XIAOBO ZHOU        

Prof. Zhou’s research is centered on advancing system support for data-intensive applications, particularly in the realms of cloud computing and machine learning. He focuses on optimizing performance, resource efficiency, and system reliability, all of which are critical for scaling modern computational workloads.

He served as the chair of the IEEE Technical Committee on Distributed Processing (2020-2023).

Research Interests

• Cloud Computing
• Systems for Machine Learning
• High-Performance Distributed Computing
• Memory Systems and OS

PhD Student Opportunities

* Prof. Zhou is looking for self-motivated PhD students who have solid background and interests in Operating System, Computer Architecture, Parallel and Distributed Computing and related fields. Please send him an email with your CV.  

He has supervised over a dozen PhD students, with graduates securing tenure-track faculty positions at research-oriented academic institutions, including the University of Texas at San Antonio and the University of North Carolina at Charlotte. His former students have also joined prestigious Argonne National Laboratory, and high-tech firms including Amazon, Cloudflare, Intel, Meta, Microsoft, Nvidia, and Tencent.

Recent Publications

• [SC24] Scaling New Heights: Transformative Cross-GPU Sampling for Training Billion-Edge Graphs, ACM/IEEE SC, 2024
• [SC24] MCFuser: High-Performance and Rapid Fusion of Memory-bound Compute-intensive Operators, ACM/IEEE SC, 2024
• [SC24] Accelerating Distributed DLRM Training with Optimized TT Decomposition and Micro-Batching, ACM/IEEE SC, 2024
• [ATC24] Expeditious High-Concurrency MicroVM SnapStart in Persistent Memory with an Augmented Hypervisor, USENIX ATC, 2024
• [HPDC23] Let It Go: Relieving Garbage Collection Pain for Latency Critical Applications in Golang, ACM HPDC, 2023
• [HPDC23] Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism, ACM HPDC, 2023 (The Best Paper Runner-up)