Justin Sing
Graduate Student
Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolJustin Sing is a computational biologist and PhD student at the University of Toronto with eight years of experience developing algorithms and machine learning pipelines for mass spectrometry proteomics and multi-omics personalized medicine. He has a strong track record of improving quantification of post-translational modifications (raising quantified phosphopeptides by 21% while maintaining 97% ID consistency) and building production-ready tools, including a Rust library for deep-learning peptide property prediction during an industry internship. As a back-end developer and test automation engineer, he has contributed to the widely used OpenMS codebase, focusing on robust modification handling and test coverage. Justin also co-founded a biotech startup that delivered a rapid microbiome-based CAD screening prototype and led its early commercialization efforts. He combines academic rigor with startup pragmatism and a commitment to open-source collaboration to translate method development into clinical and software impact.
8 years of coding experience
9 years of employment as a software developer
Bachelor of Technology, Biotechnology, Bachelor of Technology, Biotechnology at McMaster UniversityDoctor of Philosophy - PhD, Computational and Systems Biology, Doctor of Philosophy - PhD, Computational and Systems Biology at University of Toronto
English
Github Skills (8)
algorithms10
openms10
c-language10
mass-spectrometry10
cprogramming-language10
proteins10
testing10
meta8
Programming languages (10)
Github contributions (5)
OpenMS/OpenMS
Jun 2021 - Aug 2022
The codebase of the OpenMS project
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:23 reviews, 17 commits, 12 PRs in 1 year 2 months
Contributions summary:Justin primarily focused on bug fixes and refactoring within the `MRMAssay` module, specifically addressing issues related to modifications sequence handling. They implemented checks to ensure the correct application of modifications, considering residue specificity. Moreover, the user added and refined test cases, ensuring the correctness of the modifications and related logic. This included refactoring existing tests and incorporating new test cases to validate the implemented modifications.
windowsanalysesms-datalinuxc-plus-plus
Roestlab/diffusion-deconvolution-dia-msms-data
Sep 2024 - Mar 2025
🚀 Winner of the Donnelly Centre Innovation and Commercialization award: Apply diffusion models to deconvolute highly multiplexed DIA-MS/MS data by conditioning on MS1 signals to generate cleaner MS2 data for downstream analysis.
Contributions:5 reviews, 34 PRs, 89 pushes in 6 months
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