Eddie Zhang
I study reinforcement learning for social good.
About
I am PhD student at Harvard, interested in applying RL for social good. Other areas of interest include alignment and the principles of intelligence. I am very fortunate to currently be advised by the wonderful Professor Milind Tambe. I am also grateful to have worked closely with Chuang Gan at MIT-IBM Watson, Amy Zhang at Meta, and William Wang at UCSB.
Research
Towards Generalist Agents Through Scaling Offline Reinforcement Learning
- Introduced new perspectives on pursuing Artificial General Intelligence (AGI) under the modern data-driven regime
- Proposed a computability hypothesis regarding the potential and limits of applying RL for the real-world
Offline RL with CFPI
- Designed alternative method for solving policy improvement in closed form through Taylor Approximation
- Utilized Gaussian Mixture Model for modeling heterogeneous policies tractably
Language Control Diffusion
- Proposed and created language conditioned diffusion RL models, enabling generalization in control through large language models
- Ran several experiments comparing baselines and proposed method on distributed FAIR cluster through SLURM
Education
Harvard University
University of California Santa Barbara
Led three disparate research projects: CFPI,
LCD, and an unreleased hierarchical RL project
Teaching Assistant for CS 165B (Machine Learning)
University of California Santa Barbara
GPA: 3.96
Regents Scholar (top 2.5% of school)
College of
Engineering Honors Student
Relevant coursework: Convex Optimization, Game Theory, Advanced Linear Algebra, Differential Geometry,
Statistical Machine Learning, Special Topics in Deep Learning
Employment History
Visting Researcher
- Started work on an ongoing hierarchical RL project with Chuang Gan with potential for solving extreme long-horizon control problems such as minecraft diamond crafting
- Won 3rd place out of 19 in NeurIPS Integrated Language and Understanding Challenge, receiving a $1500 cash prize
Research Intern
- Proposed, analyzed, and deployed new group page configuration reducing misinformation by 4%, improving the experience of 3 million daily active users
- Created new facebook post ranking model with 17% gain on offline engagement area under curve (AUC) metrics
- Started work on Language Control Diffusion with Amy Zhang
Computer Vision and Software Engineering Intern
- Developed multiple view calibration pipeline through planar homographies and OpenCV.
- Created set up process and capture script for NVIDIA Jetson platform with multiple third party imaging providers.
- Designed and led benchmarking of several potential imaging candidates in low light, high light, and no light settings.
- Refactored and contributed to primary user-facing web application, utilizing VueJS and Express.
Lead Full Stack Engineer/First Hire
- Led development on cloud platform in early stage startup, collaborating directly with the CEO to architect and implement proprietary API.
- Taught advanced Vue JS by taking complete responsibility at each step of the development phase – delivered full web application while teaching and leading two other interns working on the same project.
- Built and deployed 27000 line python backend to use Elastic Beanstalk, implementing dockerized development process to speed up iteration cycles by 25%.
- Gained experience with emerging web technologies such as JWT, ProtoBuf, and Nuxt.js.
Founder and Lead Tutor
- Assessed a need for tutoring code and critical thinking to children, as programming skills arose in demand and traditional tutoring services struggled to keep up.
- Collaborated with several students and parents to create lasting relationships
- Applied ability to learn rapidly and on the fly through the picking up and application of basic marketing to give sales pitches on the tutoring service
- Organized an extensive programming curriculum of 24 lessons
- Taught over 200 hours of coding and critical thinking to students
- Gained comprehensive experience with Google Cloud, Nginx, WordPress, and Frontend Web Dev through creating the tutoring business’ website, at yaitea.com
Projects
AlphaGo Zero Reimplementation
Graph Theory w/ UCSB
BERT Lecture Summarization
Predicting Winners in League
3D graphics with React
It's like LinkedIn but Tinder
Green Uber
Connecting HS Students w/ College Students
Invited Talks & Teaching
- UCSB Master's Thesis Defense, 5/31/23. Towards Generalist Agents through Scaling Offline Reinforcement Learning. Slides, Video
- MIT Brain and Cognitive Sciences, 12/13/22. Integrating Language into Reinforcement Learning through Diffusion
- NeurIPS Integrated Grounded Language and Understanding Competition, 12/06/22. Hierarchical RL through Diffusion Models
- UCSB Computer Science Research Poster Session, 6/03/22. Offine RL with CFPI
- UCSB Natural Language Processing Lab, 4/08/22. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Review
- Allthenticate Lunch & Learn, 2/24/21. Introduction to neural networks
- UCSB CMPSC 16 (C++) Learning Assistant, Winter '20
- UCSB CMPSC 165B (ML) Teaching Assistant, Spring '23
Interests
I really like learning, and thinking about learning. I like spending time with people even more.
I love playing tennis (and losing miserably at it to my superior roommate), riding the BART, hating on Apple (sometimes while riding the BART), watching anime, and hunting dinosaurs. Haha just kidding on that last one
Awards & Certifications
- Second out of 10 in Amazon Alexa Simbot Challenge ($100,000 cash prize), 2023
- Third out of 19 in Integrated Language and Understanding (IGLU) Challenge at NeurIPS ($1500 cash prize), 2022
- First out of 16 in React Category at SBhacks, 2022
- Distinction in the Major: Research Track, 2022
- First out of 78 in Startup Category at SDhacks, 2021
- Best use of Google Cloud out of 71 at SBhacks, 2021
- First overall out of 6 at Santa Barbara Startup Weekend, 2020
- First out of 70 in Database Category at SBhacks, 2020
- Second out of 85 in AI classification competiton at UCSB, 2020
- Regents Scholar UCSB, 2019
- AP Scholar with Distinction, 2019
Miscellaneous
The credit assignment problem is an extremely interesting problem that appears in Reinforcement Learning and AI in general. Let's say that I play a game of chess, and make n moves in succession. At the end of the game, I get just one discrete feedback signal: the outcome of the game. How does one attribute the importance of each move to the outcome of the game? This is the credit assignment problem. For a more in-depth introduction to the topic I would recommend this paper from Minsky, starting from part 3 on page 10.
The reason I mention this here is because very little of my career credit should be attributed to me. I am eternally grateful to the following people for their kindness, support and guidance. Without them, I would have nothing. In order of recency (not importance): Jiachen Li, Chad Spensky, Shou Chaofan, Derren Slinde.bastardization of my actual name edwin.
my friends thought this was hilarious and
so they started calling me that too:
the domain name is just a massive joke.
