Runtian Zhai

翟润天  

PhD Student

Fourth-year PhD
Machine Learning
Computer Science Department (CSD)
School of Computer Science (SCS)
Carnegie Mellon University (CMU)

Email: rzhai at cmu dot edu
Office: GHC 5105

Photo

Fourth-year PhD
Machine Learning
Computer Science Department (CSD)
School of Computer Science (SCS)
Carnegie Mellon University (CMU)

Email: rzhai at cmu dot edu
Office: GHC 5105

Bio [CV]
I am a fourth-year PhD student at CMU CSD, co-advised by Zico Kolter and Pradeep Ravikumar. I study machine learning theory and algorithms. My primary interest is making AI systems more white-box and ideally with statistical guarantees, so that they are more controllable, more explainable, more robust, more fair, and safer to use. My current focus is the generalization of representation learning, self/semi-supervised learning, and out-of-distribution (OOD) generalization.
I received my Bachelor's degree in computer science and applied math (double degree) from Peking University, where I was advised by Liwei Wang. I visited UCLA in the summer of 2019 and worked with Cho-Jui Hsieh. In the summer of 2022, I worked at Amazon Alexa AI at Sunnyvale as an applied scientist intern. From Sept 2019 to Jun 2020 I worked as a full-time research intern in Microsoft Research Asia (MSRA) machine learning group at Beijing.
I am enthusiastic about mentoring students, especially students who just started doing research. Several of my past mentees have published their first ML papers at top venues. For junior students: Please feel free to contact me by email if you are interested in working with me, especially if you want to work on building models that are more white-box, explainable, robust or fair, or general statistical learning theory.
Services
Peer review:
  • JMLR
  • ICLR 2023 - 2024
  • NeurIPS 2022 - 2023
  • ICML 2022 - 2024
  • AISTATS 2023 - 2024
  • KDD 2023 - 2024
  • ICCV 2023
  • SDM 2024
  • ICLR workshop: BGPT'24
  • NeurIPS workshops: M3L'23, R0-FoMo'23, ML-Safety'22, TSRML'22
  • ICML workshop: PODS'22
Teaching:
  • CMU 10-701: Introduction to Machine Learning Fall 2022 (Head TA)
News
  • Two papers accepted by ICLR 2024 as spotlight.
  • Two papers accepted by NeurIPS 2023.
  • One new preprint on arXiv. (Link)
  • Two papers at ICLR 2023 workshops.
  • One paper accepted by ICLR 2023.
Links