Assistant Professor
Department of Computer Science
Courant Institute of Mathematical Sciences
Center for Data Science (joint)
New York University
Email: mengye@nyu.edu
Tel: +1 (212) 992-7547
Office: 60 5th Ave, Rm 508, New York, NY, 10011
LinkedIn X CV YouTube Google Scholar Agentic Learning AI Lab
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 Ph.D. 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.
Areas: machine learning, computer vision, representation learning, meta-learning, few-shot learning, brain & cognitively inspired learning, robot learning, self-driving vehicles
My key research question is: how do we enable human-like, agent-based machine intelligence to continually learn, adapt, and reason in naturalistic environments? I am interested in the emergence of intelligence by learning from a point-of-view experience. Current research topics in my lab are:
Memorization and forgetting in sequentially changing environments
Visual representation learning in the wild using egocentric videos
Few-shot learning, reasoning, and abstraction in vision and language
Human-AI alignment in personalized AI
NYU DS-GA 3001: Advanced Topics in Embodied Learning and Vision [2025 spring]
NYU DS-GA 1008 / CSCI-GA 2572: Deep Learning [2024 spring]
NYU CSCI-GA 2565: Machine Learning [2023 fall] [2024 fall]
NYU DS-GA 1003: Machine Learning [2023 spring]
Vector Institute: Deep Learning II [2020 fall]
UofT CSC 411: Machine Learning and Data Mining [2019 winter]
2024/12: I will teach a new course on Embodied Learning and Vision in Spring 2025 at NYU.
2024/10: Congrats Jack Lu on getting the NSERC PGS-D award!
2024/09: One paper is accepted at NeurIPS 2024.
2024/09: I will give an invited talk at Columbia University.
2024/07: We are organizing Adaptive Foundation Models Workshop at NeurIPS 2024. Consider contributing your papers!
2024/07: We are organizing Compositional Learning Workshop at NeurIPS 2024. Consider contributing your papers!
2024/07: One paper is accepted at COLM 2024.
2024/07: One paper is accepted at ECCV 2024.
2024/05: One paper is accepted at ICML 2024.
2024/04: Congrats Chris Hoang on receiving the 2024 DoD NDSEG fellowship!
2024/04: I will give an invited talk at Flatiron Institute in New York.
2024/04: One paper is accepted at CoLLAs 2024.
2024/04: One paper is accepted at CogSci 2024.
2024/04: I gave an invited talk at the German Consulate General in New York.
[Full List] [Google Scholar] [dblp]
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle. Hui Dai, Ryan Teehan, Mengye Ren. arXiv preprint 2411.08324, 2024. [arxiv]
PooDLe: Pooled and dense self-supervised learning from naturalistic videos. Alex N. Wang, Christopher Hoang, Yuwen Xiong, Yann LeCun, Mengye Ren. arXiv preprint 2408.11208, 2024. [arxiv]
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. Yanlai Yang, Matt Jones, Michael C. Mozer, Mengye Ren. NeurIPS, 2024. [arxiv]
CoLLEGe: Concept embedding generation for large language models. Ryan Teehan, Brenden M. Lake, Mengye Ren. COLM, 2024. [arxiv]
ProCreate, don’t reproduce! Propulsive energy diffusion for creative generation. Jack Lu, Ryan Teehan, Mengye Ren. ECCV, 2024. [arxiv]
Integrating present and past in unsupervised continual learning. Yipeng Zhang, Laurent Charlin, Richard Zemel, Mengye Ren. CoLLAs, 2024. [arxiv]
Self-supervised learning of video representations from a child’s perspective. Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, Brenden M. Lake. CogSci, 2024. [arxiv]
Learning and forgetting unsafe examples in large language models. Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren. ICML, 2024. [arxiv]
LifelongMemory: Leveraging LLMs for answering queries in long-form egocentric videos. Ying Wang, Yanlai Yang, Mengye Ren. arXiv preprint 2312.05269, 2023. [arxiv]
BIM: Block-wise self-supervised learning with masked image modeling. Yixuan Luo, Mengye Ren, Sai Qian Zhang. arXiv preprint 2311.17218, 2023. [arxiv]
Scaling forward gradient with local losses. Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey Hinton. ICLR, 2023. [arxiv] [pdf] [code] [html]
Learning in temporally structured environments. Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin F. Elsayed, Katherine Hermann, David Mayo, Michael C. Mozer. ICLR, 2023. [pdf]
Multitask learning via interleaving: A neural network investigation. David Mayo, Tyler Scott, Mengye Ren, Gamaleldin Elsayed, Katherine Hermann, Matt Jones, Michael Mozer. CogSci, 2023. [pdf]