Mike Vella
Vice President Of Machine Learning at Oxford Nanopore Technologies
Oxford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMike Vella is a machine learning and applied-science leader with 14 years of experience building GPU-accelerated AI systems for genomics, healthcare and life sciences. Currently VP of Machine Learning at Oxford Nanopore in Oxford, he has driven production-ready basecalling and inference platforms (notably Dorado) and led ML Ops and high-performance inference teams to deliver real-time sequencing at scale. Previously he led EMEA solutions architecture for NVIDIA’s healthcare and genomics customers, helping deploy Cambridge-1 and accelerating ML adoption across pharma and research. His background spans hands-on deep learning engineering, GPU/FPGA deployment, and scientific modelling—grounded in a PhD in computational neuroscience from Cambridge. He also contributes to prominent open-source ML tooling documentation (e.g., PyTorch/TensorRT tutorials), reflecting a focus on developer experience as well as model accuracy. Colleagues describe him as a practical innovator who bridges wet-lab priorities and production ML engineering.
14 years of coding experience
12 years of employment as a software developer
PhD, Computational Neuroscience, PhD, Computational Neuroscience at University of CambridgeMSci, Physics, MSci, Physics at University of Bristol
Maltese, English, Italian
Stackoverflow
10,417reputation
1.3mreached
75answers
77questions
python
top-5%
Github Skills (12)
rs10
documentation10
python9
html9
tensorrt7
pytorch7
recursion6
substitution6
dictionary6
jinja26
r6
numpy6
Programming languages (13)
Github contributions (5)
pytorch/TensorRT
Jul 2020 - Jul 2020
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Role in this project:
Technical Writer
Technical WriterContributions:10 commits, 1 PR, 4 comments in 1 day
Contributions summary:Mike primarily focused on modifying and updating documentation within the repository, specifically the tutorials section and API documentation. Their contributions involved adjusting code examples, such as modifying compile settings, and reverting these changes. The user also made changes to the documentation files in both HTML and RST formats, ensuring clarity and accuracy. These changes suggest the user's role is centered on improving user understanding and the presentation of information regarding the project.
compilernvidiapytorchtensorrttorchscript
vellamike/emacs.d
Aug 2015 - May 2021
Contributions:24 pushes, 1 branch in 5 years 10 months
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