About Me

Hello! My name is Gang Liu. I am a fourth-year Ph.D. student in Computer Science and Engineering at the University of Notre Dame, advised by Dr. Meng Jiang. I am glad to be a member of the Data Mining towards Decision Making (DM2) Lab, which is directed by Dr. Meng Jiang.

I received my Bachelor degree in Software Engineering at Southwest University, China, in 2021. When I was an undergraduate, I was luckily advised by Prof. Yong Deng and Prof. Fuyuan Xiao.

For my education and research experience, please refer to my Experience.

Research Interest

• Data & Learning Foundations in Graph ML: I focus on data-centric learning by improving graph data quality with limited supervision [KDD'22], addressing imbalanced learning [KDD'23], and advancing transfer learning [NeurIPS'23].

• Model Foundations in Graph ML: I advance (1) generative models (Graph Diffusion Transformers, NeurIPS'24 Oral); (2) multi-modal molecular representation learning (InfoAlign, ICLR'25 with cellular data); and (3) multi-modal large language models (Llamole, ICLR'25 for molecular design).

• Applications in Scientific Discovery: My research leads to the discovery of new materials [Cell Rep. Phys. Sci.]. I am also interested in developing ready-to-use demos and tools.

I’m always open to collaboration and discussion. If my research aligns with your interests or can contribute to new scientific discoveries, feel free to reach out for a conversation (Contact).

What’s New

Jan 2025	Two papers accepted by ICLR 2025: (1) Learning Molecular Representation in a Cell and (2) Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning.
Jan 2025	I am excited to receive the 2024-2025 IBM PhD Fellowship!
Nov 2024	One paper “Learning Attribute as Explicit Relation for Sequential Recommendation” accepted by KDD 2025.
Sep 2024	“Graph Diffusion Transformer (Graph DiT)” has been accepted by NeurIPS’24 as an Oral. “LLM on Graph Survey” has been accepted by TKDE.
Aug 2024	Just finished two internships at the Broad Institute of MIT and Harvard, and the MIT-IBM Watson AI Lab. Excited to work on multi-modal molecular learning.
Oct 2023	I am excited to receive the NeurIPS 23 travel award. See you in New Orleans!
Sep 2023	One paper “Data-Centric Learning from Unlabeled Graphs with Diffusion Model” accepted by NeurIPS’23.
May 2023	One paper “Semi-Supervised Graph Imbalanced Regression” accepted by KDD’23.
	I am joining the Amazon as an applied scientist intern this summer. See you in Seattle.
Oct 2022	One paper “Network Immunization Strategy by Eliminating Fringe Nodes: A Percolation Perspective” accepted by IEEE Transactions On SMC: Systems.
June 2022	I am excited to receive the KDD 22 Student Travel Award and Notre Dame Conference Presentation Grant to support my travel to KDD’22!
May 2022	One paper “Graph Rationalization with Environment-based Augmentations” accepted by KDD’22.
	One paper “Learning from Counterfactual Links for Link Prediction” accepted by ICML’22.!

Selected Publication (Google Scholar)

[C6]

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning [Paper] [Code] [Model Weights] Gang Liu, Michael Sun, Wojciech Matusik, Meng Jiang, Jie Chen The International Conference on Learning Representations, 2025

[J5]

Superior Polymeric Gas Separation Membrane Designed by Explainable Graph Machine Learning [Paper] [News] [Issue Cover] Jiaxin Xu*, Agboola Suleiman*, Gang Liu* ..., Meng Jiang, Ruilan Guo, Tengfei Luo. Cell Reports Physical Science, 2024

[C4]

Graph Diffusion Transformers for Multi-Conditional Molecular Generation [Paper] [Code] [Online tool] Gang Liu, Jiaxin Xu, Tengfei Luo, Meng Jiang. Conference on Neural Information Processing Systems (Oral), 2024

[C3]

Data-Centric Learning from Unlabeled Graphs with Diffusion Model [Paper] [Code] Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang. Conference on Neural Information Processing Systems, 2023

[C2]

Semi-Supervised Graph Imbalanced Regression [Paper] [Code] [Video] [Blog] [Broader Impacts] Gang Liu, Tong Zhao, Eric Inae, Tengfei Luo, and Meng Jiang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2023

[C1]

Graph Rationalization with Environment-based Augmentations [Paper] [Code] [Broader Impacts] Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2022

Service

• Conference Reviewer: ICLR 2025/2024; AAAI 2025/2024/2023; KDD 2025-Feb/2025-Aug/2024/2023; ICML 2025/2024/2023/2022; NeurIPS 2024/2023/2022; SDM 2024; WebConf 2024.
• Journal Reviewer: IEEE Transactions on Knowledge and Data Engineering. IEEE Transactions on Cybernetics; Digital Signal Processing.

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Last updated: 12/05/2024. Templated adapted from Ankit Sultana and Otilia Stretcu. Thanks for their great work!