The papers listed below are recent papers and preprints on topics related to my current research interests. For a comprehensive listing of my papers, see my Google Scholar profile.


  1. [Evan Kodra, Udit Bhatia, Snigdhansu Chatterjee , Stone Chen and Auroop Ratan Ganguly ] Physics-guided probabilistic modeling of extreme precipitation under climate change, Nature Scientific Reports volume 10, Article number: 10299 (2020).
  2. [S. Bera and S. Chatterjee ] High Dimensional, Robust, Unsupervised Record Linkage, Statistics in Transition Volume 21, Issue 4, 14 September 2020, Pages 123-143.
  3. [Jun Young Park, Joerg Polzehl, Snigdhansu Chatterjee , Andre Brechmann, and Mark Fiecas] Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data, Computational Statistics & Data Analysis Volume 150, October 2020, 107006.
  4. [S. Majumdar and S. Chatterjee ] On Weighted Multivariate Sign Functions, in review.
  5. [S. Majumdar and S. Chatterjee ] Fast and General Model Selection using Data Depth and Resampling, in revision.
  6. [Subhabrata Majumdar, Saonli Basu, Matt McGue, Snigdhansu Chatterjee ] Simultaneous Selection of Multiple Important Single Nucleotide Polymorphisms in Familial Genome Wide Association Studies Data, in revision.
  7. [Subhabrata Majumdar, Snigdhansu Chatterjee ] Nonconvex penalized multitask regression using data depth-based penalties, Stat 7 (2018) e174, the article webpage.
  8. [Deepak K. Ray , Paul C. West, Michael Clark, James S. Gerber, Alexander V. Prishchepov, and Snigdhansu Chatterjee ] Climate change has likely already affected global food production, PLoS ONE, 14(5): e0217148.
  9. [Saurabh Agrawal, Saurabh Verma, Anuj Karpatne, Stefan Liess, Snigdhansu Chatterjee and Vipin Kumar] A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series.
  10. [ Saurabh Agrawal, Michael Steinbach, Daniel Boley, Snigdhansu Chatterjee , Gowtham Atluri, Anh The Dang, Stefan Liess, Vipin Kumar] Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks.
  11. [ Whitney K. Huang, Daniel S. Cooley, Imme Ebert-Uphoff, Chen Chen, Snigdhansu Chatterjee ] New Exploratory Tools for Extremal Dependence: Chi Networks and Annual Extremal Networks, Journal of Agricultural, Biological and Environmental Statistics volume 24, pages484–501(2019), the article webpage.
  12. [ Saurabh Agrawal, Saurabh Verma, Gowtham Atluri, Anuj Karpatne, Stefan Liess, Angus Macdonald III, Snigdhansu Chatterjee , Vipin Kumar] Mining Sub-Interval Relationships In Time Series Data.
  13. [A. Braverman, S. Chatterjee , M. Heyman and N. Cressie] Probabilistic Evaluation of Competing Climate Models, Advances in Statistical Climatology, Meteorology and Oceanography, 3, 93–105, (2017), the article webpage.
  14. [S. Agrawal, G. Atluri, A. Karpatne, W. Haltom, S. Liess, S. Chatterjee , and Vipin Kumar] Tripoles: A New Class of Relationships in Time Series Data, accepted for publication in Knowledge Discovery and Data Mining (KDD) 2017 conference proceedings.
  15. [S. Liess, S. Agrawal, S. Chatterjee , and Vipin Kumar] A Teleconnection between the West Siberian Plain and the ENSO Region, Journal of Climate, 30(1), 301-315, the article webpage.
  16. [N. , Y. Louzoun, L. Gragert, M. Maiers, S. Chatterjee , and M. Albrecht] Power Laws for Heavy-Tailed Distributions: Modeling Allele and Haplotype Diversity for the National Marrow Donor Program, PLOS Computational Biology 11(4): e1004204, the article webpage.