Ishanu Chattopadhyay
Section of Hospital Medicine
Assistant Professor of Medicine

Training

DegreeYearInstitutionArea
MS2005The Pennsylvania State UniversityMechanical Engineering
MA2006The Pennsylvania State UniversityMathematics
PhD2006The Pennsylvania State UniversityMechanical Engineering
Postdoctoral Fellow2008The Pennsylvania State UniversityMechanical Engineering

Academic Interests

Dr. Chattopadhyay’s research focuses on the theory of unsupervised machine learning, and the interplay of stochastic processes and formal language theory, in exploring the mathematical underpinnings of the question of inferring causality from data. His most visible contributions include the algorithms for data smashing, inverse Gillespie inference, and nonparametric nonlinear and zero-knowledge implementations of Granger causal analysis, that have crucial implications for biomedical informatics, data enabled discovery in biomedicine, and personalized precision healthcare. His current work focusses on analyzing massive clinical databases of disparate variables to distill patterns indicative of hitherto unknown etiologies,dependencies and relationships, potentially addressing the daunting computational challenge of scale, and making way for ab initio and de novo modeling in this age of ubiquitous data. In addition to eliminating the possibility of structural biases, Dr. Chattopadhyay’s massively data enabled unsupervised inference algorithms aim for r true pattern discovery, to answer hard questions in biomedicine, and even potentially those that we might not have yet thought of asking.


More Information

For more information about Dr. Ishanu Chattopadhyay publications and research collaborations , please click here