Xinglong Li
Applied Scientist II at Amazon
Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolXinglong Li is an applied scientist with seven years of experience building probabilistic and state-space models and deploying ML systems at scale, currently at Amazon. He holds a PhD in Statistics and has strong hands-on expertise in JAX and probabilistic ML, contributing core components to the Dynamax library and notebooks for Kevin Murphy’s Probabilistic Machine Learning book. His work spans end-to-end recommender systems, high-performance scientific libraries (Dynamax 800+ stars), and biomedical modeling for cancer diagnosis, demonstrating both production engineering and deep theoretical grounding. He has taught statistical modeling at UBC and repeatedly translated rigorous math into robust, tested code that runs across CPU, GPU, and TPU.
7 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at The University of British ColumbiaMaster's degree, Statistics, Master's degree, Statistics at 云南大学Bachelor's degree, Applied Mathematics, Bachelor's degree, Applied Mathematics at China University of Petroleum 中国石油大学(华东)Associate's degree, Associate's degree at Polytech Nice Sophia
Github Skills (18)
algorithm10
algorithms10
probabilistic-programming10
python10
model-driven10
machine-learning10
implement10
model-building10
state-space10
gmm10
hmmlearn10
jupyter-notebook10
jax10
modeling10
model-driven-development10
Programming languages (3)
Github contributions (5)
probml/dynamax
Jul 2022 - Nov 2022
State Space Models library in JAX
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:6 reviews, 177 commits, 30 PRs in 4 months
Contributions summary:Xinglong contributed to the development of the core infrastructure of the `dynamax` library, implementing methods for different HMM emission distributions. Their work involved the development of several models in `ssm_jax` with a focus on the mathematics and implementation of the models, involving both the design of algorithms and their implementation in code. They implemented the `GaussianSSM` and `PoissonSSM` models, which are core components of the codebase.
pythonstate-space-modelsdynamical-systemskalman-filterstate-space
probml/pyprobml
Jun 2022 - Jul 2022
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Role in this project:ML Engineer
ML EngineerContributions:51 commits, 12 PRs, 1 push in 1 month
Contributions summary:Xinglong primarily contributed to notebooks related to probabilistic machine learning, specifically focusing on Dirichlet Processes (DP) and Gaussian Mixture Models (GMM). Their work involved implementing and refining code for DP mixture simulation, clustering analysis, and truncated stick-breaking constructions. The contributions included creating plots for figures within the "Probabilistic Machine Learning" book and fixing the notebooks to align with current function output format. The user's focus demonstrates a strong understanding of probabilistic modeling techniques and their application.
pythonpmlflaxcolabpymc3
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