Li Ding | 丁立
I'm a final-year PhD candidate at UMass Amherst CICS, advised by Lee Spector. I work closely with Scott Niekum (UMass), Joel Lehman (Stability AI), Jeff Clune (UBC, DeepMind), and Masrour Zoghi (Google Research). I also spent time at Google Research and Meta.
Before PhD, I was a full-time research engineer at MIT with Lex Fridman and Bryan Reimer, and concurrently a graduate student at MIT CSAIL. I did my master's at Univ. of Rochester with Chenliang Xu.
liding@{umass.edu, mit.edu}
Google Scholar / Github / Twitter / LinkedIn / CV
Research
My research focus is optimization algorithms for large models and AI agents, focusing on:
- Open-Endedness: generative models and AI agents for open-ended tasks and environments.
- Human-AI Alignment: preference learning, reinforcement learning from human feedback (RLHF), and safe RL.
I'm also interested and have published in disciplines including quantum ML and human-computer interaction. Before PhD, I worked on deep learning for autonomous driving, cognitive modeling, and action recognition.
Selected Publications
For a complete and up-to-date list of publications, please see Google Scholar.
Pareto-Optimal Learning from Preferences with Hidden Context
Ryan Boldi, Li Ding, Lee Spector,
Scott Niekum
Preprint
[arXiv]
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
Li Ding,
Jenny Zhang
,
Jeff Clune
,
Lee Spector
,
Joel Lehman
ICML 2024
NeurIPS 2023: ALOE Workshop (Spotlight)
[project page]
[arXiv]
[demo]
[talk]
[code]
[tutorial]
Value of Temporal Dynamics Information in Driving Scene Segmentation
Li Ding, Jack Terwilliger, Rini Sherony, Bryan Reimer, Lex Fridman
IEEE Transactions on Intelligent Vehicles, 2021
[paper]
[arXiv]
[MIT DriveSeg
Dataset]
Press coverage:
[MIT News]
[Forbes]
[InfoQ]
[TechCrunch]
MIT Advanced Vehicle Technology Study:
Large-Scale Naturalistic Driving Study of
Driver Behavior and Interaction with Automation
Lex Fridman, Daniel E. Brown, Michael Glazer, William Angell, Spencer Dodd, Benedikt Jenik, Jack
Terwilliger, Julia Kindelsberger, Li Ding, Sean Seaman, Alea Mehler, Andrew Sipperley, Anthony
Pettinato, Bobbie Seppelt, Linda Angell, Bruce Mehler, Bryan Reimer
IEEE Access, 2019
[paper]
[arXiv]
[video]
Human Interaction with Deep Reinforcement
Learning Agents in Virtual Reality
Lex Fridman, Henri Schmidt, Jack Terwilliger, Li Ding
NeurIPS 2018: Deep RL Workshop
Misc.
Teaching:
TA for UMass COMPSCI 230:
Computer
Systems Principles (Summer 2021).
TA for MIT 6.S094: Deep Learning for Self-Driving
Cars
(Winter 2018-19).
TA for MIT 6.S099:
Artificial General Intelligence (Winter 2019).
Reviewer:
ICLR 2024, ECCV 2024, NeurIPS 2023, ICCV 2023, CVPR 2023, etc.
Open source projects:
google-research/ev3: Meta-learning optimization in JAX.
facebookresearch/d2go: Efficient model
training and deployment on mobile platforms.
pyribs: An open-source library for quality
diversity optimization.
mit-deep-learning: Tutorials and
coding assignments for MIT Deep Learning courses (9k+ stars).
MIT AI
Podcast
Helped prepare interview questions, search for guest speakers, etc. for a
podcast hosted by Lex Fridman about technology, science, and the
human
condition.
(Ranked #1 on Apple Podcasts in the technology category, 1M views
on YouTube.)
(My personal favorite episode is Tomaso Poggio, highly
recommended!)
MIT Robocar Workshop
Instructor for a summer/winter workshop at MIT with Tom Bertalan to
college and high school
students on building and programming autonomous robocars.
