Laboratory for Deep Structured Learning
The University of British Columbia, Department of Electrical and Computer Engineering
Welcome to the Renjie Liao’s lab for Deep Structure Learning (DSL) at UBC!
Our long-term research goal is to build superintelligent AI agents. We pursue this from both theoretical and practical perspectives, aiming to develop foundations (e.g., long-term memory mechanisms) that could lead to superintelligence.
Our recent research covers:
- Deep Generative Models
- Geometric Deep Learning
- Math/Algorithmic Reasoning with Transformers (e.g., LLMs)
- Probabilistic Inference
- Visual Understanding and Reasoning
- Motion Prediction and Planning in Self-driving
- AI for Healthcare
news
| Sep 18, 2025 | Our works Neural MJD: Neural Non-Stationary Merton Jump Diffusion for Time Series Prediction and RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis were accepted in Annual Conference on Neural Information Processing Systems (NeurIPS) 2025. Congratulations to Yuanpei, Qi, and Robin! |
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| Aug 4, 2025 | Our work An Information-Theoretic Framework for Out-of-Distribution Generalization with Applications to Stochastic Gradient Langevin Dynamics was accepted for publication in the IEEE Transactions on Information Theory (TIT). Congratulations to Wenliang! |
| Jul 21, 2025 | Our work Learning Latent Part-Whole Hierarchies for Point Clouds was accepted for publication in the International Journal of Computer Vision (IJCV). Congratulations to Xiang! |
| Jun 15, 2025 | Our work TrajFlow: Multi-modal Motion Prediction via Flow Matching was accepted in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) as an Oral Presentation. Congratulations to Qi, Brian, Yutong, Hsueh-Han, and Joshua! |
| May 1, 2025 | Our work Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation was accepted for publication in International Conference on Machine Learning (ICML) 2025. Congratulations to Sadegh, Muchen, and Kevin! |
| Feb 26, 2025 | Our works MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation and LatentHOI: On the Generalizable Hand Object Motion Generation with Latent Hand Diffusion were accepted in IEEE/CVF Conference on Computer Vision and Pattern Recognition Conference (CVPR) 2025. Congratulations to Felix, Qi, and Muchen! |
| Feb 11, 2025 | Our work SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups was accepted in International Conference on Learning Representations (ICLR) 2025 as an Oral Presentation (top 1.8%). Congratulations to Nick and Donglin! |