Shihan Ma
Senior Machine Learning Engineer at Meta
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolShihan Ma is a research scientist and machine learning engineer with six years of experience bridging academic robotics and applied ML at industry scale. He holds a PhD from Shanghai Jiao Tong University's Robotics Institute and has combined postdoctoral work at Imperial College London with internships and research engineering roles at Meta and OneFlow. At OneFlow he contributed to memory-efficient training techniques (DTR and Coop gradient checkpointing), and at Meta he progressed from intern to research scientist, applying ML research to production challenges. Based in London, he brings a rare mix of deep robotics foundations, hands-on systems experience in tensor rematerialisation, and practical ML deployment instincts. Colleagues would describe him as someone who moves fluidly between theoretical rigor and engineering trade-offs to enable large-model training under tight resource constraints.
6 years of coding experience
3 years of employment as a software developer
Visiting Postgraduate Student, Bioengineering and Biomedical Engineering, Visiting Postgraduate Student, Bioengineering and Biomedical Engineering at Imperial College LondonDoctor of Philosophy - PhD, Robotics institute, Doctor of Philosophy - PhD, Robotics institute at Shanghai Jiao Tong University
English, Chinese
Github Skills (18)
machine-learning10
ml10
deep-learning10
deep-neural-networks10
neural-network10
cuda10
matlab7
generative-model5
computer-graphics5
octave5
image-processing5
visualization5
medical-image-processing4
3d4
cpp3
Programming languages (4)
Github contributions (5)
shihan-ma/oneflow
Jul 2021 - Dec 2021
OneFlow is a performance-centered and open-source deep learning framework.
Contributions:36 pushes, 1 branch in 4 months
pytorchdeep-learningmachine-learningperformanceframework-learning
shihan-ma/BioMime
Feb 2023 - Oct 2023
A deep latent generative model for learning biosystems
Contributions:12 pushes in 7 months
generative-model
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