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portfolio
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publications
Analysis of hybrid censored competing risks data
Published in Statistics, 2014
Shrijita Bhattacharya, Biswabrata Pradhan, Debasis Kundu (2014) " Analysis of hybrid censored competing risks data " Statistics. 48(5):1138-1154.
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AMON: An open source architecture for online monitoring, statistical analysis, and forensics of multi-gigabit streams
Published in IEEE Journal on Selected Areas in Communications (JSAC), 2016
Michael Kallitsis, Stilian Stoev, Shrijita Bhattacharya, and George Michailidis (2016) " AMON: An open source architecture for online monitoring, statistical analysis, and forensics of multi-gigabit streams " IEEE Journal on Selected Areas in Communications. 34(6):1834-1848.
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Adaptive statistical detection of false data injection attacks in smart grids
Published in 2016 IEEE global conference on signal and information processing (GlobalSIP), 2016
Michael G Kallitsis, Shrijita Bhattacharya, Stilian Stoev, George Michailidis (2018) " Adaptive statistical detection of false data injection attacks in smart grids " 2016 IEEE global conference on signal and information processing (GlobalSIP). 826-830.
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Inference on the endpoint of human lifespan and its inherent statistical difficulty: Discussion on the paper by Holger Rootzén and Dmitrii Zholud
Published in Extremes, 2018
Stilian Stoev, Shrijita Bhattacharya (2018) " Inference on the endpoint of human lifespan and its inherent statistical difficulty: Discussion on the paper by Holger Rootzén and Dmitrii Zholud " Extremes. 21(3):391-404.
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Detection of false data injection attacks in smart grids based on forecasts
Published in 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2018
Michael G Kallitsis, Shrijita Bhattacharya, George Michailidis (2018) " Detection of false data injection attacks in smart grids based on forecasts " 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). 1-7.
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Data-adaptive trimming of the Hill estimator and detection of outliers in the extremes of heavy-tailed data
Published in Electronic Journal of Statistics, 2019
Shrijita Bhattacharya, Michael Kallitsis, Stilian Stoev (2019) " Data-adaptive trimming of the Hill estimator and detection of outliers in the extremes of heavy-tailed data " Electronic Journal of Statistics. 13(1):1872-1925.
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Statistical foundation of Variational Bayes neural networks
Published in Neural Networks, 2021
Shrijita Bhattacharya, Tapabrata Maiti (2023) " Statistical foundation of Variational Bayes neural networks " Neural Networks. 167:309-330.
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A fast and calibrated computer model emulator: an empirical Bayes approach
Published in Statistics and Computing, 2021
Vojtech Kejzlar, Mookyong Son, Shrijita Bhattacharya, Tapabrata Maiti (2021) "A fast and calibrated computer model emulator: an empirical Bayes approach" Statistics and Computing 31(4):49.
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Quantile regression neural networks: a Bayesian approach
Published in Journal of Statistical Theory and Practice, 2021
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti (2021) " Quantile regression neural networks: a Bayesian approach " Journal of Statistical Theory and Practice . 15(3):68.
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Variational Bayes Ensemble Learning Neural Networks With Compressed Feature Space
Published in IEEE Transactions on Neural Networks and Learning Systems, 2022
Zihuan Liu, Shrijita Bhattacharya, Tapabrata Maiti (2022) " Variational Bayes Ensemble Learning Neural Networks With Compressed Feature Space " IEEE Transactions on Neural Networks and Learning Systems . 35(1):1379-1385.
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Black box variational Bayesian model averaging
Published in The American Statistician, 2023
Vojtech Kejzlar, Shrijita Bhattacharya, Mookyong Son, Tapabrata Maiti (2023) " Black box variational Bayesian model averaging " The American Statistician . 77(1):85-96.
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Normalizing Flows Aided Variational Inference: A Useful Alternative to MCMC?
Published in Notices of the American Mathematical Society, 2023
Sumegha Premchandar, Shrijita Bhattacharya, Tapabrata Maiti (2023) " Normalizing Flows Aided Variational Inference: A Useful Alternative to MCMC? " Notices of the American Mathematical Society . 70(07).
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Outlier detection based on extreme value theory and applications
Published in Scandinavian Journal of Statistics, 2023
Shrijita Bhattacharya, Francois Kamper, Jan Beirlant (2023) " Outlier detection based on extreme value theory and applications " Scandinavian Journal of Statistics . 50(3):1466-1502.
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Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details
Published in Neural Networks, 2023
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti (2023) " Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details " Neural Networks . 167:309-330.
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Statistically valid variational Bayes algorithm for Ising model parameter estimation
Published in Journal of Computational and Graphical Statistics, 2024
Minwoo Kim, Shrijita Bhattacharya, Tapabrata Maiti (2024) "Statistically valid variational Bayes algorithm for Ising model parameter estimation" Journal of Computational and Graphical Statistics 33(1):75-84.
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Comprehensive study of variational Bayes classification for dense deep neural networks
Published in Statistics and Computing, 2024
Shrijita Bhattacharya, Zihuan Liu, Tapabrata Maiti (2024) "Comprehensive study of variational Bayes classification for dense deep neural networks" Statistics and Computing 34(1):17.
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Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
Published in IEEE Transactions on Neural Networks and Learning Systems, 2024
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti (2024) "Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks" IEEE Transactions on Neural Networks and Learning Systems 36(6):11176-11188.
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Variational inference aided variable selection for spatially structured high dimensional covariates
Published in Journal of Computational and Graphical Statistics, 2026
Siddhartha Nandy, Minwoo Kim, Shrijita Bhattacharya, Tapabrata Maiti (2026) "Variational inference aided variable selection for spatially structured high dimensional covariates" Journal of Computational and Graphical Statistics 35(1):482-493.
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Neural Autoregressive Flows based Variational Bayes Model Averaging
Published in The American Statistician, 2026
Jiefu Zhou, Shrijita Bhattacharya (2026) "Neural Autoregressive Flows based Variational Bayes Model Averaging " The American Statistician. accepted.
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talks
Extreme Value Analysis Conference (EVA)
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Joint Statistical Meetings (JSM)
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Indian Statistical Institute (ISI)
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Extreme Value Analysis Conference (EVA)
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Joint Statistical Meetings (JSM)
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Joint Statistical Meetings (JSM)
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Joint Statistical Meetings (JSM)
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teaching
STT 422: Statistics II
Undergraduate course, Michigan State University.
Semester: 2019 Spring, 2024 Spring.
Textbook: Introduction to the Practice of Statistics, 6th ed., by Moore, McCabe, and Craig.
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STT 422 is the undergraduate course covering fundamentals of stastical estimation and inference along with their implementation in RStudio. Topics covered include:
- Confidence interval and P-values.
- Z-tests, T-tests and Chi-square tests.
- One-way and two-way analysis of variance (ANOVA).
- Linear regression and logistic regression.
- Rank-tests.
- Bootstrap methods.
STT 441: Prob and Stat I Probability
Undergraduate course, Michigan State University.
Semester: 2019 Spring, 2019 Fall, 2020 Fall, 2021 Fall.
Textbook: Introduction to Probability by David F. Anderson, Timo Seppalainen, Benedek Valko.
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STT 441 is the undergraduate course covering fundamentals of probability theory and statistics. Topics covered include:
- Conditional probability and independence.
- Random variables.
- Central limit theorem.
- Joint distributions.
- Sums of random variables.
- Expectation and variance.
STT 465: Bayesian Statistical Methods
Undergraduate course, Michigan State University.
Semester: 2025 Fall.
Textbook: A First Course in Bayesian Statistical Methods by Peter D. Hoff and A student’s guide to Bayesian statistics by Ben Lambert.
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STT 465 is the undergraduate course covering fundamentals of Bayesian statistical methods along with their implementation in RStudio. Topics covered include:
- Bayes theorem and Conjugate distributions.
- Monte Carlo methods.
- Normal models and Gibbs sampling.
- Bayesian linear regression and model selection.
- Metropolis Hastings algorithm.
- Bayesian logistic regression and model selection.
STT 867: Linear Model Methodology
Graduate course, Michigan State University.
Semester: 2025 Fall.
Textbook: Linear Model Methodology, by Andre I. Khuri.
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STT 867 is the prelim qualifier course on Linear Models for PhD students in statistics. Topics covered include:
- Least squares, Gauss-Markov Theorem and extensions.
- Simultaneous confidence intervals.
- Less-than-full-rank linear models.
- Distributional properties of quadratic forms.
- Model selection and prediction.
- Shrinkage methods.
STT 872: Statistical Inference I
Graduate course, Michigan State University.
Semester: 2020 Spring, 2021 Spring, 2022 Spring, 2024 spring, 2025 spring, 2026 spring.
Textbook: Theory of Point Estimation, 2nd edition, by E.L. Lehmann and Testing Statistical Hypotheses, 3rd edition, by E.L. Lehmann and J. P. Romano.
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STT 872 is the prelim course on Statistical Inference for PhD students in statistics. Topics covered include:
- Unbiasedness and Information Inequality.
- Equivariance in Location and Scale Families
- Bayesian Estimation and Risk Optimality.
- Minimaxity and Admissibility.
- Uniformly most powerful tests and Confidence intervals.
- Uniformly most powerful unbiased tests.
STT 874: Introduction to Bayesian Analysis
Graduate course, Michigan State University.
Semester: 2022 Fall, 2024 Fall.
Textbook: A First Course in Bayesian Statistical Methods, by Peter D. Hoff and Variational inference: A review for statisticians, by David M Blei, Alp Kucukelbir and Jon D. McAuliffe.
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STT 874 is the graduate course on introduction to Bayesian methods and variational inference. Topics covered include:
- Bayesian Estimation and Hypothesis Testing.
- Bayesian model averaging and Gibbs Sampling.
- Markov Chain Monte Carlo Methods.
- Variational inference and the CAVI method.
- Black box variational inference.