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Li Ding | 丁立

liding [at] {umass.edu, mit.edu}


I'm a 3rd-year Ph.D. student in Computer Science at UMass Amherst, advised by Prof. Lee Spector. My research interests are optimization algorithms and related areas including evolutionary computing, deep learning, and computer vision.

During Ph.D., I interned at Meta Reality Labs. Before joining UMass, I was a research engineer at MIT for three years, working with Dr. Lex Fridman and Dr. Bryan Reimer. I received my master's degree in Data Science from the University of Rochester, where I got started on research working with Prof. Chenliang Xu.


  • 01/2023 - One paper published in Entropy (Special Issue: Quantum Machine Learning).
  • 06/2022 - Passed Ph.D. Qualifying Exam.
  • 05/2022 - Joined Meta Reality Labs (XR Tech team) as a research scientist intern.
  • 04/2022 - Three papers accepted at GECCO '22 (one poster and two workshop papers).
  • 03/2022 - Invited talk at UMass CICS (Autonomous Learning Lab).
  • 01/2022 - One paper accepted at ICLR '22.
  • 07/2021 - One paper published in IEEE Transactions on Intelligent Vehicles.
  • 04/2021 - One paper accepted at GECCO '21 Workshop on NeuroEvolution at Work.
  • 04/2021 - One paper accepted at IEEE IV '21.
  • 11/2020 - Invited talk at Ford Research & Advanced Engineering.
  • 09/2020 - Joined UMass Amherst CICS as a Ph.D. student.
  • 06/2020 - Public release of the MIT DriveSeg Dataset.
  • 06/2020 - One paper accepted at IEEE IV '20.
  • 10/2019 - Invited talk at MIT CSAIL (Data Systems Group).
  • 04/2019 - One paper accepted at CVPR '19 Workshop on Autonomous Driving.
  • 09/2018 - One paper accepted at NeurIPS '18 Deep RL Workshop.
  • 04/2018 - One paper accepted at CVPR '18.
  • 09/2017 - Joined MIT as a research engineer (full-time).



Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits
Li Ding, Lee Spector
Entropy (Special Issue: Quantum Machine Learning)


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

Going Faster and Hence Further with Lexicase Selection
Li Ding, Ryan Boldi, Thomas Helmuth, Lee Spector
GECCO 2022 (poster)

Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
Li Ding, Lee Spector
GECCO 2022: Quantum Optimization Workshop (oral)
[paper] [arXiv]

Lexicase Selection at Scale
Li Ding, Ryan Boldi, Thomas Helmuth, Lee Spector
GECCO 2022: Large-Scale Evolutionary Optimization and Learning Workshop (oral)
[paper] [arXiv]


Value of Temporal Dynamics Information in Driving Scene Segmentation
Li Ding, Jack Terwilliger, Rini Sherony, Bryan Reimer, Lex Fridman
IEEE Transactions on Intelligent Vehicles
[paper] [arXiv] [MIT DriveSeg Dataset]
Press coverage: [MIT News] [Forbes] [InfoQ] [TechCrunch]

Evolving Neural Selection with Adaptive Regularization
Li Ding, Lee Spector
GECCO 2021: NeuroEvolution at Work Workshop (oral)
[paper] [arXiv] [video]

Perceptual Evaluation of Driving Scene Segmentation
Li Ding, Rini Sherony, Bruce Mehler, Bryan Reimer
IEEE IV 2021 (oral)
[paper] [video]


MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios
Li Ding, Michael Glazer, Meng Wang, Bruce Mehler, Bryan Reimer, Lex Fridman
IEEE IV 2020: NDDA Workshop (oral)
[paper] [video]

Joint Cognitive Load Estimation and Eye Landmarks Detection in the Wild
Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Bruce Mehler, Bryan Reimer, Lex Fridman
[Under Review]


Arguing Machines: Human Supervision of Black Box AI Systems that Make Life-Critical Decisions
Lex Fridman, Li Ding, Benedikt Jenik, Bryan Reimer
CVPR 2019: Workshop on Autonomous Driving
[paper] [arXiv] [video]

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
[paper] [arXiv] [video]

Object as Distribution
Li Ding, Lex Fridman
Technical Report


Human Interaction with Deep Reinforcement Learning Agents in Virtual Reality
Lex Fridman, Henri Schmidt, Jack Terwilliger, Li Ding
NeurIPS 2018: Deep RL Workshop

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


TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation
Li Ding, Chenliang Xu
Technical Report

Last update: 02/2023
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