Kevin Ren

Kevin Ren

PhD Student @ Cornell Tech

Bio

Hello! I am a second year PhD Student in the Computer Science Department at Cornell Tech, where I work with Nikhil Garg, and am broadly interested in the intersections between machine learning and society (some recent interests have included test-time adaptation for AI text detection, social choice and alignment in RLHF, and multi-turn interactions for AI agents). I have been fortunate to be funded by an NSF Graduate Research Fellowship and Digital Life Initiative Fellowship.

In 2025, I received a B.S. in Statistics and Machine Learning with a minor in Computer Science from Carnegie Mellon University. My undergraduate research spanned topics such as uncertainty calibration for LLMs, decision-focused learning, and multi-agent pathfinding, where I worked with Bryan Wilder, Maxim Likhachev, and Steven Wu.

News

Paper My paper Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift was accepted to NeurIPS 2026!
Award Awarded and accepted Digital Life Initiative's Doctoral Student Fellowship!
Project Spent some time vibe coding! Check out my paper browser: https://paper-browser.github.io/
Paper New preprint out on arXiv: Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift!
Award Awarded and accepted the NSF GRFP Award!
Milestone Committed to Cornell Tech :)

Publications

You can also find my articles on my Google Scholar profile.

  • Kevin Ren, Manish Raghavan, Nikhil Garg.
    Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift.
    NeurIPS-26. Fortieth Annual Conference on Neural Information Processing. 2026. [Link].

  • Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Zhiwei Steven Wu, Bryan Wilder.
    Predicting Language Models' Success at Zero-Shot Probabilistic Prediction.
    EMNLP Findings-25. Conference on Empirical Methods in Natural Language Processing. 2025. [Link].

  • Rishi Veerapaneni, Arthur Jakobsson, Kevin Ren, Samuel Kim, Jiaoyang Li, Maxim Likhachev.
    Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF.
    ICRA-25. International Conference on Robotics and Automation. 2025. [Link].

  • Kevin Ren, Yewon Byun, Bryan Wilder.
    Decision-Focused Evaluation of Worst-Case Distribution Shift.
    UAI-24. Conference on Uncertainty in Artificial Intelligence. 2024. [Link]

  • Rishi Veerapaneni, Qian Wang, Kevin Ren, Arthur Jakobsson, Jiaoyang Li, Maxim Likhachev.
    Improving Learnt Local MAPF Policies with Heuristic Search.
    ICAPS-24. Thirty-Fourth International Conference on Automated Planning and Scheduling. 2024. [Link]

Preprints

    Teaching

    I've had the opportunity to serve on the course staff of a few ML courses at CMU and Cornell:

    Fall 2023
    CMU Introduction to Machine Learning (10-301/601)
    Spring 2024
    CMU Introduction to Machine Learning (10-301/601)
    Spring 2025
    CMU Introduction to Machine Learning (PhD) (10-701)
    Fall 2025
    Cornell Applied Data Science (ORIE 5355)

    CV

    You can download a PDF version of my CV.