Honglin Chen
Researcher at OpenAI
Email: honglinmc [at] gmail (dot) com
I am a researcher in the training team at OpenAI. Previously, I obtained my Ph.D. in Computer Science at Stanford University. I work on multimodal pre-training, post-training, and world modeling.
OpenAI
2024–Present
I work on multimodal pre-training and post-training. I contributed to ChatGPT Advanced Voice and GPT-5.
Stanford
2019–2024
I obtained my Ph.D. in Computer Science in 2024. My Ph.D. focused on learning world models from visual data, advised by Dan Yamins. I also had the pleasure of working with Jiajun Wu and Josh Tenenbaum.
MIT CBMM
2018–2019
I was a visiting researcher at the MIT Center for Brains, Minds, and Machines, advised by Tomaso Poggio.
UCLA
2015–2018
I obtained my B.S. in Mathematics of Computation. I worked with Demetri Terzopoulos and Andrea Bertozzi.
Selected Projects
- GPT 5, Video Perception Post-Training
- ChatGPT Advanced Voice Mode
- Dissertation: Learning to See the Physical World from Internet Videos
Thesis committee: Dan Yamins, Jiajun Wu, Nick Haber, Gordon Wetzstein, Judith Fan
- World Modeling with Probabilistic Structure Integration
Klemen Kotar, Wanhee Lee, Rahul Venkatesh, Honglin Chen, Daniel Bear, Jared Watrous, Simon Kim, Khai Loong Aw, Lilian Naing Chen, Stefan Stojanov, Kevin Feigelis, Imran Thobani, Alex Durango, Khaled Jedoui, Atlas Kazemian, Dan Yamins
- Physical Object Understanding with a Physically Controllable World Model
Rahul Mysore Venkatesh, Klemen Kotar, Lilian Naing Chen, Wanhee Lee, Gia Ancone, Seungwoo Kim, Luca Thomas Wheeler, Jared Watrous, Honglin Chen, Daniel Bear, Stefan Stojanov, Daniel LK Yamins
- Unified 3D Scene Understanding Through Physical World Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh, Jared Watrous, Honglin Chen, Khai Loong Aw, Daniel LK Yamins
- Understanding Physical Dynamics with Counterfactual World Modeling
Rahul Venkatesh*, Honglin Chen*, Kevin Feigelis*, Daniel M. Bear, Khaled Jedoui, Klemen Kotar, Felix Binder, Wanhee Lee, Sherry Liu, Kevin A. Smith, Judith E. Fan, Daniel L. K. Yamins (* equal contribution)
- Unsupervised 3D Scene Representation Learning via Movable Object Inference
Honglin Chen*, Wanhee Lee*, Koven Hong-Xing Yu, Rahul Venkatesh, Joshua Tenenbaum, Daniel Bear, Jiajun Wu, Daniel Yamins (* equal contribution)
- Unifying (Machine) Vision via Counterfactual World Modeling
Daniel Bear, Kevin Feigelis, Honglin Chen, Wanhee Lee, Rahul Venkatesh, Klemen Kotar, Alex Durango, Daniel Yamins
- Unsupervised Segmentation in Real-World Images via Spelke Object Inference
Honglin Chen, Rahul Venkatesh, Yoni Friedman, Jiajun Wu, Joshua Tenenbaum, Daniel Yamins, Daniel Bear
- Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio
- PDEs on Graphs for Semi-Supervised Learning in First-Person Activity Recognition in Body-Worn Video
Hao Li, Honglin Chen, Matt Haberland, Andrea L. Bertozzi, P. Jeffrey Brantingham
- Biomimetic Eye Modeling & Deep Neuromuscular Oculomotor Control
Masaki Nakada, Arjun Lakshmipathy, Honglin Chen, Nina Ling, Tao Zhou, Demetri Terzopoulos
- Deep Learning of Biomimetic Sensorimotor Control for Biomechanical Human Animation
Masaki Nakada, Tao Zhou, Honglin Chen, Tomer Weiss, Demetri Terzopoulos