Ph.D. in Computer Science, University of Montreal, 2024

M.S. in Computer Science, Georgia Institute of Technology, 2019

B.S. in Computer Science, Georgia Institute of Technology, 2017

B.S. in Electrical Engineering, Georgia Institute of Technology, 2017

Zhen Liu is an assistant professor at the School of Data Science, Chinese University of Hong Kong, Shenzhen. He received his PhD degree in computer science from Mila and University of Montreal. Between 2022 and 2024, he was a visiting student at Max Planck Institute for Intelligent Systems. He obtained his bachelor’s and master’s degrees in computer science from Georgia Institute of Technology.

Zhen Liu’s research focuses on principled and novel machine learning methods for 1) 3D modeling and generation and 2) vision/language foundation models. He is also widely interested in representation learning and probabilistic methods. He published papers at top-tier conferences such as NeurIPS, ICML, ICLR, CVPR, ECCV and SIGGRAPH Asia, of which some were selected for oral/spotlight presentations.

(^# for corresponding author; ^*, ^** for equal contribution):

Fast Diversity-Preserving Reward Finetuning of Diffusion Models via Nabla-GFlowNets. Zhen Liu^#, Tim Z. Xiao^*, Weiyang Liu^*, Yoshua Bengio, Dinghuai Zhang^#. arXiv, 2024.

Can Large Language Models Understand Symbolic Graphics Programs? Zeju Qiu^*, Weiyang Liu^*, Haiwen Feng^*, Zhen Liu^**, Tim Z. Xiao^**, Katherine M. Collins^**, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf. arXiv, 2024.

PuzzleAvatar: Assembling 3D Avatars from Personal Albums. Yuliang Xiu, Yufei Ye, Zhen Liu, Dimitrios Tzionas, Michael J. Black. ACM Transactions on Graphics (TOG / SIGGRAPH Asia), 2024.

Ghost on the Shell: An Expressive Representation of General 3D Shapes. Zhen Liu, Yao Feng^*, Yuliang Xiu^*, Weiyang Liu^*, Liam Paull, Michael J. Black, Bernhard Schölkopf. ICLR 2024.

Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. Weiyang Liu^*, Zeju Qiu*, Yao Feng^**, Yuliang Xiu^**, Yuxuan Xue^**, Longhui Yu^**, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf. ICLR 2024.

Controlling Text-to-Image Diffusion by Orthogonal Finetuning. Zeju Qiu^*, Weiyang Liu^*, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf. NeurIPS 2023.

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods. Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron Courville, Alessandro Sordoni. TMLR 2023.

MeshDiffusion: Score-based Generative 3D Mesh Modeling. Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu. ICLR 2023. Notable Top-25%.

Iterative Teaching by Data Hallucination. Zeju Qiu^*, Weiyang Liu^*, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf. AISTATS 2023.

Continual Learning by Modeling Intra-Class Variation. Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu. TMLR 2023.

Structural Causal 3D Reconstruction. Weiyang Liu^*, Zhen Liu^*, Liam Paull, Adrian Weller, Bernhard Schölkopf. ECCV 2022.

Generative Flow Networks for Discrete Probabilistic Modeling. Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio. ICML 2022.

Iterative Teaching by Label Synthesis. Weiyang Liu^*, Zhen Liu^*, Hanchen Wang^*, Liam Paull, Bernhard Schölkopf, Adrian Weller. NeurIPS 2021. Spotlight Presentation.

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. Mingde Zhao^*, Zhen Liu^*, Sitao Luan^*, Shuyuan Zhang^*, Doina Precup, Yoshua Bengio. NeurIPS 2021

Orthogonal Over-parametrized Training. Weiyang Liu^*, Rongmei Lin^*, Zhen Liu, James M Rehg, Liam Paull, Li Xiong, Adrian Weller, Le Song. CVPR 2021. Oral Presentation.

Learning with Hyperspherical Uniformity. Weiyang Liu^*, Rongmei Lin^*, Zhen Liu^*, Li Xiong, Bernhard Schölkopf, Adrian Weller. AISTATS 2021.

Neural similarity learning. Weiyang Liu^*, Zhen Liu^*, James M Rehg, Le Song. NeurIPS 2019.

Exponential Family Estimation via Adversarial Dynamics Embedding. Bo Dai^*, Zhen Liu^*, Hanjun Dai^*, Niao He, Arthur Gretton, Le Song, Dale Schuurmans. NeurIPS 2019.

Coupled Variational Bayes via Optimization Embedding. Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song. NeurIPS 2018.

Learning towards Minimum Hyperspherical Energy. Weiyang Liu^*, Rongmei Lin^*, Zhen Liu^*, Lixin Liu^*, Zhiding Yu, Bo Dai, Le Song. NeurIPS 2018.

Decoupled Networks. Weiyang Liu^*, Zhen Liu^*, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James Rehg, Le Song. CVPR 2018. Spotlight Presentation.

Towards Black-box Iterative Machine Teaching. Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg, Le Song. ICML 2018.

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song. ICML 2018.

Deep Forward and Inverse Perceptual Models for Tracking and Prediction. Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots. ICRA 2018.

One Shot Learning for Semantic Segmentation. Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots. BMVC 2017.