Jyotishka Datta

• 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

[1] Datta, J., and Mukherjee, B. (2021). “Discussion on “Regression Models for Under-
standing COVID-19 Epidemic Dynamics with Incomplete Data"", Invited discussion,
Journal of American Statistical Association.

[2] Li, Y., Datta, J., Craig, B.A., and Bhadra, A. (2021). “Joint mean–covariance estimation
via the horseshoe". Journal of Multivariate Analysis. 183 (2021): 104716.[preprint].

[3] Gu, X., Mukherjee, B., Das, S., Datta, J. (2021). “COVID-19 prediction in South Africa:
Understanding the unascertained cases–the hidden part of the epidemiological iceberg".
Journal of Statistical Research. (Special issue to celebrate 50-year independence of
Bangladesh). preprint.

[4] Neurology: Chaudhuri, J.; Biswas, S.; Gangopadhyay, G.; Biswas, T.; Datta, J.; Biswas,
A.; Datta, A.; Mukherjee, A.; Bhattacharya, P.; Hazra, A. (2021). “Correlation of
ATP7B gene mutations with clinical phenotype and radiological features in Indian Wil-
son Disease patients", Accepted, Acta Neurologica Belgica.

[5] Toxinology: Deshwal, A., Phan, P., Datta, J., Kannan, R., Suresh Kumar, T.K., “A Meta-
Analysis of the Protein Components in the Rattlesnake Venom". Toxins, 13 (6), 372.

[6] Criminology: Steinman, H., Drawve, G., Datta, J., Harris, C. T., and Thomas, S. A.
(2021): “Risky Business: Examining the 80-20 Rule in Relation to a RTM Framework".
(Criminal Justice Review), 46 (1), 20-39.

[7] Bhadra, A., Datta, J., Li, Y., and Polson, N. G.(2020). (*alphabetical1), “Horseshoe
Regularization for Machine Learning in Complex and Deep Models". https://doi.org/
10.1111/insr.12360, International Statistical Review. [preprint].

[8] Bhadra, A., Datta, J., Polson, N. G., & Willard, B. T (2020), (*alphabetical), “Global-
local mixtures - A Unifying Framework". https://doi.org/10.1007/s13171-019-00191-2,
Sankhya A - J. K. Ghosh Memorial Issue. [blog article on the paper]

[9] Criminology: Drawve, G., Harris, C., Thomas, S. A., Datta, J., Cothren, J. (2020): “Cur-
rent and New Frontiers: Exploring how Place Matters through Arkansas NIBRS Re-
porting Practices". (Crime & Delinquency), 67 (6-7), 941-969.

[10] Bhadra, A., Datta, J., Li, Y., and Polson, N. G. (2019), (*alphabetical), “Prediction Risk
for Global-Local Shrinkage Regression". 20 (78), 1-39, Journal of Machine Learning
Research. [full-text].

[11] Bhadra, A., Datta, J., Polson, N. G., & Willard, B. T (2019), (*alphabetical), “Lasso
Meets Horseshoe - A Survey" 34(3), 405-427. Statistical Science. [full-text]

[12] Bhadra, A., Datta, J., Polson, N. G., & Willard, B. T (2019), (*alphabetical), “Horseshoe
Regularization for Feature Subset Selection". https://doi.org/10.1007/s13571-019-00217-7,
Sankhya B. [preprint]

[13] Bhadra, A., Datta, J., Polson, N. G., & Willard, B. T (2017), (*alphabetical) “The Horse-
shoe+ Estimator of Ultra-Sparse Signals", Bayesian Analysis. 12 (4), 1105-1131. [full-
text]

[14] Genomics: Reddy, A., Zhang, J., Davis, N. S., Moffitt, A. B., Love, C. L., Waldrop, A.,
. . . , Datta, J, ... & Dave, S. S. (2017). Genetic and functional drivers of diffuse large
B cell lymphoma. Cell, 171(2), 481-494. Featured on EurekAlert!, the newsletter from
AAAS, link.

[15] Genomics: Moffitt, A. B., Ondrejka, S. L., McKinney, M., Rempel, R. E., Goodlad, J.
R., Teh, C. H., ... Datta, J., . . . & Dave, S. S. (2017). “Enteropathy-associated T
cell lymphoma subtypes are characterized by loss of function of SETD2", Journal of
Experimental Medicine, 214(5), 1371-86.

[16] Genomics: McKinney, M., Moffitt, A. B., Gaulard, P., Travert, M., De Leval, L., Nicolae,
A., ... , Datta, J, . . . , & Davé, S. S. (2017) “The Genetic Basis of Hepatosplenic T Cell
Lymphoma". Cancer Discovery, CD-16-0330.

[17] Datta, J. and Dunson, D. B. (2016), “Bayesian inference on quasi-sparse count data",
Biometrika, 103 (4): 971-983. [full-text]

[18] Genomics: Healy, J. A., Nugent, A., Rempel, R. E., Moffitt, A. B., Davis, N. S., Jiang, X.,
..., Datta, J., ... & Dave, S. S. (2016). “GNA13 loss in germinal center B cells leads to
impaired apoptosis and GCB cell persistence and promotes lymphoma in vivo". Blood,
127(22), 2723-2731.

[19] Bhadra, A., Datta, J., Polson, N. G., & Willard, B. T (2016), (*alphabetical) “Default
Bayesian analysis with global-local shrinkage priors", Biometrika, 103 (4): 955-969.
[full-text]

[20] Pediatrics: Chaudhuri, Biswas, Datta, . . ., Chakarabrty (2016). “Evaluation of malnutri-
tion as a predictor of adverse outcomes in febrile neutropenia associated with pediatric
hematological malignancies." Journal of Paediatrics and Child Health, 52 (7), 704-
709.

[21] Geology: Libohova, Z., Winzeler, H. E., Lee, B., Schoeneberger, P. J., Datta, J., and
Owens, P. R. (2016). “Geomorphons: Landform and property predictions in a glacial
moraine in Indiana landscapes". Catena, 142, 66-76.

[22] Neuroscience: Parthasarathy, Datta, Torres, Hopkins, and Bartlett (2014). “Age-Related
Changes in the Relationship Between Auditory Brainstem Responses and Envelope-
Following Responses." Journal of the Association for Research in Otolaryngology.
15 (4), 649-661.

[23] Datta, J., and Ghosh, J. K. (2014), “Bootstrap – An Exploration." Statistical Methodol-
ogy: 20, 63-72.

[24] Datta, J., and Ghosh, J. K. (2013), “Asymptotic Properties of Bayes Risk for the Horse-
shoe Prior". Bayesian Analysis 8(1), 111-132. [full-text].

REFEREED BOOK CHAPTERS

[25] Young, Datta, Kar, Huang, Williamson, Tullis, and Cothren (2020+), “Challenges and
limitations of geospatial data and analyses in the context of COVID-19". forthcoming,
“Human Dynamics in Smart Cities", Springer.

[26] Datta and Ghosh (2015), “In Search of Optimal Objective Priors for Model Selection
and Estimation". In S. Upadhyay, U. Singh, D. Dey, & A. Loganathan (Eds.), Current
Trends in Bayesian Methodology with Applications, 225-239. Chapman & Hall/CRC
Press.

[27] Dasgupta, Ghosh, Chakravarty, and Datta (2015), “Some Remarks on Pseudo Panel Data".
Growth Curve and Structural Equation Modeling, 25-34. Springer International.

ARTICLES UNDER REVIEW

[28] Boss, J., Datta, J., Wang, X., Park, S., Kang, J., Mukherjee, B. (2021+), “Group Inverse-
Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors". pre-print.

[29] Sagar K. N., Banerjee, S., Datta, J., and Bhadra A. (2021+), “Precision Matrix Estimation
under Horseshoe-like Penalty". pre-print.

[30] Guha and Datta (2021+), “Consistent Model Selection and Change Point Recovery for
High-dimensional Changing Linear Regression". pre-print.

[31] Bhaduri, R., Kundu, R., Purkayastha, S., Kleinsasser, M., Beesley, L., Datta, J., and
Mukerjee, B. (2021), “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"

[32] Harris, C.; Drawve, G.; Thomas, S.; Datta, J.; Steinman (2021+): “Lines of Black and
White: Racial Segregation, Neighborhood Permeability, and Crime" (Submitted to So-
cial Science Research).

[33] Mandana Rezaeiahari; Clare C. Brown; Mir M Ali; Jyotishka Datta; John Mick Til-
ford; (2021+) “Understanding Racial Disparities in Severe Maternal Morbidity Using
Bayesian Network Analysis". Submitted to PLoS One.

PEER-REVIEWED CONFERENCE PROCEEDINGS

[34] Chakraborty, Verma, Sahoo, and Datta, J (2020), “FairMixRep: Self-supervised Robust
Representation Learning for Heterogeneous Data with Fairness constraints", IEEE In-
ternational Conference on Data Mining Workshop (ICDMW). 2020. preprint.

[35] LeBow V., Bernhardt-Barry, M. L., and Datta, J. (2018), “Improving Spatial Visualization
Abilities Using 3D Printed Blocks". 2018 ASEE Annual Conference & Exposition , Salt
Lake City, Utah. full-text.

OTHER PUBLICATIONS

[36] Datta and Drawve, “Does Machine Learning Reduce Racial Disparities in Policing?", IISA
Newsletter, December, 2016.

[37] Datta and Ghosh, “Optimal Objective Priors for Linear Models", Indian Bayesian Society
Newsletter, Vol XI, No. 1, May, 2014.