We study new ways to scale learning, from data to discovery.
Modeling & Inference. We expand the reach of models by their and to new and challenging .
Reasoning & Self-Improvement. We turn reasoning into a source of learning, using and to create .
Scientific Discovery. We connect models to the scientific process, from to and guiding .
Foundation Models in the Scientific Loop: Design, Measure, Interpret
AI for Adaptive Experiment Design
Neurosymbolic AI for Safety-Critical Agile Control
Controlling the Structure of Inference and Learning in Neural Networks
End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
Wenda Chu, Bingliang Zhang, Jiaqi Han, Yizhuo Li, Linjie Yang, Yisong Yue, Qiushan Guo
International Conference on Machine Learning (ICML), 2026Spotlight
NitroGen: A Foundation Model for Generalist Gaming Agents
Loïc Magne, Anas Awadalla, Guanzhi Wang, Yinzhen Xu, Joshua Belofsky, Fengyuan Hu, Joohwan Kim, Ludwig Schmidt, Georgia Gkioxari, Jan Kautz, Yisong Yue, Yejin Choi, Yuke Zhu, Linxi "Jim" Fan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026Best Paper Honorable Mention
Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization
Xuefei Wang, Kai A. Horstmann, Ethan Lin, Jonathan Chen, Alexander R. Farhang, Sophia Stiles, Atharva Sehgal, Jonathan Light, David Van Valen, Yisong Yue, Jennifer J. Sun
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026
SpeeDiff: Scalable Pixel-Anchored End-to-End Latent Diffusion Model
Bingliang Zhang, Wenda Chu, Yizhuo Li, Linjie Yang, Yisong Yue, Katherine Bouman, Yang Song, Qiushan Guo
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026
KALIKO: Kalman-Implicit Koopman Operator Learning For Prediction of Nonlinear Dynamical Systems
Albert H Li, Ivan Dario Jimenez Rodriguez, Joel W Burdick, Yisong Yue, Aaron D Ames
2026 IEEE International Conference on Robotics and Automation (ICRA), 2026
CellSAM: a foundation model for cell segmentation
Markus Marks, Uriah Israel, Rohit Dilip, Qilin Li, Changhua Yu, Emily Laubscher, Ahamed Iqbal, Elora Pradhan, Ada Ates, Martin Abt, Caitlin Brown, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Van Valen
Nature Methods, 2025
Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors
Hongkai Zheng, Austin Wang, Zihui Wu, Zhengyu Huang, Ricardo Baptista, Yisong Yue
arXiv preprint arXiv:2510.10968, 2025
Population Transformer: Learning Population-level Representations of Neural Activity
Geeling Chau, Christopher Wang, Sabera Talukder, Vighnesh Subramaniam, Saraswati Soedarmadji, Yisong Yue, Boris Katz, Andrei Barbu
International Conference on Learning Representations (ICLR), 2025Oral
InverseBench: Benchmarking Plug-and-Play Diffusion Models for Inverse Problems in Physical Sciences
Hongkai Zheng, Wenda Chu, Bingliang Zhang, Zihui Wu, Austin Wang, Berthy T. Feng, Caifeng Zou, Yu Sun, Nikola Kovachki, Zachary E. Ross, Katherine L. Bouman, Yisong Yue
International Conference on Learning Representations (ICLR), 2025Spotlight
Active Learning-Assisted Directed Evolution
Jason Yang, Ravi G. Lal, James C. Bowden, Raul Astudillo, Mikhail A. Hameedi, Sukhvinder Kaur, Matthew Hill, Yisong Yue, Frances H. Arnold
Nature Communications, 2025
SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code
Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A. Ross, Cordelia Schmid, Alireza Fathi
International Conference on Machine Learning (ICML), 2024Oral
Neurosymbolic Programming
Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue
Foundations and Trends in Programming Languages, 2021
Multi-dueling Bandits with Dependent Arms
Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
Conference on Uncertainty in Artificial Intelligence (UAI), 2017
The K-armed Dueling Bandits Problem
Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
Conference on Learning Theory (COLT), 2009