Dr. Sahil Tyagi is a Postdoctoral Research Associate in the Analytics and AI Methods at Scale group (AAIMS) at ORNL, where he works at the intersection of ML and systems research. Specifically, he is exploring different computation and communication models to scale deep learning workloads across edge, cloud and high-performance computing (HPC) clusters.
He holds a Ph.D. in Intelligent Systems Engineering from Indiana University Bloomington, where he worked on distributed model training, federated learning, gradient compression, stream processing, and more.
Links
Publications
Aug, 2026
Conference Paper
Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training
Mar, 2026
Conference Paper
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC