Ari Kobren
I am a principal research scientist in Oracle Labs East, which is based in Burlington, MA. I’m currently focused on building machine learning models that help developers find and fix software issues more efficiently. More broadly, 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.
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 27, 2024 | My group is hiring! We’re looking for an ambitious ML research scientist to join our team. If you’re interested, please contact me! |
---|---|
Oct 01, 2024 | I’m looking for an intern to work with me during summer 2025 on large language models to support software development and debugging. |
Sep 27, 2024 | One paper submitted to ICLR with our intern Aditya Parashar; I will also be reviewing! |
Aug 18, 2024 | New paper accepted to EMNLP, by our intern Sagi Shaier. |
Jul 01, 2024 | I recorded a set of short video lectures on Fundamentals of Large Language Models that is part of the OCI Generative AI Certification. |
selected publications
- ACLUpstream Mitigation is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language ModelsIn Association for Computational Linguistics, 2022
- WSDMOnline Post-Processing in Rankings for Fair Utility MaximizationIn Web Search and Data Mining, 2021
- KDDPaper Matching with Local Fairness ConstraintsIn International Conference on Knowledge Discovery and Data Mining, 2019
- KDDScalable Hierarchical Clustering with Tree GraftingIn International Conference on Knowledge Discovery and Data Mining, 2019
- KDDA Hierarchical Algorithm for Extreme ClusteringIn International Conference on Knowledge Discovery and Data Mining, 2017