Shenyang Huang
Senior ML Research Scientist at University of Oxford
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolShenyang Huang is a postdoctoral researcher in geometric machine learning at the University of Oxford, specializing in temporal graph learning, link prediction, and anomaly detection on dynamic graphs. With a PhD from McGill/Mila and nine years of experience spanning academia and industry, he has contributed to state-of-the-art graph transformers and molecular property prediction (including work on GPS++ and the Graphium library). He organizes the Temporal Graph Learning Workshop at NeurIPS and runs a weekly reading group, reflecting both research leadership and community-building. His applied work includes accelerating large graph models on IPUs and integrating transformer architectures into recommender systems, with open-source contributions to PyTorch Geometric. Combining deep theoretical grounding with practical engineering, he bridges molecular modelling, continual learning, and scalable GNN/transformer systems.
10 years of coding experience
2 years of employment as a software developer
High School, IB Diploma, High School, IB Diploma at Rothesay Netherwood SchoolDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at McGill University
English, Chinese, French
Github Skills (15)
bootstrap10
static-site10
jekyll-theme9
jekyll9
graph9
sass9
visualization8
machine-learning8
geometric-deep-learning8
static-site-generator8
graph-analysis5
foundation-models2
testing1
read-the-docs1
transfer-learning1
Programming languages (6)
Github contributions (5)
shenyangHuang/LAD
May 2020 - Nov 2021
Laplacian Change Point Detection for Dynamic Graphs (KDD 2020)
Contributions:32 commits, 28 pushes, 2 branches in 1 year 5 months
shenyangHuang/openDG
Nov 2024 - Jun 2025
Open Dynamic Graph library
Contributions:62 reviews, 11 PRs, 42 pushes in 7 months
graph
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