I have also been fortunate to work under many fantastic researchers in robotics. Previously, I collabrated with Prof.
Fabio Ramos from Nvidia and on task and motion planning.
In undergrad, I was involved in the student design team aUToronto, where we competed and won the SAE Autodrive Challenge.
I also spent one amazing summer in Israel with Prof. Amir Degani and the summer before that with Prof. Angela Schoellig.
The goal of my research is to improve robot's ability to model uncertain environments and make plans for complex and long-horizon tasks.
Towards this end, I am currently working on planning algorithms for autonomous surface vessel (ASV) in Canadian lakes.
Before that, I have also also explored continual learning in learning dynamics model for control.
RL can be applied to the Minimum Latency Problem by using a graph attention network to encode stochastic policy for constructively building partial paths,
yielding solutions which are comparable to state-of-the-art, hand-engineered methods.
Re-implemented the SIGGRAPH 96 paper from David Baraff that introduced a linear-time sparse solver with Lagrangian multiplers in MATLAB.
Verified that the time complexity of our re-implemented sparse solver is linear with serial chains and trees.