I’m currently spending time at Google Brain Robotics
working at the intersection of robotic perception and manipulation. My research centers around Deep
Learning and its applications to Computer Vision and Robotics. In particular, I am interested in
developing algorithms that can allow machines to intelligently interact with the physical world and
learn to master complex skills over time through self-supervision. I'm driven to build things that
will have positive impact at scale.
Last summer, I interned at Nimble AI
where I built and deployed
a real-time, generative grasping algorithm for suction and parallel-jaw grasping. Before that, I was a visiting researcher
in the Khuri-Yakub Ultrasonics Group
at Stanford University,
applying machine learning to enhance a chemical nose, an ultrasonic touch screen and a flow meter.
Outside of work, I enjoy contributing to open source and blogging
April 2019 -
I’ll be attending Stanford University in September 2019 to begin my master’s degree in Computer Science.
Feb 2019 - Present
Google Brain Robotics Intern
3D Perception and Manipulation
Nimble AI Robotics Intern
Visual Affordance Networks for Robotic Grasping
Stanford University Khuri-Yakub Group
Machine Learning for CMUTs
American University of Beirut
B.Eng. Electrical Engineering
A general-purpose library for robotics research. Supports
RealSense D415 interfacing, pointcloud manipulation and visualization, UR5 robotic arm
interfacing in real and in sim via PyBullet, and a transformation class for dealing with
3D positions and orientations.
Tune the hyperparameters of your PyTorch models with a
Vector Norm List Processor
A processor in Verilog that computes the L2 norm of an N-dimensional
complex vector stored in doubly-linked list. Had some fun adding Python
scripts to automate the reading and writing of test benches.