Ruocheng Guo

Ruocheng is a Senior Applied Scientist at Microsoft Frontier Tuning. His current research includes Environment Scaling for Agentic RL, LLMs as (causal) World Models, and more generally Automating Decision Making in Complex Environments through ML and Causal Inference.

Prior to joining Microsoft, he was a Staff Research Scientist at Intuit, working on Tool-using Agents and Financial World Models. Before that, he was a ML Researcher at ByteDance Research/Tiktok (London) and an Assistant Professor at City University of Hong Kong. He earned his Ph.D. degree from the Data Mining and Machine Learning Laboratory (DMML) at Arizona State University, advised by Huan Liu, MSc degree from the beautiful HKUST and BEng degree from HUST.

Research Interests:

• LLMs (Env Scaling, Agentic RL, Text World Models, LLM Reasoning)
• Causal ML
• Data Mining

Interested in Causal ML? Please find our papers and algorithm/data repositories.

• Survey Papers
  • Causal Interpretability for Machine Learning–Problems, Methods and Evaluation
  • A Survey of Learning Causality with Data: Problems and Methods
• Repositories
  • Algorithm Repository: Awesome-Causality-Algorithms
  • Data Repository: Awesome-Causality-Data

Experience:

• Senior Applied Scientist, Microsoft Frontier Tuning, Aug 2026 - Now
• Staff Research Scientist, Intuit AI Research, March 2025 - Aug 2026
• Senior Machine Learning Researcher, ByteDance Research, June 2022 - Jan 2025
• Assistant Professor, City University of Hong Kong, Aug 2021 - June 2022
• AI Resident, Google X, Aug 2020 - Dec 2020
  • Mentor: Hongxu Ma
• Research Intern, Microsoft Research, May 2020 - Aug 2020
  • Mentor: Emre Kiciman, Pengchuan Zhang
• Research Intern, Etsy, May 2019 - Aug 2019
  • Mentor: Liangjie Hong, Xiaoting Zhao, Adam Henderson