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Shelby Center, Suite 3101E, Auburn University, AL 36849-5347
Department of Computer Science and Software Engineering
Samuel Ginn College of Engineering
Office: 334-844-6327 | xqin@auburn.edu
ยป curriculum vitae | How to pronounce Xiao Qin
In summer 2026, Peixiong He successfully defended a dissertation on energy-aware SQL database systems. Dr. He is an Assistant Professor of Computer Science at Tuskegee University, AL.
Our new paper, led by Hui Sun, presents HAKV, a hotness-aware zone management approach that keeps hot key-value pairs in persistent memory to reduce write amplification of LSM-tree-based key-value stores.
In summer 2022, Longwei Wang successfully defended a dissertation on the robustness of deep neural networks. Dr. Wang is an Assistant Professor at the University of South Dakota, SD.
Auburn University is ranked 46th among public universities and 100th nationally in the 2027 U.S. News & World Report Best Colleges rankings, up from 49th and 102nd last year.
The AI Bridge: From AI Literacy to AI Fluency for All Auburn Students
AI Bridge
The AI Bridge is a pathway from AI literacy to proficiency and fluency for all Auburn students. Start with COMP 1100 Power of AI (new in Spring 2027; no math or code required), cross the three-course Bridge, and continue to the Certificate in AI Engineering or the MS in AI Engineering. Supported by the Center for Inclusive Computing at Northeastern University.
BUD: A Buffer-Disk Architecture for Energy-Efficient Parallel Disks
data storage system
The goal of this research is to develop energy conservation techniques that provide significant energy savings while achieving low-cost and high-performance for parallel storage systems.
MINT: Mathematical Reliability Models for Energy-Efficient Parallel Disk Systems
Mint Model
We address the mathematical underpinnings of modeling reliability of energy-efficient parallel disk systems, where fault tolerance and energy-saving techniques are seamlessly integrated together to conserve energy without sacrificing reliability in parallel disk systems.
Multicore-Based Parallel Disk Systems for Large-Scale Data-Intensive Computing
data storage system
We investigate active storage systems which data and I/O processing are offloaded to multicore processors embedded in storage nodes. We bridge the technology gap between multicore computing and parallel storage systems by addressing fundamental issues of multicore computing, data processing and performance analysis for data-intensive computing systems.
Data-Mining-Based Multilayer Prefetching for Hybrid Storage Systems
BUD system
We develop data-mining-based multilayer prefetching techniques to improve the performance of data centers with hybrid storage systems. The technology results in data being loaded from disks to main memory before it is accessed from the disks, thus improving performance and reliability of hybrid storage systems with solid state disks (SSDs), hard disks (HDDs) and tapes.
Xiao Qin is a Professor and the Director of Graduate Programs in the Department of
Computer Science and Software Engineering at Auburn University. He received the B.S. and M.S. degrees in Computer
Science from Huazhong University of Science and Technology,
China, in 1996 and 1999, respectively. He received the Ph.D.
in Computer Science from the University of Nebraska-Lincoln in
2004. Prior to joining Auburn University in 2007, he had been
an assistant professor with New Mexico Institute of Mining and
Technology (New Mexico Tech) for three years. He won an NSF
CAREER award in 2009. His research interests include parallel
and distributed systems, real-time computing, storage systems,
fault tolerance, and performance evaluation. His research is
supported by the U.S. National Science Foundation, Auburn
University, and Intel Corporation. He had served as a subject
area editor of IEEE Distributed System Online (2000-2001). He
has been on the program committees of various international
conferences, including IEEE Cluster, IEEE IPCCC, and ICPP.