Padarn Wilson
Head Of Engineering at Grab
Singapore
Summary
🤩
Rockstar🎓
Top SchoolPadarn Wilson is a Head of Engineering based in Singapore with 13 years of experience building and leading AI and data teams, currently driving AI platform efforts at Grab. With a strong research foundation (MPhil in Computational Mathematics) and early career roles across CSIRO, ANU and Geoscience Australia, he blends rigorous quantitative thinking with product-focused engineering. He has deep hands-on ML and data science expertise, contributing to major open-source projects such as PyTorch Geometric (edge-update and neighbor loader work) and TensorFlow Probability, as well as statistical tooling in statsmodels. That open-source track record shows he not only architects platforms but also improves core ML primitives used by researchers and engineers. Colleagues would describe him as a leader who moves fluidly between research, productionization, and mentoring, able to translate advanced probabilistic and graph ML concepts into scalable platform features. He brings a rare combination of academic rigor and production-grade delivery to large-scale AI platform initiatives.
13 years of coding experience
4 years of employment as a software developer
Australian National UniversityBSc(Hons), Mathematics, First Class, BSc(Hons), Mathematics, First Class at University of Otago
Github Skills (21)
pytorch10
probabilistic-programming10
artificial-neural-networks10
python10
statistics10
machine-learning10
econometrics10
numpy10
deep-learning10
tensorflow10
statsmodels10
neural-network10
graph-neural-network10
graph-convolutional-networks10
data-analysis10
Programming languages (12)
Github contributions (5)
pyg-team/pytorch_geometric
Dec 2021 - Oct 2022
Graph Neural Network Library for PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:468 reviews, 38 commits, 57 PRs in 10 months
Contributions summary:Padarn primarily contributed to the development of graph neural network functionality within the PyTorch Geometric library. Their work focused on adding and refining edge-related operations, including the `edge_update` functionality, JIT compilation for edge updates, and the addition of a link-level neighbor loader. These contributions directly enhanced the library's capabilities for graph-based machine learning tasks. Further commits focused on adding examples and tests for the features, contributing to the library's usability and robustness.
pytorchgraph-convolutional-networksgeometric-deep-learningdeep-learningneural-graph
statsmodels/statsmodels
Oct 2013 - Jan 2015
Statsmodels: statistical modeling and econometrics in Python
Role in this project:Data Scientist
Data ScientistContributions:20 commits, 4 comments in 1 year 3 months
Contributions summary:Padarn primarily contributed to the statistical modeling and econometrics aspects of the repository, focusing on kernel density estimation and bandwidth selection. They implemented and tested features related to weighted kernel fits and added tests to validate density calculations. Furthermore, the user added functionality for bandwidth calculations, including the normal reference method and updated an example for normal reference bandwidth performance comparison.
forecastingpythonregression-modelsstatsmodelsstatistics
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.