Ishanu 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 health care. His current work focuses 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 an age of ubiquitous data.
Chattopadhyay received an MS and PhD in mechanical engineering, as well as an MA in mathematics, from the Pennsylvania State University. He completed his postdoctoral training and served as a research associate in the Department of Mechanical Engineering at Penn State. He also held a postdoctoral fellowship simultaneously at the Department of Computer Science and the Sibley School of Mechanical and Aerospace Engineering at Cornell University.
Cornell University
NY
Postdoctoral Fellow - Machine Learning
2013
The Pennsylvania State University
PA
Postdoctoral Fellow - Robotics & Automated Decision-making
2011
The Pennsylvania State University
PA
PhD - Mechanical Engineering
2006
The Pennsylvania State University
PA
MA - Mathematics
2006
The Pennsylvania State University
PA
MS - Mechanical Engineering
2005
A digital twin of the infant microbiome to predict neurodevelopmental deficits.
A digital twin of the infant microbiome to predict neurodevelopmental deficits. Sci Adv. 2024 Apr 12; 10(15):eadj0400.
PMID: 38598636
Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.
Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report. Am J Respir Crit Care Med. 2024 Feb 15; 209(4):362-373.
PMID: 38113442
Pulmonary Fibrosis Stakeholder Summit: A Joint National Heart, Lung, and Blood Institute, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.
Pulmonary Fibrosis Stakeholder Summit: A Joint National Heart, Lung, and Blood Institute, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report. Am J Respir Crit Care Med. 2023 Dec 19.
PMID: 38113442
Clinical characterization and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)-1.
Clinical characterization and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)-1. Schizophr Res. 2023 10; 260:143-151.
PMID: 37657281
Predictive Equity in Suicide Risk Screening.
Predictive Equity in Suicide Risk Screening. J Acad Consult Liaison Psychiatry. 2023 Jul-Aug; 64(4):336-339.
PMID: 37001640
Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring.
Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring. J Am Coll Cardiol. 2023 03 14; 81(10):949-961.
PMID: 36889873
Efficient Determination of Social Determinants of Health From Clinical Notes for Timely Identification of Suicidality Among US Veterans.
Efficient Determination of Social Determinants of Health From Clinical Notes for Timely Identification of Suicidality Among US Veterans. JAMA Netw Open. 2023 03 01; 6(3):e233086.
PMID: 36920398
Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records.
Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records. Nat Med. 2022 10; 28(10):2107-2116.
PMID: 36175678
Cardiac Comorbidity Risk Score: Zero-Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty.
Cardiac Comorbidity Risk Score: Zero-Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty. J Am Heart Assoc. 2022 08 02; 11(15):e023745.
PMID: 35904198
Event-level prediction of urban crime reveals a signature of enforcement bias in US cities.
Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nat Hum Behav. 2022 08; 6(8):1056-1068.
PMID: 35773401
Young Faculty Award
DARPA
2020 - 2020