agentic learning
ai lab
design

Mengye Ren

Assistant Professor

Mengye Ren is an assistant professor of computer science and data science at New York University (NYU). He runs the Agentic Learning AI Lab. Before joining NYU, he was a visiting faculty researcher at Google Brain Toronto working with Prof. Geoffrey Hinton. From 2017 to 2021, he was a senior research scientist at Uber Advanced Technologies Group (ATG) and Waabi, working on self-driving vehicles. He received PhD in Computer Science from the University of Toronto, advised by Prof. Richard Zemel and Prof. Raquel Urtasun. His research focuses on making machine learning more natural and human-like, in order for AIs to continually learn, adapt, and reason in naturalistic environments.

Mengye Ren's Papers

2024-11-13
Are LLMs Prescient? A Continuous Evaluation using Daily News as Oracle. Amelia Dai, Ryan Teehan, and Mengye Ren.
2024-08-20
PooDLe: Pooled and Dense Self-Supervised Learning from Naturalistic Videos. Alex N. Wang, Chris Hoang, Yuwen Xiong, Yann LeCun, and Mengye Ren.
2024-04-29
Integrating Present and Past in Unsupervised Continual Learning. Yipeng Zhang, Laurent Charlin, Richard S. Zemel, and Mengye Ren.
2024-03-22
CoLLEGe: Concept Embedding Generation for Large Language Models. Ryan Teehan, Brenden M. Lake, and Mengye Ren.
2024-03-14
Reawakening Knowledge: Anticipatory Recovery from Catastrophic Interference via Structured Training. Yanlai Yang, Matt Jones, Michael C. Mozer, and Mengye Ren.
2024-02-01
Self-Supervised Learning of Video Representations from a Child's Perspective. A. Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, and Brenden M. Lake.
2023-12-20
Learning and Forgetting Unsafe Examples in Large Language Models. Jiachen Zhao, Zhun Deng, David Madras, James Zou, and Mengye Ren.