Ambedkar Dukkipati

Abstract Heterogeneous catalytic pathways for clean energy conversion involve thousands of elementary steps, but most models involve only a few dozen reactions. We combine extensive density functional theory calculations, machine learning for activation barrier prediction, and human intelligence-inspired elementary reaction enumeration and identification. This enables automated kinetic modeling of CO2 hydrogenation on copper, a key process to produce fuels and chemicals.

[Submitted on 1 Feb 2026] Title:Sample Efficient Active Algorithms for Offline Reinforcement Learning View a PDF of the paper titled Sample Efficient Active Algorithms for Offline Reinforcement Learning, by Soumyadeep Roy and 2 other authors View PDF HTML (experimental) Abstract:Offline reinforcement learning (RL) enables policy learning from static data but often suffers from poor coverage of the state-action space and distributional shift problems.

[Submitted on 11 Jul 2025] Title:Emergent Natural Language with Communication Games for Improving Image Captioning Capabilities without Additional Data View a PDF of the paper titled Emergent Natural Language with Communication Games for Improving Image Captioning Capabilities without Additional Data, by Parag Dutta and Ambedkar Dukkipati View PDF HTML (experimental) Abstract:Image captioning is an important problem in developing various AI systems, and these tasks require large volumes of...