Xuesi Li is a PhD-trained researcher and engineer with 11 years of experience at the intersection of deep learning, signal processing, semiconductor sensors, and bioelectronics. He has led end-to-end AI system development from data collection to inference and performance analysis, and has deployed large time-series models with pruning and quantization for efficient inference on GPU clusters and FPGAs. His work spans academia and industry—postdoctoral research at Zhejiang Lab on automated AI testing and cross-domain transfer (EEG to gas sensors), collaborative real-time sensing systems at The University of Tokyo, and sensor process work at ULVAC China. Xuesi combines hands-on hardware deployment (CNN quantization on Lattice FPGA) with biosensor assay optimization (graphene-based miRNA detection), demonstrating rare fluency across algorithms, devices, and fabrication. He is based in Shanghai and known for translating domain-specific signals into scalable ML solutions that perform in constrained embedded and semiconductor environments.
11 years of coding experience
4 years of employment as a software developer
University of Tokyo
Bachelor of Engineering - BE, Electronic Information Engineering, Bachelor of Engineering - BE, Electronic Information Engineering at Tianjin University
A tiny state management library for Vue Composition API.
Contributions:1 release, 92 commits, 93 PRs in 9 months
apicompositionstate-managementfunction-apivue3
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