about

Ruocheng Guo is a Staff Research Scientist at Intuit AI Research. His research focuses on Trustworthy AI for LLMs, Recommendation Systems, and Graph Learning, leveraging Causal ML and Uncertainty Quantification methodologies. Prior to joining Intuit, he was a Senior Machine Learning Researcher at ByteDance Research and an Assistant Professor at City University of Hong Kong. He earned his Ph.D. from the Data Mining and Machine Learning Laboratory (DMML) at Arizona State University, advised by Huan Liu.

Research Interests:

• LLMs
• Causal ML
• Conformal Prediction
• Trustworthy AI
• Recommendation Systems
• Graph Mining

News

• 11/24 Invited to serve as PC co-chair of the industry track of IEEE DSAA’25
• 11/24 Invited by Dr. Jing Ma to give a guest lecture on conformal causal inference at Case Western Reserve University slides
• 10/24 A paper accepted to NeurIPS’24
• 8/24 Invited to present at 2nd Workshop on Causal Inference and Machine Learning in Practice @ KDD’24

Arxiv Preprints

• Inference-time Stochastic Ranking with Risk Control (rejected by KDD’24 w. novelty/technical quality 5/4)
• Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
• Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models’ Alignment (presented at SoLaR @ Neurips’23)

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

Recent Accepted Papers

• Causal Inference
  • Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards (NeurIPS’24)
  • Conformal Counterfactual Inference under Hidden Confounding (KDD’24)
    • code
  • Debiasing Recommendation by Learning Identifiable Latent Confounders (KDD’23)
    • code
  • Learning for Counterfactual Fairness from Observational Data (KDD’23)
• Fairness
  • Fair Classifiers that Abstain without Harm (ICLR’24)
  • Equal Opportunity of Coverage in Fair Regression (Neurips’23)
    • code
• Graph Learning
  • Tensorized Hypergraph Neural Networks (SDM’24)
  • Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization (ICDM’23)
  • Virtual Node Tuning for Few-shot Node Classification (KDD’23)
• Recommendation Systems
  • EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems (Recsys’24)
  • Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems (KDD’23 ADS Track)

Experience:

• Staff Research Scientist, Intuit AI Research, March 2025 -
• 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
• Research Intern, Microsoft Research, May 2020 - Aug 2020
• Research Intern, Etsy, May 2019 - Aug 2019

Students and Interns (and papers co-authored with me)

• Maolin Wang (PhD Student@City University of Hong Kong, Co-supervised w. Xiangyu Zhao and Junhui Wang)
  • WWW’24, SDM’24, ICDM’23
• Zonghao Chen (PhD Student@UCL, Intern@ByteDance Research)
  • KDD’24
• Tongxin Yin (PhD Student@UMich, Intern@ByteDance Research)
  • ICLR’24
• Qing Zhang (PhD Student@HKUST, Intern@ByteDance Research)
  • KDD’23
• Xiaohui Chen (PhD Student@Tufts, Intern@ByteDance)
  • KDD’23
• Chentao Cao (PhD Student@HKBU, Intern@ByteDance Research)
• Sichun Luo (PhD Student@CityU, Intern@ByteDance)
• Xinjian Zhao (Master Student@CityU -> PhD Student@CUHK SZ)
• Jiansheng Li (Master Student@CityU -> PhD Student@CUHK SZ)