Keke Huang (黄柯柯)
Keke Huang (黄柯柯)
Postdoctoral Researcher
Data Science Institute, Department of Computer Science
The University of British Columbia
DMM Lab, 2366 Main Mall, Vancouver, BC, Canada V6T 1Z4
Email: hkk992@gmail.com kk.huang@ubc.ca
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Short Bio (I am on the faculty job market.)
Short Bio (I am on the faculty job market.)
I am currently a Postdoctoral Researcher in the Department of Computer Science at the University of British Columbia (UBC), working with Prof. Laks Lakshmanan . I received my Ph.D. degree from Nanyang Technological University (NTU), under the supervision of Prof. Xiaokui Xiao and Prof. Aixin Sun. Before that, I got my B.Eng. Degree from Huazhong University of Science and Technology (HUST). I am a long-term visiting scholar of the AI Group of Prof. Pietro Liò at University of Cambridge.
Research Interests
Research Interests
My research is centered on the domains of data management & analysis, and graph learning. My 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. In graph learning, I have a particular interest in designing scalable and effective Graph Neural Networks. In parallel, I also study strategies to optimize the employment of Large Language Models, focusing on improving their cost-efficiency and overall performance.
Academic Services
Academic Services
Conference Program Committee Member：
International Conference on Learning Representations (ICLR): 2025
ACM Web Conference (WWW): 2022, 2023, 2024, 2025
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD): 2022, 2023, 2024, 2025
Conference on Neural Information Processing Systems (NeurIPS): 2023, 2025
ACM International Conference on Web Search and Data Mining (WSDM): 2023
ACM International Conference on Information and Knowledge Management (CIKM): 2023, 2024, 2025
Learning on Graphs Conference (LoG): 2022, 2023, 2024
Journal Reviewer of VLDBJ, TKDE, Neurocomputing, Journal of Global Optimization, ACM Computing Surveys, Applied Network Science, Mathematics.
Invited Talks
Invited Talks
Spectral Graph Neural Network: Polynomial Approximation and Optimization by Prof. Lio at University of Cambridge.
Spectral Graph Neural Network: Polynomial Approximation and Optimization by Prof. Laks at The University of British Columbia.
Node-wise Diffusion for Scalable Graph Learning by Prof. Xuanhua Shi at Huazhong University of Science and Technology.
Teaching Assistant
Teaching Assistant
Database System
Java and the Internet
Computer Organization and Architecture
Google Sites
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