Compositional Pattern-Producing Networks

Source: code, CPPN, otoro, TensorFlow.js



Li Ding | 丁立


I'm currently a Research Engineer at MIT, working on deep learning for driving scene perception and vision-based control of autonomous vehicles. Our research (involving Dr. Lex Fridman and colleagues) focuses on Human-Centered Artificial Intelligence (HCAI) that leverages both human knowledge and artificial intelligence to explore the future of human-machine collaboration.

I TAed MIT 6.S094: Deep Learning for Self-Driving Cars (Jan. 2018) & MIT 6.S099: Artificial General Intelligence (Feb. 2018). If you have questions regarding the courses, please email us via deepcars [at] mit.edu / agi [at] mit.edu, respectively.

Prior to joining MIT, I was a Research Associate at University of Rochester, after graduating with M.S. degree in Data Science. I did research on human action recognition with Prof. Chenliang Xu.

I'm from Shanghai, China. On a side of fun, I'm a casual Kaggler interested in playing with various kinds of data. I like photography, art, electro-funk, all kinds of cuisine, and at the moment, traveling around with Pokémon Go.

liding [at] mit.edu
Github
LinkedIn
CV

Last updated on 2018/10/14


Research
2018

MIT Human-Centered Autonomous Vehicle

The interaction between human and machine with growing intelligence challenges our assumptions about the limitations of human beings at their worst and the capabilities of AI systems at their best. We explore Human-Centered Autonomous vehicle as an illustrative case study of concepts in shared autonomy.
More on project page.


Arguing Machines: Human Supervision of Black Box AI Systems

We consider the paradigm of a black box AI system that makes life-critical decisions. By proposing an “arguing machines” framework, we show that the disagreement between the two AI systems, without any knowledge of underlying system design or operation, is sufficient to arbitrarily improve the accuracy of the overall decision pipeline if given human supervision over disagreements.
More on project page.


Transfer of Control in Racing Simulation

A racing simulation environment with fully functioned end-to-end autonomous driving system is developed based on Forza Motorsport 7. We study the fundamental behavior in transfer of control between human and machine.


Dynamic Scene and Optical Flow

Single-frame-based algorithms ignore the rich information contained in the dynamics of moving scene, which is usually too noisy or abstract to utilize. We study how motion representations such as optical flow can be used to help scene understanding.
More on project page.


2017

Scene Segmentation and Large-Scale Annotation

We adopt the latest semantic segmentation techniques and modify the architecture to fit into the task of driving scene perception. We also explore ways to improve accuracy and robustness, such as edge case discovery and more efficient annotation methodology.


Fully / Weakly Supervised Action Localization

We propose a new action classification model and a novel iterative alignment approach to address weakly supervised action localization task that only require the transcript to learn the exact time of actions.
More in our CVPR '18 paper with code available on Github.



Publication

Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
Lex Fridman, Li Ding, Benedikt Jenik, Bryan Reimer
[under review] [pdf] [arXiv]

MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis 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, Hillary Abraham, Alea Mehler, Andrew Sipperley, Anthony Pettinato, Bobbie Seppelt, Linda Angell, Bruce Mehler, Bryan Reimer
[under review] [pdf] [arXiv]

Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
Li Ding, Chenliang Xu
[CVPR '18] [pdf] [arXiv] [code] [poster]

TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation
Li Ding, Chenliang Xu
[arXiv Report] [pdf] [arXiv]


Service
TA


Reviewer

IEEE Transactions on Circuits and Systems for Video Technology (2018)
IEEE Access (2018)


Kaggle

Level: Competitions Expert (highest rank: 1169th | current rank)

Statoil/C-CORE Challenge
(Satellite Image Classification)

· 2018 · Top 6%

Data Science Bowl 2017
(Lung Cancer Detection)

· 2017 · Top 6%


Thanks for visiting!