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

Dec 05, 2022 I joined Meta AI as a research scientist! :man_technologist:
Nov 15, 2022 I passed my PhD defense! :mortar_board: Check out my slides here.
Oct 24, 2022 PINTO was accepted to the TSRML Workshop at NeurIPS 2022! :tada:
Oct 19, 2022 PINTO was accepted to the TL4NLP Workshop at NeurIPS 2022! :tada:
Oct 12, 2022 FRAME was accepted to the BlackboxNLP Workshop at EMNLP 2022! :tada:

Selected Publications

  1. NeurIPS
    PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
    P. Wang, A. Chan, F. Ilievski, M. Chen, and X. Ren
    {TL4NLP, TSRML} Workshop at NeurIPS, 2022
  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
  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
  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
  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