I like rice pudding

About Me

I am a PhD student at Mila advised by Prof. Sarath Chandar. My research focuses on understanding the principles behind how deep learning models work and uncovering their underlying training dynamics. My current interests lie in knowledge dependence modeling and memory augmented neural networks, in particular the use of structured memory and how to leverage it for adapting to distributional shifts or enabling more robust information flow. I am furthermore investigating how these can lead to better knowledge transfer and enabling more reliable retrieval of information within models.
My research is (or has been) generously supported through funds from NSERC, FRQNT and Hydro-Québec among other sources.

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]
Promoting Exploration in Memory-Augmented Adam using Critical Momenta [Link]

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:

If you are a Canadian citizen, a permanent resident of Canada or a Protected Person under subsection 95(2) of the Immigration and Refugee Protection Act (Canada) registered in a bachelor's degree program at a Canadian University under the description provided by the Tri-Council (SSHRC, CIHR, NSERC).
You are a student at one of Mila's partner unversities and must conduct research to satisfy degree or course requirements.
You have external funding to support an internship/research stay and are not a resident of Quebec.

As a general (but neither necessary nor sufficient) overview of what I personally look for in terms of collaborators

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.
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.
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.

To contact me, please use my e-mail. I check it daily and respond in a timely fashion (between 1 to 2 working days depending on the urgency).

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).