Li Ding | 丁立

Scholar / Github / Twitter / LinkedIn / CV

liding256@gmail.com liding@{umass.edu, mit.edu}

I'm currently at Google ( Mountain View), working on multimodal LLMs and on-device generative AI.

I obtained my Ph.D. from UMass Amherst CICS in 2024, advised by Lee Spector. I worked closely with Scott Niekum (UMass), Joel Lehman (Stability AI), Jeff Clune (UBC, DeepMind), and Masrour Zoghi (Google). I also interned at Google and Meta.

Before Ph.D., 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.

Research

My research insterests are in optimization for generative models and AI agents, focusing on:

  • Open-Endedness: generative models and agents for creativity-driven and open-ended learning.
  • Human-AI Alignment: safety and fairness in preference-based RL/RLHF for AI agents and LLMs.

I'm also interested in (and have published in) disciplines such as quantum ML and human-computer interaction. Before Ph.D., 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
arXiv preprint 2024
[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]


Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan
NeurIPS 2023: RealML Workshop
[arXiv] [code]


Probabilistic Lexicase Selection
Li Ding, Edward Pantridge, Lee Spector
GECCO 2023
[paper] [arXiv] [code]


Optimizing Neural Networks with Gradient Lexicase Selection
Li Ding, Lee Spector
ICLR 2022
[paper] [video] [poster] [code]


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]


Arguing machines: Human supervision of black box AI systems that make life-critical decisions
Lex Fridman, Li Ding, Benedikt Jenik, Bryan Reimer
CVPR 2019 Workshops
[paper] [arXiv] [video]


Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
Li Ding, Chenliang Xu
CVPR 2018
[paper] [arXiv] [poster] [code]

Misc.

Teaching:
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, NeurIPS, JMLR, CVPR, ICCV, 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).

Side projects:

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.

Last updated: 09/2024
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