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.

Sabbatical at Argo AI: I am currently on sabbatical at Argo AI, where I am working on machine learning approaches to forecasting and motion planning for autonomous vehicles. I am also helping set up a new engineering office in Pasadena, CA. Learn more about my new role here.
Diversity, Equity & Inclusion
I am committed to promoting diversity, equity, and inclusion in my research group, in my courses, within the CMS department, at Caltech more broadly, and within my research communities.
  • Diversity -- I recognize that diversity, in all its shapes and forms, strengthens us both culturally and intellectually.
  • Equity -- I will fight for equal treatment of all people, regardless of race, gender, sexual orientation, or any other attributes that do not define a person's academic and research potential.
  • Inclusion -- I will work to create an inclusive working environment, so that everyone feels their voices are heard and their contributions are recognized.
Read more about diversity, equity, and inclusion at the CMS Department and EAS Division at Caltech.
Current Research
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
  • Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
    Ivan Dario Jimenez Rodriguez*, Noel Csomay-Shanklin*, Yisong Yue, Aaron D. Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2022.
  • 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
  • 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
  • Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
    Sabera Talukder, Jennifer J. Sun, Matthew Leonard, Bingni W. Brunton, Yisong Yue
  • 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.
  • DeepGEM: Generalized Expectation-Maximization for Blind Inversion
    Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman
    Neural Information Processing Systems (NeurIPS), December 2021.
  • End-to-End Sequential Sampling and Reconstruction for MR Imaging
    Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman
    Machine Learning for Health (ML4H), December 2021.
    (Best Paper Award)
  • 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)

Core AI/ML Research: study the underlying fundamental questions pertaining practical algorithm design, inspired by real-world applications in science and engineering.
Selected Publications
  • 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.
  • Investigating Generalization by Controlling Normalized Margin
    Alexander Farhang, Jeremy Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue
    International Conference on Machine Learning (ICML), July 2022.
  • 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.
  • Online Robust Control of Nonlinear Systems with Large Uncertainty
    Dimitar Ho, Hoang M. Le, John Doyle, Yisong Yue
    International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
  • Learning Differentiable Programs with Admissible Neural Heuristics
    Ameesh Shah*, Eric Zhan*, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri
    Neural Information Processing Systems (NeurIPS), December 2020.
  • Batch Policy Learning under Constraints
    Hoang M. Le, Cameron Voloshin, Yisong Yue
    International Conference on Machine Learning (ICML), June 2019.
    (Oral Presentation)
  • Multi-dueling Bandits with Dependent Arms
    Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
    Conference on Uncertainty in Artificial Intelligence (UAI), August 2017.
News & Announcements
  • Group Assimilation Exercise: I wrote a post about the Group Assimilation Exercise that my group does annually. The goal of the exercise is the lower the barriers to the group for providing me with constructive feedback.
  • Neural-Fly published in Science Robotics: our work on Neural-Fly has been published in Science Robotics. [article][press release]
  • Neurosymbolic Programming Summer School (July 11-13 2022 @Caltech): Neurosymbolic programming is an exciting new area at the intersection of Program Synthesis and Machine Learning that aims to learn models that incorporate program-like structure. More info here.
  • Sabbatical at Argo AI: I have started my role as Principal Scientist at Argo AI. Learn more about my new role here.
  • Neurosymbolic Programming Survey Paper: published in Foundations and Trends in Programming Languages. (preprint)
  • Best Paper Award: "End-to-End Sequential Sampling and Reconstruction for MR Imaging" won Best Paper at ML4Health 2021! [project]
  • Invited Talk: I presented on Neurosymbolic Programming at the Caltech Explainable AI Virtual Workshop. [slides]
  • Advances in Machine Learning-Assisted Directed Evolution for Protein Design: new paper showing that smart training set selection can significantly improve machine learning-guided directed evolution in highly epistatic protein fitness landscapes with large low-fitness regions. [online][bioRxiv][code]
  • Personalized Preference Learning from Spinal Cord Stimulation to Exoskeletons: interactive learning approaches for personalizing medical assistive devices. [slides]
  • Fine-Grained Identification of Biological Neural Networks: our KDD 2021 paper on "Fine-Grained System Identification of Nonlinear Neural Circuits" demonstrated identifying sparse nonlinear neural networks from real neural recording data. [arxiv][code]
  • Multi-Agent Behavior Workshop: Video recordings from our Multi-Agent Behavior Workshop (co-located with CVPR 2021) are now available. [playlist]
  • Bon Voyage! We have four departures in the 2020-2021 academic year:
    • Ellen Novoseller completed her PhD and has started as a postdoc in Ken Goldberg's group at UC Berkeley.
    • Angie Liu completed her postdoc and will start as an assistant professor at Johns Hopkins University.
    • Joe Marino completed his PhD and will start as a research scientist at DeepMind.
    • Jialin Song completed his PhD and has started as a research scientist at NVIDIA AI.
  • Paper Award: Our work on Task Programming won Best Student Paper at CVPR 2021! [arxiv][code][project]
  • AI for Science Challenge: We have released the Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. [arxiv][dataset][code]
  • Invited Talk: Video now available of my talk on "Learning to Optimize as Policy Learning" presented at the Princeton Optimization Seminar. [slides]
  • Invited Talk: I presented on "Learning for Safety-Critical Control in Dynamical Systems" at the Control Meets Learning seminar series.
  • Invited Talk: I gave the Earnest C. Watson Lecture on January 13th, 2021.
[All Content © 2022 Yisong Yue]