Research

My research is broadly about designing computational algorithms. We live in very interesting times in which our new friends, computers, dramatically expand our capabilities in modeling and learning. While they get faster day by day, I am not satisfied to simply wait for hardware to scale to my problems. Moreover, some problems won’t ever be tameable by constant factor increases. Mathematics is my cudgel in this purpose. Currently, I practice in scientific machine learning and uncertainty quantificaiton, but am happily open to many fields.

Themes

Here’s a quick synopsis of some themes I’ve been exploring:

Improved Algorithms for Machine Learning

I am interested in optimization and approximation algorithms for machine learning models. One recent avenue for this is looking for classical numerical analysis techniques that can clarify or improve modern training procedures. Recent work in this direction includes VPBoost (Chowdhary, Newman, et al. 2026), a gradient boosting method for separable smooth learners that combines variable projection with a trust-region view of second-order weak learning.

Scaling uncertainty quantification

A major part of my work concerns uncertainty quantification for Bayesian inverse problems, especially PDE-constrained problems with high- or infinite-dimensional unknowns. During my PhD, I worked on scalable ways to approximate, differentiate, and optimize information-based quantities such as information gain and expected information gain, with applications to sensitivity analysis and robust optimal experimental design. I published a few papers in this direction; see my work with Georg and coauthors (Chowdhary, Tong, et al. 2024), my work with Ahmed and Alen (Chowdhary, Attia, et al. 2026), and my thesis (Chowdhary 2025).

Mathematical software

I have a personal interest in mathematical research software. It’s key for our field to improve its transparency and reproducibility while also improving the speed of dissemination of information. In this past, I was a major maintainer for PyOED (Chowdhary, Ahmed, et al. 2024), which was a package for rapidly prototyping and benchmarking data assimilation and model-constrained optimal design methods. I’m currently working on similar in scientific ML.

References

Chowdhary, Abhijit. 2025. “Scalable Uncertainty Quantification for Infinite-Dimensional Bayesian Inverse Problems.” {PhD} thesis.
Chowdhary, Abhijit, Shady E. Ahmed, and Ahmed Attia. 2024. “PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments.” ACM Transactions on Mathematical Software 50 (2): 1–22. https://doi.org/10.1145/3653071.
Chowdhary, Abhijit, Ahmed Attia, and Alen Alexanderian. 2026. “Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems.” SIAM Journal on Scientific Computing 48 (2): B262–88. https://doi.org/10.1137/24M1693921.
Chowdhary, Abhijit, Elizabeth Newman, and Deepanshu Verma. 2026. Boost Like a (Var)pro: Trust-Region Gradient Boosting via Variable Projection. https://arxiv.org/abs/2603.23658.
Chowdhary, Abhijit, Shanyin Tong, Georg Stadler, and Alen Alexanderian. 2024. “Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems.” International Journal for Uncertainty Quantification, ahead of print. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2024051416.