🎯 I'm on the 2026 Job Market — actively seeking research positions in both industry and academia. 🤝 I'm also always excited to explore new research collaborations and exchange ideas with fellow researchers. 📩 Please feel free to reach out via ruhwang@iu.edu or LinkedIn — I'd love to chat!
About
I am a Ph.D. candidate in Computer Engineering at Indiana University, advised by Prof. Dongruo Zhou. My research focuses on building self-improving large language models and autonomous AI agents that can continuously learn from interaction, feedback, memory, and experience. To support this goal, I study reinforcement learning for post-training, reasoning, and agentic behaviors in large-scale language models, with recent interests in Agent Reinforcement Learning, federated reasoning, and memory mechanisms for iterative self-improvement.
My recent work spans agentic LLM systems, uncertainty-aware reasoning, federated in-context learning, RL-enhanced retrieval-augmented generation, and offline reinforcement learning. I have also worked on multimodal agentic recommender systems and adaptive reasoning frameworks for large language models.
I am currently a Research Intern at Tencent AI Lab (Hunyuan Frontier Lab), where I work on Agentic Reinforcement Learning for large language model agents. Previously, I worked as a Ph.D. Research Intern at Mitsubishi Electric Research Laboratories, where I explored quantum machine learning and generative models.
Selected Publications
View All →Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
Runze Zhao, Yue Yu, Ruhan Wang, Chunfeng Huang, Dongruo Zhou
Forty-Third International Conference on Machine Learning (ICML)
Instance-dependent analysis of continuous-time reinforcement learning using maximum likelihood estimation.
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C.S. Lui, Dongruo Zhou
Forty-Second International Conference on Machine Learning (ICML)
Privacy-preserving framework (Fed-ICL) that combines federated learning with in-context learning to collaboratively train diverse LLM agents, with theoretical equivalence to established FL algorithms.
Safe Decision Transformer with Learning-based Constraints
Ruhan Wang, Dongruo Zhou
7th Annual Learning for Dynamics and Control Conference (L4DC)
Constrained Q-learning Decision Transformer (CQDT) for safe offline RL, addressing stitching limitations of CDT while strictly adhering to safety constraints.
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
Transactions on Machine Learning Research (TMLR)
Return Augmented Decision Transformer (RADT) for offline off-dynamics RL with rigorous suboptimality analysis and D4RL evaluation across off-dynamics shifts.
LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models
Ahmad Faiz, Sotaro Kaneda, Ruhan Wang, Rita Osi, Parteek Sharma, Fan Chen, Lei Jiang
The Twelfth International Conference on Learning Representations (ICLR)
End-to-end carbon footprint projection model for LLMs across training, inference, experimentation, and storage phases, integrating LLM, hardware, and data center parameters.
News
🏅 Recognized as a Gold Reviewer for ICML 2026, placing among the top reviewers for this year's conference (with complimentary registration).
🚀 Started my Ph.D. Research Internship at Tencent AI Lab (Hunyuan Frontier Lab) in Bellevue, WA, hosted by Dr. Kishan Panaganti. Working on Agentic Reinforcement Learning for large language model agents.
🎉 Our paper Instance-Dependent Continuous-Time RL via Maximum Likelihood Estimation — joint work with Runze Zhao, Yue Yu, Chunfeng Huang, and Prof. Dongruo Zhou — has been accepted to ICML 2026 (Seoul)!
📝 Submitted FERA: Uncertainty-Aware Federated Reasoning with Large Language Models to COLM 2026 — a parameter-free federated reasoning framework that uses uncertainty quantification for cross-client knowledge integration. With Chengkai Huang, Zhiyong Wang, Rui Wang, Tong Yu, Lina Yao, and Prof. Dongruo Zhou.
🏅 Recognized as a Top 25% Reviewer for ICLR 2026.
🛫 Attended ICML 2025 in Vancouver to present Federated In-Context Learning: Iterative Refinement for Improved Answer Quality — a privacy-preserving Fed-ICL framework with theoretical equivalence to established FL algorithms. With Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C.S. Lui, and Prof. Dongruo Zhou.
🛫 Attended L4DC 2025 in Ann Arbor, MI to present Safe Decision Transformer with Learning-based Constraints — our Constrained Q-learning Decision Transformer (CQDT) for safe offline RL. Joint work with Prof. Dongruo Zhou; previously presented at the NeurIPS 2024 Safe Generative AI Workshop.
🎓 Advanced to Ph.D. Candidacy in Computer Engineering at Indiana University after passing the qualifying examination.
📚 Our work Towards Agentic Recommender Systems in the Era of Multimodal LLMs has been accepted to ACM TIST — a formal LLM-ARS framework spanning user profiling, memory, planning, and action selection, identifying seven open challenges. Collaboration led by Chengkai Huang with the team at UNSW, Adobe Research, UCSD, and Indiana University.
🎓 Completed my M.S. in Computer Engineering at Indiana University Bloomington.
🔬 Wrapped up my Ph.D. Research Internship at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA, hosted by Dr. Toshiaki Koike-Akino. Worked on quantum machine learning and generative models — resulted in Quantum Diffusion Models for Few-Shot Learning, accepted to ICAD 2025 and the AAAI 2024 Quantum Computing & AI Workshop.
⭐ LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models selected for Oral Presentation at ICLR 2024 (Vienna) — projecting LLM carbon footprints across training, inference, experimentation, and storage. Joint work with Ahmad Faiz, Sotaro Kaneda, Rita Osi, Parteek Sharma, Fan Chen, and Lei Jiang.
🤝 Joined the Machine Learning Lab at Indiana University, advised by Prof. Dongruo Zhou — shifting research focus toward reinforcement learning, foundation models, and agentic AI.
🌟 Started my Ph.D. journey in Computer Engineering at Indiana University Bloomington, joining the Quantum Computing Lab under Prof. Fan Chen.