Liang Wang (王亮)
Hi! I am Liang Wang, a fourth-year PhD student at Institute of Automation, Chinese Academy of Sciences (CASIA). I am a member of State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), advised by Prof. Liang Wang, and co-advised by Prof. Shu Wu and Prof. Qiang Liu.
Meanwhile, I am also a visiting PhD student at National University of Singapore (NUS), a member of ZhangLab, advised by Prof. Yang Zhang and mentored by Dr. Zhiyuan Liu.
In addition, I am a research intern at DP Technology. Previously, I was fortunate to be a research intern at Alibaba DAMO Academy, advised by Dr. Yu Rong and Dr. Tingyang Xu. I also had internship experiences at Bytedance and Meituan. I received a B.E. in Software Engineering from Tongji University.
📍 I am actively seeking industrial research opportunities or a postdoc position! Feel free to reach out if you are interested in my work or would like to get in touch with me.
Google Scholar /
Github /
CV[PDF]
Email: liang.wang[at]cripac.ia.ac.cn / liang.wang[at]u.nus.edu
Research Interests
Currently, I am focusing on the research of AI for Science, Diffusion Models (for scientific design), and LLMs (for scientific reasoning). Previously, I also worked on Graph Machine Learning and Data Mining.
AI for Science Diffusion Models LLM Reasoning Graph Machine Learning Data Mining
News
[May 26, 2025] Our survey paper "Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey" is released! [Paper] [Awesome Paper List] [Apr 17, 2025] I will attend ICLR 2025 from Apr 24 to Apr 28. Looking forward to seeing you in Singapore! [Feb 14, 2025] Our survey paper "Diffusion Models for Molecules: A Survey of Methods and Tasks" is released! [Paper] [Awesome Paper List] [Jan 23, 2025] Our paper on molecular representation pre-training (MolSpectra) is accepted by ICLR 2025. Thanks to all collaborators! [Dec 10, 2024] Our paper on knowledge graph completion (S2DN) is accepted by AAAI 2025 as oral presentation. Congratulations to Tengfei! [Dec 9, 2024] I will attend NeurIPS 2024 from Dec 10 to Dec 15. Looking forward to seeing you in Vancouver@Canada! [Nov 1, 2024] Invited to give a talk on molecular representation learning and property prediction at DP Technology. [Slides] [Sep 26, 2024] Our paper on molecular property prediction (Pin-Tuning) is accepted by NeurIPS 2024. Thanks to all collaborators!
Selected Publications
(Full Publication List)AI for Science
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Liang Wang, Shaozhen Liu, Yu Rong, Deli Zhao, Qiang Liu, Shu Wu, Liang Wang
ICLR 2025
[Paper] [Code] [Poster] [Slides] [Blog (Chinese)] Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
Liang Wang, Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang
NeurIPS 2024
[Paper] [Code] [Poster]
Graph Machine Learning
Rethinking Graph Masked Autoencoders through Alignment and Uniformity
Liang Wang*, Xiang Tao*, Qiang Liu, Shu Wu, Liang Wang
AAAI 2024
[Paper] [Code] [Poster] DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
Xin Sun, Liang Wang, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang
KDD 2024
[Paper] GSLB: The Graph Structure Learning Benchmark
Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu
NeurIPS 2023
[Paper] [Code]
Data Mining
S2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Tengfei Ma, Yujie Chen, Liang Wang, Xuan Lin, Bosheng Song, Xiangxiang Zeng
AAAI 2025 (Oral)
[Paper] Bi-Level Graph Structure Learning for Next POI Recommendation
Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang
IEEE Transactions on Knowledge and Data Engineering 2024
[Paper] [Code] Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
WWW 2024
[Paper]
Preprints
Diffusion Models for Molecules: A Survey of Methods and Tasks
Liang Wang, Chao Song, Zhiyuan Liu, Yu Rong, Qiang Liu, Shu Wu, Liang Wang
arXiv preprint 2025
[Paper] [Awesome Paper List] Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
Zhixun Li*, Bin Cao*, Rui Jiao*, Liang Wang*, Ding Wang, Yang Liu, Dingshuo Chen, Jia Li, Qiang Liu, Yu Rong, Liang Wang, Tong-yi Zhang, Jeffrey Xu Yu
arXiv preprint 2025
[Paper] [Awesome Paper List] A Survey of Graph Transformers: Architectures, Theories and Applications
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, Liang Wang, Tingyang Xu, Wenbing Huang, Deli Zhao, Hong Cheng, Yu Rong
arXiv preprint 2025
[Paper]
Projects
PyGCL: An easy-to-use library for graph contrastive learning with PyTorch. It implements a wide variety of contrastive objectives, data augmentations, contrasting modes and other utilities useful for implementing and evaluating contrastive learning on graphs.
GSLB: An open-source library built for easy implementation and evaluation of graph structure learning model family. It offers a versatile control of graph dataset loading, structure learners, structure processors, and a bunch of reproduced models.
Services
Conference Reviewer
Conference on Neural Information Processing Systems (NeurIPS), 2024, 2025 International Conference on Learning Representations (ICLR), 2025 International Conference on Machine Learning (ICML), 2025 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024, 2025 International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Journal Reviewer
IEEE Transactions on Knowledge and Data Engineering (TKDE) IEEE Transactions on Computational Biology and Bioinformatics (TCBB)
Education
Visiting PhD
PhD (selected for the PhD honors program)
Bachelor
