Robot Vision and Learning Lab @ University of Toronto

• Currently developing a novel technique to convert a monocular video into a dynamic 3D gaussian splatting representation to use for large scale imitation learning.
• Created a computer vision pipeline to incorporate learning into task and motion planning (TAMP) problems by learning representations for 3D scenes with a series of CNNs and GNNs using perception data and automatically generated scene graphs from object poses.
• Used Slurm and Docker to parallelize multiple data collection instances from a DRAKE simulation of a Franka Panda environment and multiple model training sessions in Tensorflow to gather training statistics and compare different models efficiently.

Lab for Translational Engineering @ Massachusetts Institute of Technology and BWH

• Led the development of a pill sized ingestible robot to autonomously deliver mRNA vaccines inside the stomach through a non-invasive manner. (Manuscript under preperation)
• Created an ingestible robot to create a map of the small intestine in 10 wavelengths to non-invasively and autonomously detect internal bleeding and bruising. (Manuscript under preparation)
• Developed a low power algorithm in C++ to fuse sensor data on an implant and detect opioid overdoses. An Implantable System for Opioid Safety, Device 100517

Machine Learning Group @ Electronic Arts

• Created a tool for the Frostbite game engine in C++ to gather 3D bounding boxes for key animation objects and stream the data to a remote server for machine learning models.
• Optimized the FIFA game testing process by designing an Autoencoder CNN with Tensorflow to detect visual animation glitches and therefore allowing human game testers to focus on more complex feedback.