Aaron Chan

Research Scientist at Meta AI   •   aarzchan :slightly_smiling_face: gmail :upside_down_face: com


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Hi! :wave: I’m a research scientist on the AI for Modern Recommendation Systems (MRS) Team at Meta AI.

My research is at the intersection of natural language processing and machine learning. In particular, I’m excited about:

  • model explainability: explaining language model behavior more faithfully, plausibly, and efficiently.
  • explanation-based learning: operationalizing explanations to improve language model generalization and decision-making.

Previously, I was a research intern at Meta AI and an engineering intern at Google.

I recently earned my PhD in computer science from the University of Southern California. During my PhD, I was advised by Prof. Xiang Ren at the INK Lab. Before that, I earned my MSE in robotics from the University of Pennsylvania and my BS in electrical engineering from the University of Maryland.

Currently, I’m based in the Washington, DC metro area, where I live with my wife. Outside of work, I enjoy playing/watching basketball, skiing, hiking, reading, board gaming, and foodie-ing. Also, I’m a huge fan of the TV shows Seinfeld and BoJack Horseman.


News

Jan 20, 2023 PINTO was accepted to ICLR 2023! :confetti_ball:
Dec 20, 2022 Our HUFTR preprint is now on OpenReview! :page_facing_up:
Dec 19, 2022 Our KNIFE preprint is now on arXiv! :page_facing_up:
Dec 09, 2022 Peifeng Wang is presenting PINTO today at the TSRML Workshop at NeurIPS 2022. Come check out our poster!
Dec 08, 2022 Brihi Joshi is presenting ER-Test today at the BlackboxNLP Workshop at EMNLP 2022. Come check out our poster!

Selected Publications

  1. ICLR
    PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
    P. Wang, A. Chan, F. Ilievski, M. Chen, and X. Ren
    ICLR, 2023
    arXiv PDF Code
  2. EMNLP
    ER-Test: Evaluating Explanation Regularization Methods for NLP Models
    B. Joshi*, A. Chan*, Z. Liu*, S. Nie, M. Sanjabi, H. Firooz, and X. Ren
    Findings of EMNLP, 2022
    arXiv PDF Code
  3. ICML
    UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
    A. Chan, M. Sanjabi, L. Mathias, L. Tan, S. Nie, X. Peng, X. Ren, and H. Firooz
    ICML, 2022
    arXiv PDF
  4. NeurIPS
    SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
    A. Chan, J. Xu, B. Long, S. Sanyal, T. Gupta, and X. Ren
    NeurIPS, 2021
    arXiv PDF Code
  5. ICLR
    Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
    M. Raman, A. Chan*, S. Agarwal*, P. Wang, H. Wang, S. Kim, R. Rossi, H. Zhao, N. Lipka, and X. Ren
    ICLR, 2021
    arXiv PDF Code