Hi, I am a Master’s student specializing in Computer Science at Georgia Institute of Technology. I was a research intern in Prof. Tong Zhang’s group, working with Rui Yang. I have also spent time at Microsoft Research, advised by Dr. Minghua Ma and managed by Dr. Ze Li. Before that, I was a research intern at Microsoft Research Asia, advised by Dr. Chaoyun Zhang and Dr. Lu Wang. Previously, I earned my bachelor’s degrees from Tianjin University. I am fortunate to collaborate with researchers of 
Georgia Tech, 
UIUC, 
Microsoft Research, Microsoft Research Asia, 
NEC Laboratories America, and 
Tianjin University. [Resume] [Google Scholar]
My research centers on Trustworthy AI, with a focus on integrating human-in-the-loop (HITL) to navigate and adapt to dynamic environments:
- Reliability: integrating external knowledge and tools to enhancing the reasoning and planning capabilities of foundation models;
- Robustness: advancing RL/RLHF for improved generalization and trustworthiness under distribution shift;
- Applications of RL/RLHF and LLMs in specific domains including AIOps, AI4SE, .etc.
I am actively seeking PhD positions for Fall 2025. If you have or know of any opportunities that align with my interests, I would be grateful if you could contact me at rmding@gatech.edu. I am very interested in discussing any potential opportunities. Thank you!
🔥 News
- Dec, 2024: 🎉🎉 One paper is accepted by SDM 2025.
- Sep, 2024: 🎉🎉 Our paper, “Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs”, has been accepted by NeurIPS 2024. [paper] [code]
- May, 2024: 🎉🎉 Our paper, “Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation”, has been accepted by ACL 2024 as a long paper finding. [paper] [code]
- Apr, 2024: 🎉🎉 I am thrilled to announce that I will be joining Microsoft Redmond as a Research Intern this summer. I am excited for the journey ahead and can’t wait to be in Seattle!
📝 Publications
Everything of thoughts: Defying the law of penrose triangle for thought generation
Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
- Findings of the Association for Computational Linguistics (ACL), 2024.
- Incorporate external domain knowledge into thoughts using RL and Monte Carlo Tree Search (MCTS), enhancing LLMs’ capabilities and enabling them to efficiently generalize to unseen problems.
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs
Rui Yang, Ruomeng Ding, Yong Lin, Huan Zhang, Tong Zhang
- Neural Information Processing Systems (NeurIPS), 2024.
- Enhance the reward model’s generalization ability against distribution shifts by regularizing the hidden states.
Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Xiaomin Wu, Meng Zhang, Qingjun Chen, Xin Gao, Xuedong Gao, Hao Fan, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
- Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2023.
- A end-to-end RCA framework for large-scale microservice systems. It utilizes reinforcement learning to automatically eliminate redundant components, improving RCA efficiency.
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SDM 2025 Evidence-Based Out-of-Distribution Detection on Multi-Label Graphs, Ruomeng Ding, Xujiang Zhao, Chen Zhao, Minglai Shao, Zhengzhang Chen, Haifeng Chen
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KDD 2023 Root cause analysis for microservice systems via hierarchical reinforcement learning from human feedback, Lu Wang, Chaoyun Zhang, Ruomeng Ding, Yong Xu, Qihang Chen, Wentao Zou, Qingjun Chen, Meng Zhang, Xuedong Gao, Hao Fan, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang [paper]
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VLDB 2023 Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection, Yuhang Chen, Chaoyun Zhang, Minghua Ma, Yudong Liu, Ruomeng Ding, Bowen Li, Shilin He, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang [paper] [code]
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UDM-AAAI 2023 Detecting Multi-Label Out-of-Distribution Nodes on Graphs, Ruomeng Ding, Xujiang Zhao, Chen Zhao, Minglai Shao [paper]
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TCYB 2023 Exploring temporal community structure via network embedding, Tianpeng Li, Wenjun Wang, Pengfei Jiao, Yinghui Wang, Ruomeng Ding, Huaming Wu, Lin Pan, Di Jin [paper]
📖 Educations
- 2022.08 - 2025.05 (Estimated), M.S. in Computer Science, Georgia Institute of Technology
- GPA: 4.0/4.0, Specialization: Machine Learning.
- Both in Atlanta and Shenzhen Campus, pursuing a dual master’s degree at Tianjin University. Expected to graduate with separate M.S. degrees from both institutions in May 2025.
- 2018.08 - 2022.05, Bachelor in Computer Science and Technology, Tianjin University
- GPA: 3.75/4.0, Rank: 11/169 (6.5%).
💻 Internships
- 2024.05 - 2024.08, Microsoft Research, Redmond, WA.
- Advised by Dr. Minghua Ma and Dr. Ze Li.
- 2022.11 - 2023.08, Microsoft Research Asia, Beijing, China.
- Advised by Dr. Lu Wang and Dr. Chaoyun Zhang.
🎖 Honors and Fellowships
- 2023, National Scholarship, TOP 1%, Tianjin University.
- 2022, Merit Scholarship, TOP 5%, Georgia Institute of Technology (Shenzhen Campus)
- 2020, People’s Scholarship, TOP 1%, Tianjin University
- 2019, Merit Scholarship, TOP 5%, Tianjin University
🛠️ Services
- 2024, Reviewer, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
- 2023, Reviewer, KDD Workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM-KDD), 2023
- 2023, Reviewer, Journal of Information Processing and Management
🔦 Miscellaneous
- Practiced synchronized swimming during teens—our team earned 2nd place at the National Youth Competition. Now I swim just for fun. 🏊♀️
- Played the flute in orchestras throughout school and performed in benefit concerts. 🎶
- Took up piano in 2022—progress is slow, but it’s all about the journey. 🎹
- A big fan of novels and musicals, especially Les Misérables. 🎭