Raymond Chua
Hey there and a warm welcome!
đ MontrĂ©al, QuĂ©bec, Canada
Last updated 23 Feb 2025.
Iâm a (final-year) doctoral candidate at McGill University and Mila, advised by Doina Precup and Blake Richards. Their expertise in machine learning and computational neuroscience has been instrumental in shaping my approach to exploring the intricate relationship between artificial intelligence and brain function. My work dives into how neural mechanisms of representation and memory can be modeled to enhance AIâs adaptability and efficiency. By integrating insights from neuroscience, I aim to develop AI systems capable of continuous learning and adept at navigating unpredictable environments.
My expected graduation date is in Fall 2025 and will be on the lookout for post-graduation opportunities in academia, industry, or research labs.
In addition to my research, I am actively involved in mentoring emerging talent in the field of machine learning. I supervise students from McGill University, University of Montreal, and Mila who are pursuing challenging internships with leading companies in Montreal and Quebec. These internships, lasting 6-9 months, often blend real-world applications with research components, addressing new problems where established methods do not yet exist. My role involves advising both the students and sometimes the companies on the selection of machine learning models, developing data pipelines, and refining model performance. This mentorship not only allows me to give back to the academic community but also keeps me connected to the latest innovations in the industry. For more detailed information about the students and projects I have mentored, please visit my mentorship page.
My personal connection to this work stems from having a family member with learning disabilities, which has deeply motivated me to understand how our brains function and why some people face challenges in learning. Through my research, I hope to advance our understanding of these fundamental processes and create AI systems that could one day assist individuals with learning difficulties, making my work relevant not only to the scientific community but to society as a whole.
Beyond academia, the Covid-19 pandemic rekindled my passion for sports, leading me to cross-country skiing, swimming, cycling, and runningâculminating in participating in triathlons. I have dedicated a section under the projects page to share these adventures, offering a glimpse into my life beyond academia. Feel free to check it out, or visit my Instagram to see what keeps me busy when Iâm not at my computer!
news
| Mar 10, 2025 | Invited by the Integrate and Fire Seminar organisers at McGill University to share my perspective on the discussion theme of the night, âHow Intelligent is AI?â Through sharing my own work on reinforcement learning, I presented some evidence which showed that agents learned solely on rewards can obtain some form of intelligence. |
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| Jan 30, 2025 | Invited by Prof. Paul Masset to give a talk on my research on Successor Features and Continual Reinforcement Learning at his lab in McGill University, Montréal. The talk was well-received, and I had the opportunity to discuss my work with several members of his lab. Look forward to future collaborations with the lab and hope to visit again soon! |
| Dec 08, 2024 | Gave a short talk on my work on Continual Reinforcement Learning at the âForesight Institute Vision Weekend event in San Francisco!â |
| Sep 28, 2024 | Presented our recent work on Continual RL using Successor Features and Memory Mechanisms at NAISys, a NeuroAI conference at Cold Spring Harbour Laboratory at NY, USA. đđșđž. Stay tuned for the preprint (early 2025)! đđ§ |
| Sep 25, 2024 | My first research paper titled âLearning Successor Features the Simple Way,â on which I am the primary first author, co-authored with Arna Ghosh, Christos Kaplanis, Blake Richards and Doina Precup, has been accepted to the Neural Information Processing Systems conference (NeurIPS) a top-tier machine learning conference with a very selective 25.8% acceptance rate. Check out the blogpost and the paper! đ |