I am a first-year Ph.D. student in the Robotics Institute at Carnegie Mellon University,
supervised by Prof. Jiaoyang Li .
I completed my MSc in Computer Science at the University of Toronto, co-advised by Prof.
Florian Shkurti and Prof. Tim Barfoot.
I received a Bachelor of Applied Science in Engineering Science from the University of Toronto.
I have been fortunate to work under many fantastic researchers in robotics. Previously, I collabrated with Prof.
Fabio Ramos from Nvidia 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.
My research focuses on developing planning algorithms for robot and robot teams to accomplish long-horizon tasks
in dynamic and uncertain environment.
I am particuarly interested in solving problems in real-world robotics and have worked on many different applications,
such as multi-robot assembly, autonomous surface vessels (ASV) for environmental monitoring, and self-driving cars.
I have also worked on continual learning for robot dynamics modeling and control.
If you have any questions / want to collaborate, feel free to reach out and send me an email!
I am very excited to talk with more people and learn about your work!
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.