About
I am currently a Postdoctoral Research Associate in the Energy and Natural Resources Security Group within the Earth and Environmental Sciences (EES) Division at Los Alamos National Laboratory (LANL). My research focuses on integrated gas–power system simulations and the development of machine learning-based surrogate models to characterize, predict, and analyze disturbance propagation across interconnected energy infrastructures.
Previously, I was a Postdoc in the Mathematics and Computer Science Division at Argonne National Laboratory. I received my Ph.D. in Information Systems from the University of Maryland, Baltimore County, where I worked with the Center for Real-time Distributed Sensing and Autonomy and the Mobile, Pervasive, and Sensor Computing Lab advised by Dr. Nirmalya Roy.
My work focuses on scalable AI training architectures for leadership-class HPC systems, scientific imaging, smart power-grid analytics, queue-aware federated learning, and robust communication protocols for distributed autonomous systems.
During my Ph.D., I developed robust and communication-efficient federated learning methods for non-IID data, lossy communication channels, model compression, adaptive quantization, and ROS/ROS2-based heterogeneous robot experimentation. I received my M.S. from UMBC in 2022 and my B.Sc. in Electrical and Electronic Engineering from Chittagong University of Engineering and Technology.
A recurring part of this work is making FL communication robust in contested and lossy environments, including packet-loss scenarios caused by high velocity differences among collaborating robotic agents.
Scientific machine learning
Multi-modal and physics-aware Vision Transformers for ptychographic phase retrieval and real-time diffraction imaging.
Federated learning
Queue-aware asynchronous FL, continual FL, class-aware scaling, and federated training for smart-grid systems.
Robotics and CPS
Robust learning and ROS/ROS2 middleware for heterogeneous UAV/UGV experimentation.