Jianliang Gao is a senior teaching fellow and experienced digital healthcare researcher with 10 years of hands-on experience building scalable medical software, cloud and HPC infrastructure, and data science training programmes. He combines deep technical skills in Python, R, MATLAB, Docker, SaltStack and Slurm with practical expertise in MRI/medical imaging pipelines, machine learning (CNNs, U-Net), and XNAT database management. At Imperial College he leads postgraduate data science modules, research computing projects and a neonatal brain imaging platform, while also delivering big-data and LLM workflows for social-economics research. His background spans academia and industry—from designing sensor hardware and IoT systems to deploying containerised analysis on Azure and private clouds—bringing both low-level embedded insight and large-scale orchestration. Colleagues describe him as a creative problem-solver who reliably invents new approaches under deadline pressure, and his work includes openly shared tools and cloud deployments used by global research teams.
10 years of coding experience
7 years of employment as a software developer
Master of Education - MEd, Master of Education - MEd at Imperial College London
BS, Computer Science, BS, Computer Science at Fujian Normal University
Doctor of Philosophy (PhD), Computing, Doctor of Philosophy (PhD), Computing at Ulster University
Hong Kong University of Science and Technology (HKUST)
Contributions:24 commits, 1 PR, 88 pushes in 1 year 10 months
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