Arsh Jhaj
Machine Learning Engineer at University of British Columbia
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolArsh Jhaj is a machine learning engineer and recent MSc graduate from UBC with a strong grounding in privacy-preserving ML and education innovation for computer science curricula. He combines academic research—including work on stochastic subgradient descent and theoretical ML—with practical engineering, having built grading automation and an RNN-based plagiarism detector that markedly improved adversarial detection. With 18 years of cumulative experience across research, teaching, and industry internships, he is fluent in Python, PyTorch, Docker, AWS and CI tooling. Arsh is an active open-source contributor, improving usability and compatibility across projects from scikit-learn to reveal.js and conda-forge, showing attention to both user-facing features and backend robustness. He has helped launch a new UBC course on randomized algorithms and repeatedly turned classroom needs into production tooling, reflecting a rare blend of pedagogy and production software craftsmanship. Based in Vancouver, he brings a research-first mindset to building reliable, privacy-aware ML systems that scale in educational and practical settings.
18 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at The University of British Columbia
Stackoverflow
28,593reputation
3.0mreached
384answers
8questions
virtualenv
top-5%
tree
top-5%
cluster-analysis
top-5%
artificial-intelligence
top-5%
numpy
top-1%
neural-network
top-5%
Github Skills (63)
markdown10
scipy10
javascript10
conda-forge10
python10
py10
slide10
testing10
scikit10
statistics10
machine-learning10
slideshow10
cicd10
numpy10
representations10
Programming languages (21)
Github contributions (5)
conda-forge/conda-smithy
Sep 2017 - Aug 2018
The tool for managing conda-forge feedstocks.
Role in this project:
DevOps Engineer
DevOps EngineerContributions:1 release, 40 commits, 13 PRs in 11 months
Contributions summary:Arsh primarily contributed to improving the quality and maintainability of the `conda-smithy` tool. Their work focused on linting, specifically for recipe files, and involved identifying and fixing various issues such as incorrect section ordering, selector issues with `noarch` packages, and bioconda recipe validation. They also updated CI-related components, addressing issues with Travis API versions.
conda-forgecondagocdcontinuous-integrationtravis-ci
scikit-learn/scikit-learn
Feb 2013 - Jun 2019
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:12 commits, 5 PRs, 23 comments in 6 years 4 months
Contributions summary:Arsh primarily contributed to bug fixes and documentation improvements within the scikit-learn library. They addressed issues related to the StratifiedKFold and RBFSampler functionalities. The user also clarified docstrings for KernelDensity and ForestClassifier methods, and corrected documentation related to default gamma values. These commits demonstrate a focus on improving the accuracy and usability of the library.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.