About Me

  • I am a PhD student at Mila advised by Prof. Sarath Chandar.
  • My research broadly focuses on understanding the principles behind how and why different deep learning models work, with a current focus on sequence models. On the side, I also dabble in more applied natural language processing research.
  • My research is (or has been) generously supported through funds from NSERC, FRQNT, Hydro-Québec and 日本学術振興会 among other sources. I thank them for their broad support and allowing me to conduct research on topics of personal fulfillment and interest.
  • I'm on the hunt for research internships or visiting opportunities from Fall 2025 (i.e. beginning August/September 2025) onwards. If you are in search of one, please reach out by e-mail if there is overlap in research interest or if you simply feel I would be a good fit/addition. For an understanding of my background and skills, refer to my papers (below) and my GitHub (which may be slightly out-of-date).
  • In the meantime, here is where I will likely be and planned conference travel for the rest of 2025:
    • June to August: Tokyo
    • September to November: Montreal -> COLM
    • December: TBD
    If you happen to be in close range, do not hesitate to reach out to meet up and chat!

Relevant Works

  • Investigating the Effects of Architectural Inductive Biases on Hallucination [Link]
  • ZETA: Leveraging Z-order Curves for Efficient Top-K Attention [Link]
  • How Well Can a Long Sequence Model Model Long Sequences? [Link]
  • Do Large Language Models Know How Much They Know? [Link]
  • Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models [Link]
  • Predicting the Impact of Model Expansion through the Minima Manifold [Link]
  • Towards Practical Tool Usage for Continually Learning Large Language Models [Link]
  • EpiK-Eval: Evaluation for Language Models as Epistemic Models [Link]

Working With Me

  • I am actively seeking motivated students for collaboration. If you are passionate about research and eager to work on innovative projects in deep learning, consider reaching out. In addition, please fill out this form - I will follow up if there is a strong fit and ample resources. In the event that you satisfy one of the following criteria, please reach out directly (by e-mail) as there may be additional ways to increase the chances of collaboration:
    1. You satisfy the eligibility requirements for a NSERC USRA.
    2. You study at one of Mila's partner unversities and must conduct research to satisfy degree requirements.
    3. You have external funding to support an internship/research stay and are not a resident of Canada.
  • As a general (but neither necessary nor sufficient) overview of what I personally look for in terms of collaborators
    1. Familiarity with standard deep learning libraries: You should able to train and evaluate some standard networks and models on a task related to your topic of interest. Additionally, you should be able to read and explain code written by others.
    2. Effective communication: Being able to compose clear and coherent ideas both in verbal and writtern form is imperative for disseminating research properly. An easy way to demonstrating this is providing a short research proposal or report.
    3. Diligence and persistence: A somewhat more personal preference, I like observing when an individual can incorporate suggestions into their research ideas as well as counter those that are likely unnecessary.
  • Nevertheless, everyone has different experiences and I encourage those from a non-conventional background (specifically non-engineering or math based) to contact me if you are interested but might not meet all of the above criteria.

Contacting Me

  • Please use my e-mail. They are structured as "[first_name].[last_name]@[domain_name]" for both Mila an Université de Montréal. I check it daily and will respond in a timely fashion (between 1 to 2 working days depending on the urgency).

Other Information

  • Outside of research, I read books and cook (particularly seafood). I faithfully abide by the principle of 腹八分目 (hachi hachi bun me - eat until you are eight parts full). I also play badminton, swim, fish and snowboard (in the winter).