GEMM and attention on ARM SME/SVE, and HPC, AI agents, and model deployment brought together into systems that run.
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01
ARM / HPC
UniGEMM, GEMM, and attention on SME/SVE
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02
AI agents
Tool use, remote execution, and agent workflows
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03
Model deployment
Local inference, ARM platforms, and performance checks
Education
- 2019–2024Harbin Institute of Technology, Shenzhen · B.Eng. in Electronics and Information Engineering
- 2021–2023KU Leuven · B.Sc. in Engineering Technology, joint program
- 2023–2024KU Leuven · M.Sc. in Engineering Technology, Software Systems · Cum Laude
Experience
- 2025–presentUniGEMM, GEMM, and attention on ARM SME/SVE: multi-precision assembly generation, core GEMM kernels above 95% of theoretical peak, and memory-access and compute optimizations across several attention variants.
- Same periodMPI and UCX performance work in HPC settings, and maintenance of KBLAS and Eigen.
- 2023–2024Stability after model deployment: monitoring input distributions and output anomalies to reduce concept drift.
Paper
- Data Augmentation Using Language Models for Event-Driven Stock Prediction Sentiment Analysis AEMR, 2023 · DOI 10.56028/aemr.8.1.298.2023