I am a PhD student at the University of Cambridge, where I am advised by Ferenc Huszár and David Krueger, and generously funded by a scholarship from Twitter. My research focus is on empirical approaches to understanding how deep learning works, especially at scale. I am also interested in policy considerations for the responsible development of AI.
I completed an MSc at the Université de Montréal and Mila, where I was advised by Laurent Charlin and worked at the intersection of self-supervised learning and deep reinforcement learning. I previously spent a few years working at Airbnb on site performance and anti-fraud initiatives. Prior to that, I did my undergrad in Software Engineering and Computer Science at the University of Waterloo. During that time I had the opportunity to study on exchange at the Hong Kong University of Science and Technology, and work at startups in Toronto and San Francisco as well as a financial services firm in New York.
Really though, I would prefer to spend my time disconnected from technology and the internet – attending film festivals, drinking wine at a beach somewhere warm while reading interesting books, and enjoying the company of good friends. Please talk to me about movies, long-distance running, world events, Georgist socio-economic policy, or literally anything that is not focused on tech.
“Beware of Nitarshan in general… He asks thought-provoking questions that will make you rethink your whole research agenda”
“Quite fun and interesting. Those are his parameters.”
“Nitarshan’s wardrobe is definitely proof of discerning consumption”
Self-Supervision for Data Interpretability and Data Efficiency in Reinforcement Learning
Pretraining Representations for Data-Efficient Reinforcement Learning
arXiv Code NeurIPS 2021 ICLR 2021 Workshop (SSL-RL) (Rejected from ICML 2021)
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, Devon Hjelm, Phil Bachman, Aaron Courville