Yisong Yue (he/him)
California Institute of Technology
1200 E. California Blvd.
CMS, 305-16
Pasadena, CA 91125

Office: 303 Annenberg

Contact Information >

I am a professor of Computing and Mathematical Sciences at the California Institute of Technology. My research interests lie primarily in machine learning, and span the entire theory-to-application spectrum from foundational advances all the way to deployment in real systems. I work closely with domain experts to understand the frontier challenges in applied machine learning, distill those challenges into mathematically precise formulations, and develop novel methods to tackle them.

Asari AI: I am a founding advisor for Asari AI, where I help design AI co-inventors (AI agents that can plan, abstract, and verify complex design tasks).

Latitude AI: I am currently a (part-time) Principal Scientist at Latitude AI, where I work on machine learning approaches to behavior modeling and motion planning for autonomous driving.

ICLR 2025: I am serving as the General Chair at ICLR 2025. The Program Chairs are Carl Vondrick (SPC), Rose Yu, Violet Peng, Fei Sha, Animesh Garg.

We are conducting a broad faculty search, and are interested in all AI and AI-adjacent fields. Please apply here.

My current research interests can be broadly organized into three overlapping groups:

AI for Autonomy: study how AI methods can enable novel capabilities in autonomous systems; characterize and address key technical bottlenecks (e.g., data-driven safety guarantees); deploy in real systems.

Selected Publications

• Robust Agility via Learned Zero Dynamics Policies
  Noel Csomay-Shanklin, William D. Compton, Ivan Dario Jimenez Rodriguez, Eric R. Ambrose, Yisong Yue, Aaron D. Ames
  International Conference on Intelligent Robots and Systems (IROS), 2024
  [arxiv]
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
  Michael O’Connell*, Guanya Shi*, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
  Science Robotics, May 2022.
  [arxiv][online][code][video][press release]
• MLNav: Learning to Safely Navigate on Martian Terrains
  Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono
  IEEE Robotics and Automation Letters (RA-L), May 2022
  [conference][journal][video]
• GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
  Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
  IEEE Robotics and Automation Letters (RA-L), June 2020.
  (Best Paper Nomination)
  [pdf][arxiv][demo video]
• Preference-Based Learning for Exoskeleton Gait Optimization
  Maegan Tucker*, Ellen Novoseller*, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames
  International Conference on Robotics and Automation (ICRA), May 2020.
  (Best Paper Award)
  [pdf][arxiv][demo video][project]

AI for Science: study how AI methods can improve workflows in science and accelerate knowledge discovery; develop methods for automated experiment design and human-intelligible modeling; deploy in real systems.

Selected Publications

• 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
  [arxiv]
• Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
  Zihui Wu, Yu Sun, Yifan Chen, Bingliang Zhang, Yisong Yue, Katherine L. Bouman
  Neural Information Processing Systems (NeurIPS), 2024
  [arxiv]
• 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
  [bioRxiv]
• A Foundation Model for Cell Segmentation
  Uriah Israel, Markus Marks, Rohit Dilip, Qilin Li, Morgan Sarah Schwartz, Elora Pradhan, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Ashley Van Valen
  [bioRxiv][service]
• Self-Supervised Keypoint Discovery in Behavioral Videos
  Jennifer J. Sun*, Serim Ryou*, Roni Goldshmid, Brandon Weissbourd, John Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, Pietro Perona
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
  [arxiv]
• Task Programming: Learning Data Efficient Behavior Representations
  Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.
  (Best Student Paper Award)
  [arxiv][code][project]

Core AI/ML Research: study the underlying fundamental questions pertaining practical algorithm design, inspired by real-world applications in science and engineering.

Selected Publications

• Find Any Part in 3D
  Ziqi Ma, Yisong Yue, Georgia Gkioxari
  [project][arxiv][demo][code]
• Practical Bayesian Algorithm Execution via Posterior Sampling
  Chu Xin Cheng, Raul Astudillo, Thomas Desautels, Yisong Yue
  Neural Information Processing Systems (NeurIPS), 2024
  [arxiv]
• Online Policy Optimization in Unknown Nonlinear Systems
  Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, Adam Wierman
  Conference on Learning Theory (COLT), 2024
  [arxiv]
• TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
  Sabera Talukder, Yisong Yue, Georgia Gkioxari
  Transactions of Machine Learning Research (TMLR), 2024
  [arxiv][code]
• Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
  Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue
  International Conference on Machine Learning (ICML), 2024
  (Oral Presentation)
  [arxiv][website]
• Automatic Gradient Descent: Deep Learning without Hyperparameters
  Jeremy Bernstein, Chris Mingard, Kevin Huang, Navid Azizan, Yisong Yue
  [arxiv][code][blog post]
• LyaNet: A Lyapunov Framework for Training Neural ODEs
  Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue
  International Conference on Machine Learning (ICML), July 2022.
  [arxiv][code]
• Neurosymbolic Programming
  Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue
  Foundations and Trends in Programming Languages, Volume 7: No. 3, pages 158-243, December 2021.
  [preprint][online]
• Batch Policy Learning under Constraints
  Hoang M. Le, Cameron Voloshin, Yisong Yue
  International Conference on Machine Learning (ICML), June 2019.
  (Oral Presentation)
  [pdf][arxiv][project]

• Find3D -- we developed an approach for training foundation model for 3D part-level understanding. The main contribution is a data engine that can generate large amounts of 3D training data using existing 2D foundation models. [project]
  
• Self-Training for LLM-based Tree-Search -- we developed a self-training approach for process-reward learning to empower LLM-based Tree-Search. [arxiv]
  
• Plug-and-Play Bayesian Inversion via Diffusion Models & Physics -- we developed a principled plug-play inverse imaging approach that blends diffusion model prior and physics-bassed forward models. [arxiv]
  
• Humanlike Bot Behavior -- we developed an approach for generating humanlike bot behavior from demonstrations, demoed in a gym environment within the Fortnite game engine. [project][paper]
  
• Farewell! We had five members depart the group during the 2023-2024 Academic Year:
  • Lu Gan completed her postdoc and has started as a faculty at Georgia Tech.
  • Ziniu Hu completed his postdoc and has started at xAI.
  • Yujia Huang completed her Ph.D. and has started at Citadel Securities.
  • James Preiss completed his postdoc and has started as a faculty at UC Santa Barbara.
  • Kaiyu Yang completed his postdoc and has started at Meta AI.
  
• SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code -- we developed a Large Language Model (LLM) Agent for converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets. This process requires complex spatial planning and arrangemet, which we tackle through a combination of advanced abstraction, strategic planning, and library learning. [arxiv]
  
• Tokenized Time Series Embeddings -- we developed a framework for learning a tokenization for downstream time series modeling, including training generalist models that can be applied to many domains. [arxiv][code]
  
• Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion -- we developed a method for guided diffusion using non-differentiable rules, called Stochastic Control Guidance. Our approach is inspired by path integral control and can be applied in a plug-and-play way to any diffusion model. We demonstrate our approach on symbolic music generation. [arxiv][website]
  
• SustainGym: we have released a suite of environments designed to test the performance of RL algorithms on realistic sustainability tasks. [project][code]
• Farewell! We had three members depart the group during the 2022-2023 Academic Year:
  • Jennifer Sun completed her Ph.D. and will start as an Assistant Professor at Cornell.
  • Cameron Voloshin completed his Ph.D. and has started at Latitude AI.
  • Victor Dorobantu completed his Ph.D. and has started a postdoc at MIT.
  
• ICLR 2024: I will be serving as the Senior Program Chair at ICLR 2024. The rest of Program Chair team includes Swarat Chaudhuri, Katerina Fragkiadaki, Emtiyaz Khan, and Yizhou Sun.
  
• Automatic Gradient Descent: We have developed a new hyperparameter-free optimizer for deep neural networks. Our method has been demonstrated at ImageNet scale using ResNet50 architectures.
  [arxiv][code][blog post]
• Conformal Generative Modeling: We have developed a new framework for generative modeling that works on a wide range of manifolds. [project]