Scholar
Sun Shixuan
Google Scholar ID: jCAwFlgAAAAJ
Shanghai Jiao Tong University
Citations & Impact
Citations
769
H-index
14
i10-index
15
Publications
20
Co-authors
26
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Publications:
- - LiquidGEMM: Hardware-Efficient W4A8 GEMM for High Performance LLM Serving (SC'25)
- - RapidStore: An Efficient Dynamic Graph Storage System for Concurrent Queries (VLDB'25)
- - GES: High-Performance Graph Processing Engine and Service in Huawei (SIGMOD'25)
- - Revisiting the Design of In-Memory Dynamic Graph Storage (SIGMOD'25)
- Awards:
- - Outstanding PC Award at ICDE 2025
- - The Championship of Alibaba POLARDB High Performance Competition (1/1808)
- - Hong Kong PhD Fellowship Award 2015-2019
- - Outstanding Graduate of Shanghai 2011, 2014
- - China National Scholarship 2009, 2013
- - Google Excellent Student Scholarship 2010
Research Experience
- Worked as a research fellow at the School of Computing, National University of Singapore from 2020 to 2023, supervised by Prof. Bingsheng He; Worked as a Software Development Engineer at Microsoft (Shanghai) from 2014 to 2015.
Education
- Ph.D. in Computer Sciences from the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) in 2020, supervised by Prof. Qiong Luo; M.S. and B.S. in Computer Sciences from the School of Software Engineering, Tongji University in 2014 and 2011 respectively.
Background
- Currently a Tenure-Track Associate Professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University. Member of the EPCC Lab. Research interests include parallel and distributed computing, database systems, machine learning, and data-intensive applications.
Miscellany
- Looking for self-motivated PhD students, master’s students, and research assistants to join his group. Research areas include large-scale data processing systems, efficient and effective retrieval-augmented generation techniques, high-performance LLM inference system deployment, and accelerating zero-knowledge proof systems with heterogeneous computing.
