Yuejiang LIU

I am Yuejiang Liu, a postdoc fellow at Stanford, advised by Chelsea Finn. I previously obtained my PhD degree at EPFL, advised by Alexandre Alahi, and interned with Francesco Locatello, Chris Russell, and Bernhard Schölkopf.

I enjoy designing and prototyping algorithms that enable deep neural networks to generalize or adapt to the unexpected, with applications in robotics. My research spans:

representation learning: incorporating structured or causal priors for robust interactions with environments
data curation: synthesizing, selecting, and mining quality and informative examples from online experience
test-time adaptation: designing objectives and mechanisms for dynamic model adaptation during inference

My long-term goal is to create embodied intelligent agents that can step out of lab demos and thrive in our ever-evolving world. I believe achieving this requires breakthroughs in the science of scaling, especially in high-quality data acquisition and test-time compute. If our research interests intersect, please don’t hesitate to get in touch!

(For pronunciation: my first name /yweh-jyang/, but any close approximation is welcome)

Publications

Learning Long-Context Diffusion Policies via Past-Token Prediction.
M Villasevil*, A Tang*, Y Liu*, C Finn
Conference on Robot Learning (CoRL). Best paper at RSS RoboReps, 2025. [pdf, code]
Demo SCORE: Curating Demonstrations using Online Experience.
A Chen, A Lessing, Y Liu, C Finn
Robotics: Science and Systems (RSS), 2025. [pdf, code]
Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling.
Y Liu*, J Hamid*, A Xie, Y Lee, M Du, C Finn
International Conference on Learning Representations (ICLR), 2025. [pdf, code]
Targeted Data Selection via Optimal Transport.
L Feng, F Nie, Y Liu*, A Alahi*.
International Conference on Machine Learning (ICML), 2025. [pdf, code]
Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations.
A Rahimi*, P Luan*, Y Liu*, F Rajic, A Alahi.
Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [pdf, code]
Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts.
Y Liu, A Alahi.
Preprint. 2024. [pdf, code]
On Pitfalls of Test-time Adaptation.
H Zhao*, Y Liu*, A Alahi, T Lin.
International Conference on Machine Learning (ICML), 2023. [pdf, code]
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning.
Y Liu, A Alahi, C Russell, M Horn, D Zietlow, B Schölkopf, F Locatello.
Conference on Causal Learning and Reasoning (CLeaR), 2023. [pdf, code]
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective.
Y Liu, R Cadei, J Schweizer, S Bahmani, A Alahi.
Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [pdf, code]
Modular Low-Rank Style Transfer for Deep Motion Forecasting.
P. Kothar*, D. Li*, Y Liu, A Alahi.
Conference on Robot Learning (CoRL), 2022. [pdf, code]
TTT++: When Does Self-supervised Test-time Training Fail or Thrive?
Y Liu, P Kothari, B Delft, B Bellot-Gurlet, T Mordan, A Alahi.
Conference on Neural Information Processing Systems (NeurIPS), 2021. [pdf, code]
Social NCE: Contrastive Learning of Socially-aware Motion Representations.
Y Liu, Q Yan, A Alahi.
International Conference on Computer Vision (ICCV), 2021. [pdf, code]
Collaborative Sampling from Generative Adversarial Networks.
Y Liu*, P Kothari*, A Alahi.
Conference on Artificial Intelligence (AAAI), 2020. [pdf, code]
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning.
C Chen, Y Liu, S Kreiss, A Alahi.
International Conference on Robotics and Automation (ICRA), 2019. [pdf, code]
Map-based Deep Imitation Learning for Obstacle Avoidance.
Y Liu, A Xu, Z Chen.
International Conference on Intelligent Robots and Systems (IROS), 2018. [pdf]
Real-Time Distributed Algorithms for Nonconvex Optimal Power Flow.
Y Liu, JH Hours, G Stathopoulos, CN Jones.
American Control Conference (ACC), 2017. [pdf]

Honors

RSS RoboReps Best Paper, 2025
SNSF Postdoc Fellowship, 2024
Thesis Award Nominations, 2023
National Doctorate Award, 2022
1st Place, BoltzGo, ICML, 2021
1st Place, TrajNet++, ICCV, 2021

Service

Teaching Assistant: Deep Learning for Autonomous Vehicles (Guest Lecture on Self-supervised Learning)
Review Service: NeurIPS, ICLR, ICML, CVPR, ICCV, RSS, CoRL, ICRA, IROS, JMLR, PAMI, IJCV, RAL

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