• Data Matter (CMDA 2014):  Undergraduate course with an aim of teaching modern, complex analytic methods to students who are almost completely new to data analytics, to develop fundamental analytical and programming skills to complete the “analytic pipeline”for different data types, e.g., quantitative data, text data, and image data.
• Data Analytics (STAT/CS 5525): Graduate course in statistics and quantitative disciplines covering topics including but not limited to Introductorion to algorithmic thinking, Supervised and unsupervised learning methods (e.g. PCA, Nearest neighbor, Modern regression including penalized regression, Random Forest, SVM, artificial neural network) using the R language.

High-dimensional data, shrinkage prior, sparse signal recovery, structure learning, change point estimation, Compositional data, Grouped covariates, nonparametric Bayes, Cancer genomics, Microbiomics, Ecology, Crime forecasting.

See my Google Scholar profile for a complete and updated list: 
https://scholar.google.com/citations?user=_wA03WkAAAAJ&hl=en

Submitted

1. “Inverse Probability Weighting: from Survey Sampling to Evidence Estimation”, Datta, J., and Nick Polson. preprint:  https://arxiv.org/abs/2204.14121
2. “Merging Two Cultures: Deep and Statistical Learning”.  Bhadra, A., Datta, J., Polson, N. G., Sokolov, V., Xu, J. , preprint: https://arxiv.org/abs/2110.11561
3. “Graphical Evidence“.  Bhadra A., Sagar K. N., Banerjee, S., and Datta, J. preprint: https://arxiv.org/abs/2104.10750 
4. “Precision Matrix Estimation under Horseshoe `like’ Penalty”,  Sagar, K., Banerjee, S., Datta, J., Bhadra, A..  (preprint: https://arxiv.org/abs/2104.10750) 
5. “Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Group-Correlated Covariates”.  Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (preprint: https://arxiv.org/abs/2102.10670)
6. On Posterior consistency of Bayesian Changepoint models,  Guha, N., Datta, J. (preprint: https://arxiv.org/abs/2102.12938) 

Published/In-press:

A. Statistics (Journal):

1. “Extending the Susceptible-Exposed-Infected-Removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy“, Bhaduri, R., Kundu, R., Purkayastha, S., Kleinsasser, M., Beesley, L., Mukherjee, B. and Datta, J. (2022), Statistics in Medicine. 
2. “Discussion on “Regression Models for Understanding COVID-19 Epidemic Dynamics with Incomplete Data””, Datta, J., and Mukherjee, B. (2021). Invited discussion,  Journal of American Statistical Association. 
3. “Joint Mean-Covariance Estimation via the Horseshoe with an Application in Genomic Data Analysis”. Li, Datta, Craig, and Bhadra,  (2021).  Journal of Multivariate Analysis. 
4. “COVID-19 prediction in South Africa: Understanding the unascertained cases — the hidden part of the epidemiological iceberg”. Xuelin Gu, Bhramar Mukherjee, Sonali Das and Jyotishka Datta. Accepted, Journal of Statistical Research (Invited paper for Special Issue), Preprint: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743090/ 
5. “Horseshoe Regularisation for Machine Learning in Complex and Deep Models”, Bhadra, Datta, Li, and Polson (2020), International Statistical Review. [URL]
6. “Prediction risk for global-local shrinkage regression“. Bhadra, Datta, Li, Polson, and Willard (2019), (alphabetical*) Journal of Machine Learning Research. 20 (78), 1-39. 
7. “Lasso Meets Horseshoe – A Survey“, Bhadra, Datta, Polson, and Willard (2019), (alphabetical*). Statistical Science. 34 (3), 405-427. (link to pdf)
8.  “Horseshoe Regularization for Feature Subset Selection”. Bhadra, Datta, Polson, and Willard, Brandon (2017+), (alphabetical*), arXiv. Accepted, Sankhya B – J. K. Ghosh Memorial Issue.
9.  “Global-local mixtures: An Unifying Framework”, Bhadra, Datta, Polson, and Willard (2019), (alphabetical) arXiv. See Prof. Christian Robert’s blog entry: Blog: Global-local mixtures , Featured on Xi’an’s Og ! Accepted, Sankhya A – J. K. Ghosh Memorial Issue. 
10. “The Horseshoe+ Estimator of Ultra-Sparse Signals“, Bayesian Analysis, Bhadra, Datta, Polson, and Willard. (2017)  (alphabetical*). arXiv and software (featured on Andrew Gelman’s blog: Bayesian survival analysis with horseshoe priors – in Stan! ).
11. “Bayesian inference on quasi-sparse count data”. Biometrika 103 (4): 971-983. Datta and Dunson (2016), R markdown pages (Simulation, Slice sampling R, and Stan codes)
12. “Default Bayesian analysis with global-local shrinkage priors”, Biometrika 103 (4): 955-969. Bhadra, Datta, Polson, and Willard. (2016) (alphabetical*). arXiv and software.
13. “Bootstrap : An Exploration”, Statistical Methodology (Special Issue in Memory of Kesar Singh). Datta and Ghosh (2014).
14. “Asymptotic Properties of Bayes Risk for the Horseshoe prior”, Bayesian Analysis, 8 (1), 111-132. Datta and Ghosh (2013), link.

B. Cancer Genomics / Human Genetics : 

1.  “Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma”, Reddy, Anupama, et al. (2017), Cell 171.2: 481-494. Featured on EurekAlert!, the official newsletter for AAAS.
2. “Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2“, with Moffitt, Andrea et al. (2017). Journal of Experimental Medicine, 214(5), 1371-86,
3. “The Genetic Basis of Hepatosplenic T Cell Lymphoma“, with McKinney, Matthew et al. (2017), Cancer Discovery, CD-16-0330,
4. “GNA13 loss in germinal center B cells leads to impaired apoptosis and GC-B cell persistence and promotes lymphoma in vivo”. with Healy et al. (2016). Blood,127 (22), 2723-2731, Link
5. “Integrative Genetic and Clinical Analysis through Whole Exome Sequencing in 1001 Diffuse Large B Cell Lymphoma (DLBCL) Patients Reveals Novel Disease Drivers and Risk Groups”. Zhang et al. (2016). Blood, 128 (22), 1087. [Abstract]
6. “SETD2 Functional Loss through Mutation or Genetic Deletion Promotes Expansion of Normal and Malignant γδ T Cells through Loss of Tumor Suppressor Function and Upregulation of Oncogenic Pathways”. McKinney et al. (2016). Blood. 128 (22):1052; [Abstract]
7. “Correlation of ATP7B gene mutations with clinical phenotype and radiological features in Indian Wilson Disease patients”, Chaudhuri, J.; Biswas, S.; Gangopadhyay, G.; Biswas, T.;  Datta, J.; Biswas, A.; Datta, A.; Mukherjee, A.; Bhattacharya, P.; Hazra, A. 122 (1), 181-190, Acta Neurologica Belgica. 

C. Criminology:

1. “ Innovative Data in Communities and Crime Research: An Example at the Intersection of Racial Segregation, Neighborhood Permeability, and Crime”, Harris, C.; Drawve, G.; Thomas, S.; Datta, J.; Steinman (2022): 1-18, Journal of Crime and Justice. 
2. “Current and New Frontiers: Exploring how Place Matters through Arkansas NIBRS Reporting Practices”. Drawve, Harris, Thomas, Datta, J., Cothren (2020): (Crime & Delinquency).
3. “Risky Business: Examining the 80-20 Rule in Relation to a RTM Framework”, Hannah Steinman, Grant Drawve, Jyotishka Datta, Casey T Harris, Shaun A Thomas (2020), Criminal Justice Review. 

D. Other Interdisciplinary Work:  

1. “Understanding Racial Disparities in Severe Maternal Morbidity Using Bayesian Network Analysis”.  Rezaeiahari, M.; Brown, C. C.; Ali, M. M.; Datta, J.; Tilford, J. M.; (2021), PLoS One ;16(10):e0259258. https://pubmed.ncbi.nlm.nih.gov/34705872/ 
2. “A Meta-Analysis of the Protein Components in the Rattlesnake Venom”. Deshwal, A., Phan, P., Datta, J., Kannan, R., Suresh Kumar, T.K., Toxins, 13 (6), 372.
3. Pediatrics: “Evaluation of malnutrition as a predictor of adverse outcomes in febrile neutropenia associated with pediatric hematological malignancies.” Chaudhuri, Biswas, Datta, J., …, Chakarabrty. (2016),  Journal of Paediatrics and Child Health, 52 (7), 704-709.
4. Neuroscience: “Age-related changes in the relationship between auditory brainstem responses and envelope-following responses”.  Parthasarathy, Datta, J., Torres, Hopkins, Bartlett (2014), Journal of the Association for Research in Otolaryngology,15(4): 649-661. Springer US.
5. Geology: “Geomorphons: Landform and property predictions in a glacial moraine in Indiana landscapes”. Libohova, , Winzeler, Lee, Schoeneberger, Datta, J., and Owens, Phillip R. (2016).  Catena 2016 v.142.

Book chapters

1. “Challenges and limitations of geospatial data in the context of COVID-19” (Book chapter), Sean G. Young; Datta, J.; Bandana Kar; Xiao Huang; Malcolm D. Williamson; Jason A. Tullis; Jackson Cothren. 
2. “In Search of Optimal Objective Priors for Model Selection and Estimation”. Datta, J. and Ghosh (2015), in Current Trends in Bayesian Methodology with Applications, edited by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan, CRC Press.
3. “Some Remarks on Pseudo Panel Data”. Dasgupta, Ghosh, Chakravarty, and Datta, J. (2015), Growth Curve and Structural Equation Modeling, pp. 25-34. Springer International Publishing.

Articles in preparation

1. “Sparse generalized Dirichlet distributions for high-dimensional probabilities”. Datta, Hainer, Ovaskainen and Dunson (201x+). JSM 2020 Talk Slides.
2. “Non-parametric Bayes multi-resolution testing for massive-dimensional rare events”. Datta and Dunson (201x+).
3. “A statistical method for drawing robust inferences in the presence of local dependence in genome-scale data”. Majumder Partha P., and Datta, J.
4. “An Empirical Bayes Approach to Power Calculation and Cross-validation in Multiple Testing”. Datta, J. (201x+).
5. “Proximity Block-models for Network Data”, Sengupta, Datta, Chen (201x+),
6. “Bayesian Square-root Lasso”. Abba, Bhadra, Datta, and Polson (201x), (*alphabetical),
7. “Shrinkage and Selection for Compositional Data”, Datta, Shi and Bandopadhyay, D. (201x+),

Newsletter/essays: 

1. “Does Machine Learning Reduce Racial Disparities in Policing?” Datta, Jyotishka and Drawve, Grant, International Indian Statistical Association Newsletter, December 2017.
2. “Optimal Objective Priors for Linear Models”, Datta, Jyotishka. Indian Bayesian Society Newsletter, May 2014.