About Me
I am a PhD Candidate at University of California, Riverside. Prior to the PhD study in Computer Science, I obtained MS and BS degrees from Columbia University and Peking University.
News
- Jun 2025: Three papers were accepted at SC’25
- Mar 2025: Gave a talk at the Las Vegas, NV (PPoPP 2025).
- June. 2023: I gave a talk at International Conference on Supercomputing 2023.
- April. 2023: A paper was accepted at International Conference on Supercomputing 2023.
Education
-
Ph.D. in Computer Science (Sep. 2022 – Present)
University of California, Riverside (UCR)
Advisor: Prof. Zizhong Chen -
M.S. in Electrical Engineering (Sep. 2020 – May 2022)
Columbia University -
B.S. in Computer Science (Sep. 2016 – Jul. 2020)
B.S. in Economics (Double Major)
Peking University
Research Experience
- USC ISI / Argonne National Laboratory (Jan. 2024 – Present)
Los Angeles, CA / Lemont, IL
Scientific Workflow Applications on Resilient Metasystem
Mentors: Dr. Franck Cappello, Dr. Sheng Di, Dr. Krishnan Raghavan (ANL); Dr. Ewa Deelman (USC ISI)- Designed a Q-learning + GNN-based topology protocol (DGRO) that reduces network diameter by optimizing virtual rings over heterogeneous, failure-prone systems.
- Implemented a single-hop gossip-based failure detector, resilient to network jitter and churn, enabling decentralized membership monitoring across 20+ globally distributed sites.
- Deployed DGRO on the FABRIC testbed spanning Japan, Europe, Hawaii, and 15+ U.S. locations, demonstrating fast convergence and robustness at international scale.
- UCR / Lawrence Berkeley National Laboratory (Sep. 2022 – Present)
Riverside, CA
Data-driven Exascale Control of Optically Driven Excitations in Chemical and Material Systems
Mentor: Dr. Zizhong Chen- Designed and implemented in-kernel ABFT GEMM using tensor cores, achieving higher performance than cuBLAS while ensuring fault detection and correction under soft errors.
- Developed a fully GPU-resident ABFT FFT pipeline, outperforming cuFFT, and enabling error-resilient spectral analysis in scientific simulations.
- Proposed the first ABFT-enabled K-means clustering framework on GPUs, exceeding cuML performance with integrated resilience support.
- Innovated lightweight, low-overhead in-kernel fault tolerance mechanisms across linear algebra and ML workloads, demonstrating resilience-performance co-design in exascale systems.
- Nvidia (Jun. 2024 – Sep. 2024)
Santa Clara, CA
Compiler Optimization for OpenMP Target Offload on Heterogeneous GPU Architectures
Mentor: Dr. David Appelhans- Investigated performance bottlenecks of OpenMP target offload in SPEChpc 2021 on GH200/H200 GPUs.
- Developed compiler/runtime optimizations achieving up to 10× speedup without source code changes.
- Analyzed OpenMP vs. OpenACC performance and contributed optimized versions to SPEChpc 1.1.9.
- Work adopted by RWTH Aachen University, demonstrating both research impact and practical relevance.
- Columbia University / AI4Finance Foundation (Aug. 2021 – Jul. 2022)
New York, NY
ElegantRL: Massively Parallel Deep Reinforcement Learning Library
Mentors: Dr. Xiaoyang Liu, Dr. Xiaodong Wang- Developed multi-agent RL algorithms in ElegantRL, a popular RL library with ~4k GitHub stars.
- Co-led ElegantRL_Solver, a high-performance solver that outperforms Gurobi for dense MaxCut problems.
Selected Publications
1.
SC '25
2.
SC '25
3.
PPoPP '25
4.
SC '24
5.
ICS '23
6.
Cluster '24
7.
SC '25
FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention. [paper]
Huangliang Dai, Shixun Wu, Jiajun Huang, Zizhe Jian, Yue Zhu, Haiyang Hu, and Zizhong Chen.
Huangliang Dai, Shixun Wu, Jiajun Huang, Zizhe Jian, Yue Zhu, Haiyang Hu, and Zizhong Chen.
8.
SIGMOD '24
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation. [paper]
Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Sian Jin, Zizhe Jian, Jiajun Huang, Shixun Wu, Zizhong Chen, Franck Cappello.
Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Sian Jin, Zizhe Jian, Jiajun Huang, Shixun Wu, Zizhong Chen, Franck Cappello.
9.
IPDPS '24
CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction. [paper]
Zizhe Jian, Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang, Haiying Xu, Robert Underwood, Shixun Wu, Jiajun Huang, Zizhong Chen, and Franck Cappello.
Zizhe Jian, Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang, Haiying Xu, Robert Underwood, Shixun Wu, Jiajun Huang, Zizhong Chen, and Franck Cappello.
10.
BigData '23
Exploring Wavelet Transform Usages for Error-bounded Scientific Data Compression. [paper]
Jiajun Huang, Jinyang Liu, Sheng Di, Yujia Zhai, Zizhe Jian, Shixun Wu, Kai Zhao, Zizhong Chen, Yanfei Guo, Franck Cappello.
Jiajun Huang, Jinyang Liu, Sheng Di, Yujia Zhai, Zizhe Jian, Shixun Wu, Kai Zhao, Zizhong Chen, Yanfei Guo, Franck Cappello.
11.
Allerton '23
Downlink beamforming optimization via deep learning. [paper]
Jeremy Johnston, Xiaoyang Liu, Shixun Wu, Xiaodong Wang.
Jeremy Johnston, Xiaoyang Liu, Shixun Wu, Xiaodong Wang.