions including Harvard, U Chicago, Duke, UNC, and industry positions at companies like Google, Novartis, and SAS. His collaborative network spans across multiple disciplines including geometry, topology, learning theory, cancer biology, computational biology, and evolutionary ecology.
He has developed significant software tools including Bayesian Sparse Factor Analysis of Genetic Covariance Matrices (BSFG), Automated 3D Geometric Morphometrics (auto3dgm), and Analysis of Sample Set Enrichment Scores (ASSESS), demonstrating his commitment to creating practical computational methods for complex biological and statistical problems.
