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.

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.

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

• Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
  Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
  International Conference on Intelligent Robots and Systems (IROS), September 2021.
  [arxiv][demo video]
• Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions
  Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue, Soon-Jo Chung
  IEEE Transactions on Robotics (T-RO), 2021.
  [arxiv][video][press release]
• 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

• DeepGEM: Generalized Expectation-Maximization for Blind Inversion
  Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman
  Neural Information Processing Systems (NeurIPS), December 2021.
  [pdf][code][project]
• 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)
  [arxiv][project]
• Fine-Grained System Identification of Nonlinear Neural Circuits
  Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister
  ACM Conference on Knowledge Discovery and Data Mining (KDD), August 2021.
  [pdf][arxiv][code]
• 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 Algorithms & Theory: study the underlying theoretical questions pertaining practical algorithm design, inspired by real-world applications in science and engineering.

Selected Publications

• Meta-Adaptive Nonlinear Control: Theory and Algorithms
  Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2021.
  [arxiv][code]
• 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.
  [pdf][arxiv]
• 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.
  [arxiv][code]
• On the distance between two neural networks and the stability of learning
  Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu
  Neural Information Processing Systems (NeurIPS), December 2020.
  [arxiv][code]
• Imitation-Projected Programmatic Reinforcement Learning
  Abhinav Verma*, Hoang M. Le*, Yisong Yue, Swarat Chaudhuri
  Neural Information Processing Systems (NeurIPS), December 2019.
  [pdf][arxiv][code][demo video]
• 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]
• Multi-dueling Bandits with Dependent Arms
  Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
  Conference on Uncertainty in Artificial Intelligence (UAI), August 2017.
  [pdf][arxiv]

• Neurosymbolic Programming Survey Paper: published in Foundations and Trends in Programming Languages. (Non-paywalled version will be widely available in June 2022 -- I may be able to accommodate individual requests in the meantime.)
• 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.
  
• Invited Talk: I gave a presentation on AI for Adaptive Experiment Design at the Directions in ML: AutoML and Automating Algorithms hosted by Microsoft Research. [slides]
  
• Neural-Swarm2: we have released the details of our heterogeneous neural swarm approach! [paper]
  
• Invited Talk: I am presenting on "Competitive Algorithms for Online Control" at the Simons Institute Workshop on Mathematics of Online Decision Making. [slides]
  
• Invited Talk: I am presenting at the Workshop on Imitation Learning: Single & Multi-Agent hosted by DAI 2020. [slides]
• Invited Talk: I am presenting on "Learning to Optimize as Policy Learning" at the Princeton Optimization Seminar. [slides]
• ML for Rover Path Planning: check out this video of our MLNav extension of ENav (the path planner currently on the Mars Perseverance Rover). [arxiv]
  
• NSF Expeditions on Program Learning: we are starting a new research initiative titled Understanding the World Through Code, funded through the NSF Expeditions in Computing Program.