I’m a PhD student in the Machine Learning Group, co-supervised by Prof. Jimmy Ba and Prof. Sanja Fidler. During my undergraduate degree, I studied Engineering Science, majoring in Electrical and Computer Engineering at the University of Toronto. My undergraduate thesis advisor was Prof. Deepa Kundur. I interned at Qualcomm Canada in the Video Processing group as part of my Professional Experience Year for 16 months. Prior to starting my graduate degree, I spent a year working at Intel Programmable Solutions Group (PSG) in the OpenCL Usability team. Recently, I was a research intern at Borealis AI (with Kevin Luk), Google Brain Toronto (with William Chan and Jamie Kiros) and Google Research (with DeLesley Hutchins). More details in my CV.
I’m also a speedcuber.
If you are interested in working with me and/or in our lab, please fill out this application form or contact me directly.
My dream is to build artificial general intelligence (AGI). Currently, I’m interested in developing efficient and generalizable learning systems at the intersection of reinforcement learning and natural language processing, which can allow humans to interact with the system as it learns, and potentially help it learn more like humans. I’m also working on generative models for discrete objects such as graphs and texts, as a step towards abstract modeling of the world.
I was a Teaching Assistant for the following courses:
- CSC413/2516 Neural Networks and Deep Learning, Winter 2020.
- CSC311 Introduction to Machine Learning, Fall 2019/2020.
- ECE421 Introduction to Machine Learning, Winter 2019. Taught weekly 2-hour tutorials.
- MIE324 Introduction to Machine Intelligence, Fall 2018. Developed 3 new assignments over the summer.
- CSC321 Introduction to Neural Networks, Winter 2018
- CSC411 Introduction to Machine Learning, Fall 2017
- Closing the generalization gap in stochastic optimization through Fisher gradient noise. Vector Institute, Toronto, Canada. February, 2018