Microsoft Research (First Author)
Wrote a paper, as the first author, accepted for publication to ICLR 2020, as part of a Pre-doc with MSR. The work demonstrates the the sensitivity of modern memory approaches to policy stochasticity and noise in deep RL. We propose a solution framework that improves average return by 19% over a baseline with the same number of parameters, and by 9% over a DNC, with many more parameters.
Feedback was very positive. For example, Reviewer1 states:
"The experiments provided are good, and vary nicely between actual RL runs and theoretical analysis, all of which convinces me that this could well become a standard Deep RL component... It would be good to see more papers proposing new neural components with this kind of rigour."
(Note, scores are now out of 8, and 6 is second greatest possible score.)
SDC Lab (Research Member)
Worked on social interactions between humans and autonomous vehicles, calculating Stackelberg equilibria in game trees. Paper accepted and to be published at the 2019 International Conference on Social Robotics.
SDC Lab (Research Lead)
Leading ongoing research into training an autonomous vehicle via imitation learning with labeled feedback. Used transfer learning with an inception net backbone. Trained the car on the task of lane following and compared the time until the car made a mistake for various architectures and loss functions.
Research into a biologically inspired RNN with a notion of space and conservation of energy, for a graduate AI seminar.
Implemented the paper "Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation" by Wu et al. for a graduate seminar.
Food with Friends
Co-founder, designer, and developer
Responsible for: multi-threading, algorithms, maps and texting api, user data storage, user management, product ideas, UI, logo, design, dividing work
(screenshots available depending on device)
Paper for Graduate Level Robotics Seminar
Coded an agent in Minecraft using reinforcement learning and emotion detection implemented via computer vision (OpenCV)
SDC Lab (Group Member)
We made a DQN for lane sharing as our first lab project. The goal for the agent was to get to the other side of the truck as fast as possible without crashing. We compared the performance of the RL agent to a human, against three different "dumb" AI opponents that had different aggression levels.
Implemented a deep convolutional generative adversarial network (DCGAN) for a deep learning graduate seminar. The neural network completes faces with up to 80% of the pixels missing.
A horror video game featuring Brown University's very own Blueno the Bear – aka Untitled (Lamp/Bear)
Please play in full screen!
A simple platformer with a twist. (Made in Unity.)
Please play in full screen!
Winner of the Kalundborg Game Jam 2017. A simple game with a grappling hook mechanic. Made in Unity in 30 hours minus sleep.