Ari Kobren

prof_pic.jpg

I am a principal research scientist in Oracle’s Machine Learning Research Group, which is based in Burlington, MA. I am interested in studying how computational tools–especially those that are powered by machine learning–can be used safely, and to promote social good. At Oracle, I’ve worked on fairness in AI, machine learning models for software development, and on various natural language processing projects.

Before Oracle, I completed a Ph.D. at UMass Amherst under the supervision of Andrew McCallum. I received a B.S. in Computer Science from Tufts University and worked as a researcher at MIT Lincoln Laboratory building intelligent decision support systems. I spent the summers of 2014 and 2016 interning at Google.

For a complete list of my publications, see Google Scholar.

news

Oct 05, 2026 I’m happy to announce our new work on Hill Sampling, a simple approach to LLM-based discovery that achieves state-of-the-art results on a handful of tasks. As always, I’m very grateful for the opportunity to collaborate with Jake Beck and Philip Ogren.
Apr 12, 2026 I’m excited to be working with two interns this summer: Collin Coil and Hanane Moussa. We’ll be studying topics related to evolutionary search with LLMs.
Dec 04, 2025 We have two full time research positions we’re trying to fill. The first is looking for folks with experience in computer vision, and the second with recommender systems. If you’re interested please apply and send me mail.
Oct 21, 2025 Honored to be named a top reviewer at NeurIPS. If you’re planning on attending the conference and would like to chat, please reach out!
Oct 03, 2025 My team is currently investigating evolutionary search systems similar to AlphaEvolve. If you’re interested in doing an internship with us over the summer (2026), please reach out!

selected publications

  1. ACL
    Upstream Mitigation is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models
    Ryan Steed, Swetasudha Panda, Ari Kobren, and 1 more author
    In Association for Computational Linguistics, 2022
  2. WSDM
    Online Post-Processing in Rankings for Fair Utility Maximization
    Ananya Gupta, Eric Johnson, Aditya Roy Kumar, and 5 more authors
    In Web Search and Data Mining, 2021
  3. KDD
    Paper Matching with Local Fairness Constraints
    Ari Kobren, Barna Saha, and Andrew McCallum
    In International Conference on Knowledge Discovery and Data Mining, 2019
  4. KDD
    Scalable Hierarchical Clustering with Tree Grafting
    Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, and 2 more authors
    In International Conference on Knowledge Discovery and Data Mining, 2019
  5. KDD
    A Hierarchical Algorithm for Extreme Clustering
    Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, and 1 more author
    In International Conference on Knowledge Discovery and Data Mining, 2017