Ishanu Chattopadhyay

Assistant Professor
Research Summary
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.
machine learning, data science, EHR, clinical screening, complex diseases, social systems, predictive analytics, pathogen evolution
  • 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
Biosciences Graduate Program Association
Awards & Honors
  • 2020 - 2020 Young Faculty Award DARPA
  1. Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote?Monitoring. J Am Coll Cardiol. 2023 Mar 14; 81(10):949-961. View in: PubMed

  2. Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records. Nat Med. 2022 10; 28(10):2107-2116. View in: PubMed

  3. 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. View in: PubMed

  4. Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nat Hum Behav. 2022 08; 6(8):1056-1068. View in: PubMed

  5. Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts. PLoS Comput Biol. 2021 10; 17(10):e1009363. View in: PubMed

  6. Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns. Sci Adv. 2021 Oct 08; 7(41):eabf0354. View in: PubMed

  7. Postnatal immune activation causes social deficits in a mouse model of tuberous sclerosis: Role of microglia and clinical implications. Sci Adv. 2021 Sep 17; 7(38):eabf2073. View in: PubMed

  8. Development and Validation of Computerized Adaptive Assessment Tools for the Measurement of Posttraumatic Stress Disorder Among US Military Veterans. JAMA Netw Open. 2021 07 01; 4(7):e2115707. View in: PubMed

  9. Development of a computerized adaptive diagnostic screening tool for psychosis. Schizophr Res. 2022 07; 245:116-121. View in: PubMed

  10. Estimating heritability and genetic correlations from large health datasets in the absence of genetic data. Nat Commun. 2019 12 03; 10(1):5508. View in: PubMed

  11. Conjunction of factors triggering waves of seasonal influenza. Elife. 2018 02 27; 7. View in: PubMed

  12. Data smashing: uncovering lurking order in data. J R Soc Interface. 2014 Dec 06; 11(101):20140826. View in: PubMed

  13. Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples. Proc Natl Acad Sci U S A. 2013 Aug 06; 110(32):12990-5. View in: PubMed

  14. Abductive learning of quantized stochastic processes with probabilistic finite automata. Philos Trans A Math Phys Eng Sci. 2013 Feb 13; 371(1984):20110543. View in: PubMed

  15. Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains. IEEE Trans Syst Man Cybern B Cybern. 2009 Dec; 39(6):1505-15. View in: PubMed

  16. Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence. arXiv:1801.07807. 2018.::::

  17. Causality Networks. arXiv:1406.6651v1. 2014.::::