Xiaoke Zhu

Xiaoke Zhu

Ph.D. candidate at ACT lab, Beihang University

About Me

I am Xiaoke Zhu (朱筱可, Hsiaoko Chu in Wade-Giles romanization), currently a Ph.D. candidate in the ACT lab at Beihang University (BUAA), under the supervision of Prof. Wenfei Fan. I received master degree at Yunnan University (YNU) in 2020, where I was advised by Prof. Wei Zhou and Prof. Shaowen Yao.

Research Interests

My research focuses on graph computing and databases, optimizing runtime efficiency for shared-memory and CPU/GPU architectures, with publications in SIGMOD, VLDB, ICDE, BigData, and CLOUD. A brief summary of my past work can be found below.

Graph Computing Systems

I have worked on building a high-level programming model and runtime system that can execute applications on shared-memory or out-of-memory architectures with CPUs or GPUs. For out-of-core graph analytics (e.g., PageRank, SSSP), I improved I/O efficiency, and for graph mining (e.g., Graph Data Cleaning, Pattern Matching), I optimized GPU performance. Relevant results were published in [VLDB’23, VLDB’25, SIGMOD’25]

Data Cleaning Systems

I have worked on improving the performance of data cleaning systems on modern hardware like GPU or on distributed cluster. I have also compared different parallel runtime systems for data cleaning, and identified their performance bottlenecks. Relevant results were published in [ICDE’22, VLDB’25, VLDBJ’25, SIGMOD’25]

AI4DB

I have leverages machine learning and deep learning model to improve tasks traditionally handled by human database administrators or classical algorithms, enabling more efficient data processing and resource management. Specially I have designed learned models for sorting, load balancing, and scheduling. Relevant results were published in [CLOUD’21, BigData’24]

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