My recent research concerns probabilistic machine learning and uncertainty quantification methodology for graphical models with intractable normalizing constants, highly multivariate Gaussian processes, and generative modeling of manifold-valued data. I work on biological and environmental applications.
I currently serve as an Associate Editor for the Annals of Applied Statistics, the Journal of Computational and Graphical Statistics, Statistical Analysis and Data Mining, and Sankhya A. Previously I served as an Associate Editor for the Journal of the American Statistical Association (Theory & Methods).
@article{bhadra2026reverse,title={{The Reverse Telescoping Coordinate System for Positive Definite Matrices: Geometry, Computation, and Generative Modeling}},author={Bhadra, A.},year={2026},journal={arXiv:2606.15442},period={Preprints},category={Methodology},}
JMLR
Likelihood-based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants
Y. Chen†, A. Bhadra, and A. Chakraborty
Journal of Machine Learning Research (to appear), 2026
@article{chen2026likelihoodbased,title={{Likelihood-based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants}},author={Chen, Y. and Bhadra, A. and Chakraborty, A.},journal={Journal of Machine Learning Research (to appear)},year={2026},category={Methodology},period={2025–present},}
ICLR
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
J. Loría† and A. Bhadra
In The 13th International Conference on Learning Representations (ICLR 2025), 2025
@inproceedings{lora2025deep,title={{Deep Kernel Posterior Learning under Infinite Variance Prior Weights}},author={Loría, J. and Bhadra, A.},booktitle={The 13th International Conference on Learning Representations (ICLR 2025)},year={2025},period={2025–present},category={Methodology},}
JMLR
Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix
A. Bhadra, K. Sagar†, D. Rowe, S. Banerjee, and J. Datta
@article{bhadra2024evidence,title={{Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix}},author={Bhadra, A. and Sagar, K. and Rowe, D. and Banerjee, S. and Datta, J.},journal={Journal of Machine Learning Research},volume={25},number={295},pages={1--43},year={2024},period={2020–2024},category={Methodology},}
JASA
Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions
P. Ma and A. Bhadra
Journal of the American Statistical Association, 2023
@article{ma2023beyond,title={{Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions}},author={Ma, P. and Bhadra, A.},journal={Journal of the American Statistical Association},volume={118},pages={2045--2058},year={2023},doi={10.1080/01621459.2022.2027775},period={2020–2024},category={Methodology},}
