Personal Profile

HUANG Keke is a professor of School of Computer Science and Technology, Huazhong University of Science & Technology.

His research is centered on the domains of data management & analysis, and machine learning. His work to date has been dedicated to developing scalable, efficient, and theoretically robust algorithms to enhance both the efficiency and effectiveness of graph analytics and graph learning. He also studies strategies to optimize the ensemble of Large Language Models, focusing on improving their cost-efficiency and overall performance with guarantees. More recently, he has started exploring AI for Science, with a particular interest in data-driven and learning-based methods for scientific discovery.

Academic Degrees

2015.8-2019.8

Ph.D. in Nanyang Technological University, Singapore.

2011.9-2015.6

B.Sc. in Huazhong University of Science & Technology, China.

Professional Experience

2026.3-now  

Professor of Computer Science, Huazhong University of Science and Technology, China

2024.9-2025.9

Postdoctoral Researcher, University of British Columbia, Canada

2020.12-2024.9

Research Fellow, National University of Singapore, Singapore

Courses Taught

Awards and Honors

Selected Projects Funded

Selected Publications

[1] An Effective Method for Heterophilic GCL Methods With Regularization and Stabilization Techniques Enhanced High-Pass Filter. Han Chen, Yuhua Li, Yixiong Zou, Keke Huang, Rui Zhang, Ruixuan Li. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2026.

[2] ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries. Keke Huang, Yimin Shi, Dujian Ding, Yifei Li, Yang Fei, Laks Lakshmanan, and Xiaokui Xiao. Proceedings of the VLDB Endowment (PVLDB), 2025.

[3] How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing. Keke Huang, Yu Guang Wang, Ming Li, and Pietro Liò. Proceedings of the International Conference on Machine Learning (ICML), 2024.

[4] Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation. Keke Huang, Ruize Gao, Bogdan Cautis, and Xiaokui Xiao. Proceedings of the ACM Web Conference (TheWebConf), 2024. [Oral Presentation]

[5] Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach. Keke Huang, Wencai Cao, Hoang Ta, Xiaokui Xiao, and Pietro Liò. Proceedings of the ACM Web Conference (TheWebConf), 2024.

[6] Node-wise Diffusion for Scalable Graph Learning. Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, and Xiaokui Xiao. Proceedings of the ACM Web Conference (TheWebConf), 2023.

[7] Efficient and Effective Edge-wise Graph Representation Learning. Hewen Wang, Renchi Yang, Keke Huang, and Xiaokui Xiao. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023.

[8] Scalable and Effective Bipartite Network Embedding. Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao. Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.

[9] Optimal Streaming Algorithms for Multi-Armed Bandits. Tianyuan Jin, Keke Huang, Jing Tang, and Xiaokui Xiao. Proceedings of the International Conference on Machine Learning (ICML), 2021.

[10] Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, and Quanquan Gu. Proceedings of the International Conference on Machine Learning (ICML), 2021.

[11] Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization. Tianyuan Jin, Yu Yang, Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao. Proceedings of the VLDB Endowment (PVLDB), 2021.

[12] Effective and Scalable Clustering on Massive Attributed Graphs. Renchi Yang, jieming Shi, Yin Yang, Keke Huang, Shiqi Zhang, Xiaokui Xiao. Proceedings of The Web Conference (TheWebConf) 2021.

[13] Efficient Approximation Algorithms for Adaptive Influence Maximization. Keke Huang*, Jing Tang*, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, and Andrew Lim. The International Journal on Very Large Data Bases (VLDBJ), 2020.

[14] Efficient Approximation Algorithms for Adaptive Target Profit Maximization. Keke Huang, Jing Tang, Xiaokui Xiao, Aixin Sun, and Andrew Lim. Proceedings of the IEEE International Conference on Data Engineering (ICDE), 2020.

[15] Best Bang for the Buck: Cost-Effective Seed Selection for Online Social Networks. Kai Han, Yuntian He, Keke Huang, Xiaokui Xiao, Shaojie Tang, Jingxin Xu, Liusheng Huang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.

[16] Efficient Approximation Algorithms for Adaptive Seed Minimization. Jing Tang*, Keke Huang*, Xiaokui Xiao, Laks V.S. Lakshmanan, Xueyan Tang, Aixin Sun, and Andrew Lim. Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2019.

[17] Efficient Algorithms for Adaptive Influence Maximization. Kai Han*, Keke Huang*, Xiaokui Xiao*, Jing Tang, Aixin Sun, Xueyan Tang. Proceedings of the VLDB Endowment (PVLDB), 11(9):1029-1040, 2018.

[18] Revisiting the Stop-and-Stare Algorithms for Influence Maximization. Keke Huang, Sibo Wang, Glenn Bevilacqua, Xiaokui Xiao, and Laks V.S. Lakshmanan. Proceedings of the VLDB Endowment (PVLDB), 10(9):913-924, 2017.

Professional Affiliations

Research group

Enrollment Information

Personal Homepage

Web：http://faculty.hust.edu.cn/huangkeke/zh_CN/index.htm