Boyuan Zhang

Boyuan Zhang (张博源)

Assistant Professor, Department of Computer Science · University of Kentucky
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About

I will be joining the Department of Computer Science at the University of Kentucky as an Assistant Professor. I received my Ph.D. from the Department of Intelligent Systems Engineering at Indiana University Bloomington (2026), advised by Dr. Dingwen Tao and Dr. Fengguang Song.

I earned a B.Eng. in Information Engineering from Shanghai Jiao Tong University (2018) and an M.S. in Electrical Engineering from the University of Southern California (2020).

I collaborate with Argonne National Laboratory (Dr. Sheng Di, Dr. Franck Cappello) on scientific data compression, Pacific Northwest National Laboratory (Dr. Nathan R. Tallent) on quantum simulation and AI-based compression, and Meta (Dr. Min Si) on GPU-based compression for distributed training. In the summer of 2025, I interned at ByteDance (Seed, ML Systems) working on LLM inference optimization.

Openings

I am actively recruiting fully-funded Ph.D. students to join my research group at the University of Kentucky. If you are interested in High-Performance Computing, Data Compression, Parallel Computing, ML Systems, Quantum Computing, or related topics, please feel free to reach out! Positions are available starting from Fall 2026.

For full details, see the recruiting page: English | 中文版

News

2026.06
I will be joining the Department of Computer Science at the University of Kentucky as an Assistant Professor.
2026.03
Two papers accepted at IPDPS'26.
2025.10
Reached Diamond in League of Legends with a 68% win rate, maining Aatrox top lane.
2025.08
Completed internship at ByteDance (Seed, ML Systems) on LLM inference optimization.
2025.06
Best Paper Runner-Up at ACM ICS'25 for BMQSim.
2025.06
Best Paper Candidate at ACM ICS'25 for Aatrox.
2025.03
Two papers accepted at ICS'25.
2024.07
One paper accepted at SC'24.
2023.05
Started internship at Pacific Northwest National Laboratory with Dr. Nathan R. Tallent.
2023.04
Paper on FZ-GPU accepted at HPDC'23.
2023.04
Paper on GPULZ accepted at ICS'23.

Research

My research primarily focuses on High-Performance Computing (HPC), with particular emphasis on:

Data Compression

Design of GPU-based lossy and lossless compression algorithms for various workloads, with an emphasis on efficiency, fidelity, and scalability.

Parallel Computing

Development of optimized GPU kernels, parallel algorithms, and distributed workflows to accelerate large-scale scientific and engineering applications.

ML Systems

Research on system-level optimizations for training and inference, including memory footprint reduction and GPU-accelerated data pipelines.

Quantum Computing

Investigation of high-performance methods to support large-scale quantum circuit simulation and emerging quantum computing applications.

Selected Publications

IPDPS'26
Near-Zero Cost KV Cache Compression for Large Language Model Inference
Boyuan Zhang, Ding Zhou, Yafan Huang, Shihui Song, Hao Feng, Jinda Jia, Chengming Zhang, and Zhi Zhang.
Proceedings of the 40th IEEE International Parallel and Distributed Processing Symposium, 2026.
IPDPS'26
Accelerating AI Compression through Lightweight Lossless Encoding and Pipelined Workflows
Boyuan Zhang, Luanzheng Guo, Jiannan Tian, Jinyang Liu, Daoce Wang, Chengming Zhang, Bo Fang, Fengguang Song, Jan Strube, Nathan R. Tallent, and Dingwen Tao.
Proceedings of the 40th IEEE International Parallel and Distributed Processing Symposium, 2026.
ICS'25 Best Paper Runner-Up
BMQSim: Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework
Boyuan Zhang, Bo Fang, Fanjiang Ye, Luanzheng Guo, Fengguang Song, Nathan R. Tallent, and Dingwen Tao.
Proceedings of the 39th ACM International Conference on Supercomputing, pp. 689-704, 2025.
ICS'25 Best Paper Candidate
Pushing the Limits of GPU Lossy Compression: A Hierarchical Delta Approach
Boyuan Zhang, Yafan Huang, Sheng Di, Fengguang Song, Guanpeng Li, and Franck Cappello.
Proceedings of the 39th ACM International Conference on Supercomputing, pp. 654-669, 2025.
SC'24
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
Hao Feng*, Boyuan Zhang*, Fanjiang Ye, Min Si, Ching-Hsiang Chu, Jiannan Tian, Chunxing Yin, Summer Deng, Yuchen Hao, Pavan Balaji, Tong Geng, and Dingwen Tao.
SC24: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-16, IEEE, 2024.
HPDC'23
FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs
Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Yunhe Feng, Xin Liang, Dingwen Tao, and Franck Cappello.
Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 129–142, 2023.
ICS'23
GPULZ: Optimizing LZSS Lossless Compression for Multi-Byte Data on Modern GPUs
Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Martin Swany, Dingwen Tao, and Franck Cappello.
Proceedings of the 37th ACM International Conference on Supercomputing, pp. 348–359, 2023.

* denotes equal contribution. Please refer to the full list on Google Scholar.