James Melville
Director, Software & Data Science
San Diego, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJames Melville is a Director of Software & Data Science with over a decade of experience building data-centric platforms across autonomous robotics, drug discovery, and bioimage analysis. He blends hands-on engineering—cloud-native systems, SRE, and performant C++ libraries—with statistical and machine-learning rigor developed during a PhD in physical chemistry. James is the author of uwot, the R/C++ UMAP implementation widely used by Seurat and Monocle3 to produce the 2D embeddings seen in high-impact biology journals, and has contributed performance and cross-platform fixes to pynndescent and hnswlib. He has led teams through cloud migrations, observability for fleets of robots, and production biostatistics tooling that automates complex hypothesis testing. Comfortable moving from low-level SIMD optimizations to high-level Dash apps, he combines curiosity-driven research instincts with pragmatic delivery. Based in San Diego, he describes his GitHub work wryly as “the work of a lazy, self-indulgent man,” hinting at a playful, efficiency-first approach to engineering.
10 years of coding experience
20 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physical Chemistry, Doctor of Philosophy (Ph.D.) Physical Chemistry at University of OxfordMaster’s Degree Chemistry, Master’s Degree Chemistry at University of Bath
English
Github Skills (21)
algorithms10
c-language10
dimensionality-reduction10
python10
imap10
nearest-neighbors10
scikit10
testing10
machine-learning10
topological-data-analysis10
cicd10
performance-optimization10
omap10
scikit-learn10
simd10
Programming languages (6)
Github contributions (5)
lmcinnes/pynndescent
Apr 2019 - Sep 2021
A Python nearest neighbor descent for approximate nearest neighbors
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:79 commits, 43 PRs, 18 pushes in 2 years 5 months
Contributions summary:James primarily contributed to documentation fixes and enhancements within the project. They added build status badges to the README, improving the project's visibility. Additionally, the user addressed compilation warnings and resolved type signature issues. They also worked on fixing a recursion error and making the code compatible with Windows, indicating a focus on cross-platform compatibility and maintainability.
knn-graphsneighborspythonapproximate-nearest-neighbor-searchnearest-neighbor
nmslib/hnswlib
Oct 2018 - Aug 2022
Header-only C++/python library for fast approximate nearest neighbors
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:6 commits, 8 PRs, 11 comments in 3 years 10 months
Contributions summary:James primarily focused on optimizing the performance of the HNSW (Hierarchical Navigable Small World) library. Their contributions involve removing unnecessary code, particularly semi-colons, and implementing preprocessor directives to handle non-portable code and leverage SIMD instructions (SSE, AVX). They also addressed constructor initialization and fixed a reordering warning, contributing to overall code quality and efficiency.
python-libraryneighborspythonheadernearest
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