Aaron Chan

PhD Candidate at USC   •   aarzchan :slightly_smiling_face: gmail :upside_down_face: com


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Hi! :wave: I’m a computer science PhD candidate in the INK Lab at the University of Southern California (USC), advised by Prof. Xiang Ren. I’m also a member of USC’s Information Sciences Institute (ISI).

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.

In the past, I’ve worked as a research intern at Meta AI and an engineering intern at Google.

Before coming to USC, I earned a master’s degree in robotics from the University of Pennsylvania and a bachelor’s degree in electrical engineering from the University of Maryland, College Park.

Currently, I’m based in the Washington, DC metro area, where I live with my wife. Outside of work, you might find me playing/watching basketball (go Wizards!), skiing, hiking, board gaming, or foodie-ing (:sushi::curry::crab::pizza:). Plus, I’m a huge fan of the TV shows Seinfeld and BoJack Horseman.


News

Jul 18, 2022 I’m in Baltimore this week for ICML 2022! :airplane: I’ll be presenting UNIREX on July 19.
Jul 10, 2022 I’m in Seattle this week for NAACL 2022! :airplane: I’ll be presenting ER-Test with Brihi Joshi at the TrustNLP Workshop on July 14.
May 27, 2022 I’m presenting UNIREX today at the BigScience Workshop at ACL 2022. Come chat with me at the poster session!
May 15, 2022 UNIREX was accepted to ICML 2022 as a spotlight presentation! :confetti_ball:
May 10, 2022 After completing my PhD in Fall 2022, I’ll be joining Meta AI as a research scientist! :man_technologist:

Selected Publications

  1. arXiv
    FRAME: Evaluating Simulatability Metrics for Free-Text Rationales
    A. Chan, S. Nie, L. Tan, X. Peng, H. Firooz, M. Sanjabi, and X. Ren
    arXiv Preprint, 2022
    arXiv PDF
  2. TrustNLP
    ER-Test: Evaluating Explanation Regularization Methods for NLP Models
    B. Joshi*, A. Chan*, Z. Liu*, S. Nie, M. Sanjabi, H. Firooz, and X. Ren
    TrustNLP Workshop at NAACL, 2022
    arXiv PDF
  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