Wendy Mak
Senior Data Scientist
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolWendy Mak is a Senior Data Scientist and ML engineer based in London with 11 years of experience building and deploying production ML and LLM solutions across cloud environments. She combines deep academic training (PhD in Physics, Cambridge) with hands-on MLOps expertise, having migrated on-prem prototypes to AWS and operated ML systems on GCP while improving platform security and team practices. Her work spans semantic search, low-latency LLM serving, high-throughput document parsing and time-series/graph models, and she contributes to open-source projects—improving PyTorch Geometric type hints and stabilizing a flood-forecasting PyTorch library. A pragmatic mentor and process advocate, she champions rigorous code review, knowledge sharing, and reusable deployment components to accelerate cross-team delivery. Colleagues rely on her ability to translate complex research ideas into robust, maintainable production services.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at University of CambridgeMaster of Arts (MA), Publishing, Master of Arts (MA), Publishing at Anglia Rusking University
Github Skills (27)
data-visualizations10
pytorch10
bokeh10
python10
testing10
pandas10
data-visualisation10
typehinting10
deep-learning10
data-visualization10
type-checking10
graph-neural-network10
geometric-deep-learning9
spatial-data9
geospatial9
Programming languages (7)
Github contributions (5)
AIStream-Peelout/flow-forecast
Jun 2020 - Jun 2020
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:69 commits, 11 PRs, 55 pushes in 13 days
Contributions summary:Wendy's primary focus was on fixing failing tests within the `flow-forecast` repository. Their commits demonstrate efforts to resolve CPU/CUDA errors and address issues causing test failures in multiple files, including `test_da_rnn.py` and `pytorc_train_tests.py`. The user also made changes to the `setup.py` file. Their work helped improve the reliability and stability of the codebase by ensuring that tests pass and the project is able to run effectively.
forecastingtime-series-analysistime-seriesclassificationautoencoder
pyg-team/pytorch_geometric
Oct 2022 - Jan 2023
Graph Neural Network Library for PyTorch
Role in this project:Back-end Developer
Back-end DeveloperContributions:5 reviews, 10 commits, 14 PRs in 3 months
Contributions summary:Wendy primarily contributed to type hinting and code modernization efforts within the PyTorch Geometric library. They added type hints to various datasets, normalization layers, and model components, enhancing code readability and maintainability. They also implemented and integrated new features such as the PGM explainer, including the code required to perturb and test node features. This work demonstrates a focus on improving code quality, adding new functionalities, and ensuring compatibility with newer PyTorch versions through the addition of type hints.
pytorchgraph-convolutional-networksgeometric-deep-learningdeep-learningneural-graph
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