I am Machine Learning Engineer at Ocado Engineering working to build an autonomous robotics system for grocery fulfillment. In the past, I was a research student at University of Toronto, adviced by Dr. Jonathan Kelly where I had the opportunity to publish several papers in the field of Robotics and Artificial Intelligence. I am a receipient of the NSERC Canadian Graduate Scholarship and Vector Scholarship in AI.
I graduated from University of Waterloo with a bachelors in Mechatronics Engineering and an option in AI, during which time I worked as a research assitant for Dr. Krzysztof Czarnecki, Dr. Mihaela Vlasea, and Dr. James Tung. For 12 months, I worked as an intern at Nvidia US where I contributed to their self-driving infrastructure. Through various co-ops and internships, I graduated with over 2 years of work experience.
Please checkout my projects and publications below.
MASc in Robotics and AI (Aerospace Department), 2021
University of Toronto
BASc in Mechatronics Engineering, 2019
University of Waterloo
Worked in a team of 25 machine learning engineers, led by Dr. Urs Muller, creating an end-to-end autonomous driving solution. (Linux, C++, Git, Bash)
Worked on Nvidia’s DRIVE hardware stack, aiming to revolutionize the Automotive computing industry (C++, bash, python)
As a core member of the software development team, I worked closely with the R&D department to prototype a new application for their tool tracking system.
Completed as part of a graduate course at the University of Toronto | PyTorch. Implemented a Fully Connected Network based Road Segmentation pipeline on Audi’s A2D2 dataset using PyTorch and OpenCV.
Completed as part of a graduate course at the University of Toronto | Tensorflow, Python, OpenAI gym. Conducted a failure mode analysis on learned DDPG-based policies used to control a dexterous hand with tactile sensors, in an attempt to understand the utility of tactile information.
Completed as part of a graduate course at the University of Toronto | MATLAB. Simultaneous Localization and Mapping (SLAM) has been a highly research problem and the techniques that solve it have undergone huge improvements in the last two decades.