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:
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]
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]
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).
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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