I received my PhD degree in Engineering Sciences at University of California San Diego,
working with advisors Sonia Martinez and Nikolay Atanasov.
Prior to this, I received my Master's degree from Purdue University ( 1, 2 ),
and Bachelor's degree from Indian Institute of Technology Guwahati.
Some of my work with undergraduate students and interns at MURO lab is mentioned here.
Research
My research has focused on addressing achieving networked autonomous systems.
In particular, my work aims to address several aspects of provably correct distributed estimation and optimization in networked systems,
such as accuracy, efficiency, approximations and privacy.
News
Dec 2025: Attending the IEEE Conference on Decision and Control (CDC) to present our paper ‘‘Distributed Variational Inference for Online Supervised Learning," published in IEEE Transactions on Control of Network Systems (TCNS).
June 2025: Presented our work ‘‘Privacy-Preserving Convergent Dynamic Average Consensus via State Decomposition" at the IEEE Statistical Signal Processing Workshop (SSP 2025).
May 2025: Our paper ‘‘The Effect of the Prior on Asymptotic Performance of Uncertain Naive Bayesian Networks" was accepted for presentation at the International Conference on Information Fusion (ICIF 2025).
Apr 2024: Joined DEVCOM Army Research Laboratory as a postdoctoral fellow.
Dec 2023: Defended my thesis titled "Scalable and Efficient Bayesian Algorithms for Distributed Estimation and Inference". Read here.
Sept 2023: Presented a poster on "Distributed Variational Inference for Online Supervised Learning" with a new Turtlebot4 team mapping implementation at SoCal Robotics Symposium 2023.
July 2023: Submitted our work on "Distributed Variational Inference for Online Supervised Learning". Read here.
Dec 2022: Presented our work on "Distributed Bayesian Estimation of Continuous Variables Over Time-Varying Directed Networks" at IEEE CDC 2022.
Aug 2022: Our work titled "Distributed Bayesian Estimation of Continuous Variables Over Time-Varying Directed Networks" was accepted to IEEE Control Systems Letters.
Mar 2022: Senate exam with a talk discussing "Scalable and Efficient Bayesian algorithms for Distributed Estimation and Inference"
Teaching
Teaching assistant for Cooperative Controls [MAE 247 syllabus], Spring 2023: Graded homework solutions and held office hours.
Additional teaching assistant MAE 242, Fall 2022: Discussion sessions and coding tutorials for motion planning.
Teaching assistant for Robot Motion Planning [MAE 242 syllabus], Spring 2022: Prepared homework solutions, created programming assignments, autograders and held office hours.
Teaching assistant for Introduction to Mathematical Analysis [MAE 289A syllabus], Fall 2021: Prepared homework solutions, and held office hours.