EMON DEY

Emon Dey is a Postdoctoral Researcher in the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory. His research focuses on the design and optimization of efficient foundational model architectures for complex 3D vision and smart grid applications. He is affiliated with the APPFL (Advanced Privacy Preserving Federated Learning) team, where he investigates scalable federated learning techniques and their deployment on high-performance computing (HPC) systems.

Prior to joining Argonne, Emon Dey earned his Ph.D. and M.S. in Computer Information Systems from the University of Maryland, Baltimore County (UMBC). His doctoral research involved developing an edge-deployable federated training framework tailored for multi-robot systems, addressing challenges related to communication bottlenecks and resource-constrained environments. He also holds a Bachelor’s degree in Electrical and Electronic Engineering.

Research Interests:

• Parallel Programming
• Federated Learning at Scale
• Foundational Models
• Edge Computing and Embedded AI Systems