Cheng Ong
Senior Principal Research Scientist at CSIRO's Data61
Canberra, Australia
Summary
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Rockstar🎓
Top SchoolCheng Ong is a Senior Principal Research Scientist with over a decade of experience advancing statistical machine learning for scientific discovery, particularly in genomics, systems biology, fMRI and medical imaging. He blends theoretical contributions—new optimization methods for feature selection and structured output prediction—with applied collaborations that translate models into domain impact. At CSIRO/Data61 he has led and directed programs exploring how to embed domain constraints, privacy and explainability into ML for real-world science. An advocate for open science and reproducible research, he has contributed to notable community resources and worked on the companion site for the widely used "Mathematics for Machine Learning" book. Based in Canberra, he pairs deep academic training (PhD ANU) with practical engineering experience, from kernel learning to production-ready open-source tooling.
11 years of coding experience
19 years of employment as a software developer
Australian National UniversitySPM, SPM at Royal Military College, MalaysiaThe University of SydneyA-levels Mathematics Physics Economics, A-levels Mathematics Physics Economics at Raffles Institution
German, Malay, hokkien
Github Skills (3)
html10
documentation10
css8
Programming languages (4)
Github contributions (5)
mml-book/mml-book.github.io
Dec 2017 - Dec 2020
Companion webpage to the book "Mathematics For Machine Learning"
Role in this project:
Technical Writer
Technical WriterContributions:76 commits, 3 PRs, 59 pushes in 3 years
Contributions summary:Cheng primarily contributed to the companion webpage by adding content and structure. This involved updating chapter titles, adding links to chapter PDFs, and including a table of contents, foreword, and references. Furthermore, the user incorporated author Twitter handles and clarified how to report errata and provide feedback. The overall focus of the commits was on organizing and structuring the webpage for the book.
companionmathematicsmachine-learningwebpage
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