About

Ayush Mohanty is a Ph.D. candidate in Machine Learning at the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, advised by Prof. Nagi Gebraeel. His research advances methodological foundations of decentralized machine learning, with a focus on causal inference in distributed and heterogeneous data environments. He develops federated learning frameworks for causal discovery, uncertainty quantification, and counterfactual reasoning across interdependent clients, with applications in cyber-physical systems, distributed manufacturing, multi-party supply chain networks, and healthcare informatics. His research also spans predictive modeling in multi-component dynamical systems, with applications in prognostics and remaining useful life estimation for aerospace, robotic, and industrial systems. As part of a five-year collaboration with NASA's Habitats Optimized for Missions of Exploration (HOME) Space Technology Research Institute, he led multi-university teams in live demonstrations of decentralized learning systems before NASA engineers and aerospace industry stakeholders. He is on the academic job market starting Fall 2026.