About

Li Jing is a postdoctoral researcher at Facebook AI Research (FAIR), working with Yann LeCun. Before joining FAIR, he obtained his PhD in physics from MIT advised by Marin Soljacic. He received a BS in physics and a BA in economics from Peking University. He is a co-founder of Lightelligence Inc..

Research Interests

• Machine Learning, Artificial Intelligence, Computer Vision
• Self-supervised Learning, Representation Learning
• Learning Theory, AI for Science, AI Hardware

Selected Honors

• Forbes China 30 under 30 Enterprise Technology 2019

• International Physics Olympiad (IPhO) Gold Medal 2010

Selected Publications

Self-supervised Learning

Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian
[Paper]

Equivariant Contrastive Learning
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
[Paper]

Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar*, Li Jing*, Ishan Misra, Yann LeCun, Stephane Deny
ICML 2021
[Paper] [Code] [Talk]

Implicit Rank-Minimizing Autoencoder
Li Jing, Jure Zbontar, Yann LeCun
NeurIPS 2020
[Paper] [Code] [Talk]

Recurrent neural networks and NLP

We Can Explain Your Research in Layman’s Terms: Towards Automating Science Journalism at Scale
Rumen Dangovski,Michelle Shen, Dawson Byrd, Li Jing, Desislava Tsvetkova, Preslav Nakov, Marin Soljacic
AAAI 2021
[Paper]

Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
Rumen Dangovski*, Li Jing*, Marin Soljacic
TACL (presented at NAACL) 2019
[Paper] [News] [Code]

Gated Orthogonal Recurrent Units: On Learning to Forget
Li Jing*, Caglar Gulcehre*, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio
Neural Computation 2019
[Paper] [Code]

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Li Jing*, Yichen Shen*, Tena Dubcek, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic
ICML 2017
[Paper] [Code] [Talk]

AI for Science

Deep learning enabled self-adaptive invisibility cloak
Chao Qian, Bin Zheng, Yichen Shen, Li Jing, Erping Li, Lian Shen, Hongsheng Chen
Nature Photonics 2020
[Paper] [News]

Migrating knowledge between physical scenarios based on artificial neural networks
Yurui Qu*, Li Jing*, Yichen Shen, Min Qiu, Marin Soljacic
ACS Photonics 2019
[Paper]

Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks
John Peurifoy, Yichen Shen, Li Jing, Yi Yang, Fidel Cano-Renteria, Brendan Delacy, John Joannopoulos, Max Tegmark, Marin Soljacic
Science Advances 2018
[Paper] [News] [Code]

AI Software-Hardware Co-design

Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications
Matthew Khoury, Rumen Dangovski, Longwu Ou, Preslav Nakov, Yichen Shen, Li Jing
EMNLP 2020
[Paper]

Heuristic Recurrent Algorithms for Photonic Ising Machines
Charles Roques-Carmes, Yichen Shen, Cristian Zanoci, Mihika Prabhu, Fadi Atieh, Li Jing, Tena Dubcek, Vladimir Ceperic, John Joannopoulos, Dirk Englund, Marin Soljacic
Nature Communications 2020
[Paper] [News]