My primary research interests are:
LLMs as well as how their common sense capabilities can be exploited to train other AI models
Incorporating ideas from causality, neuroscience and cognitive science into our AI models in order to give them more human-like capabilities such as out-of-distribution generalization and long-term planning/reasoning
Applying AI to projects that have a concrete positive impact on society, by tackling problems related to drug discovery, healthcare, climate change, autonomous driving, etc.
I completed my Bachelor’s in Honours Computer Science at McGill University where I worked with Professor Blake Richards and Dr. Chen Sun on identifying important states for reinforcement learning in sparse reward environments, as well as with Professor Doina Precup and Dr. Khimya Khetarpal on temporally extended models and planning using option models in pixel environments.
I was also previously an intern at Expedia, Square Enix, Amazon, the Vector Institute and Waabi, as well as a Technical Project Manager for the McGill A.I. Society, where I helped to organize, run, and teach MAIS 202, the Accelerated Introduction to ML Bootcamp every semester.
In 2022, I was a part of McGill’s team in Project X, a machine learning research competition organized by the University of Toronto. Our paper on using deep conservative reinforcement learning for mechanical ventilation treatment (which I co-first authored) received the highest score out of all 25 papers submitted to the competition, winning in the clinical practice category.
I was also fortunate to be selected to participate in the 10th Heidelberg Laureate Forum.
Reach out at firstname.lastname@example.org if there is anything you want to discuss, I’m always happy to talk!
- DeepVentTowards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement LearningAAAI 2023
- ConSpecContrastive introspection to identify critical steps in reinforcement learningNeurIPS 2023
- HVACA Comparison of Classical and Deep Reinforcement Learning Methods for HVAC ControlUIC 2023
|Oct 28, 2023||Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels accepted to the Seventh Workshop on Generalization in Planning at NeurIPS 2023.|
|Oct 1, 2023||Attended the 10th Heidelberg Laureate Forum: Selected as one of the top 200 young researchers in math and computer science from among over 3000 applicants from all over the world to spend a week interacting with the recipients of the most prestigious awards in math and computer science (Turing Award, ACM Prize in Computing, Fields Medal, Abel Prize).|
|Oct 2, 2022||Deep Conservative Reinforcement Learning for Personalization of Mechanical Ventilation Treatment (co-first author) accepted to be published at AAAI 2023|
|Jun 8, 2022||Deep Conservative Reinforcement Learning for Personalization of Mechanical Ventilation Treatment (co-first author) presented at RLDM 2022|
|Mar 14, 2022||Deep Conservative Reinforcement Learning for Personalization of Mechanical Ventilation Treatment (co-first author) selected as best paper in the Clinical Practice section of the University of Toronto’s Machine Learning Research Competition Project X, achieving the highest score out of 25 papers|
|Mar 1, 2022||Article about Deep Conservative Reinforcement Learning for Personalization of Mechanical Ventilation Treatment (co-first author) published in McGill newspaper|