Reinforcement learning
Robust Policy Optimization
RPO is integrated into CleanRL and skrl, and used in Physical Intelligence’s released implementation of real-time action chunking.
Method and adoption →
Postdoctoral Researcher · Purdue University
Md Masudur Rahman
Learning and reasoning for intelligent autonomy.
I develop reinforcement learning and foundation-model methods for intelligent systems that learn, reason, and act when conditions change.
I earned my Ph.D. in Computer Science at Purdue in 2024, advised by Yexiang Xue. I now work with Juan P. Wachs in Purdue’s Edwardson School of Industrial Engineering.
Research agenda
Adaptive learning. Multimodal reasoning. Collaborative autonomy.
I aim to connect these capabilities in intelligent systems that act with appropriate human guidance. My future research on bounded surgical tasks brings together my work in robotics and AI; the underlying questions extend to robot learning and intelligent decision systems beyond medicine.
Explore my research program →Selected contributions
All publications →Language & learning
Language-based representations
Natural-language-based state representations connect semantic descriptions with sequential decision making.
Findings of NAACL 2024
Research and paper →Multimodal reasoning
Reasoning from medical evidence
AURA investigates how vision-language models combine visual evidence and domain knowledge for burn assessment.
IAAI 2026 · Oral presentation
Research and paper →Teaching & mentoring
Computing foundations to current AI research.
My experience spans a Lecturer appointment at BRAC University, graduate teaching assistant roles at Purdue and the University of Virginia, and student research mentoring in reinforcement learning, medical AI, and robotics.
Teaching and mentoring record →Selected updates · 2025–2026
News & highlights
Research support. NAIRR Compute Support as Principal Investigator, with an allocation on Argonne’s Cerebras CS-3 system. Details →
New publication. “A Multimodal Artificial Intelligence Reasoning Framework for Burn Diagnosis” published in Military Medicine. Paper ↗
Research service. Recognized as a Gold Reviewer at ICML 2026. Recognition →
Paper acceptance. AMBUSH-AI, our work on automated non-invasive burn diagnosis, accepted at Annals of Surgery. Publication details →
Paper acceptance. AURA accepted for an oral presentation at IAAI 2026. Research and paper →
Best Paper Award. Recognized at MIUA 2025 for work on knowledge-driven burn diagnosis with vision-language models. Purdue feature ↗