James Webber is a Senior Bioinformatics Scientist with 14 years of experience bridging computational biology and software engineering, currently leading bioinformatics efforts at Delve Bio from Columbus, Ohio. He combines deep academic training (PhD, UCSF) with hands-on production work at institutions like the Broad Institute and CZ Biohub, delivering scalable analysis pipelines and algorithmic improvements. A Python veteran and Rust aficionado, he contributes to prominent open-source projects including SciPy (notably improving dendrogram plotting) and RustPython, demonstrating expertise in numerical libraries and interpreter correctness. His contributions to performance-sensitive libraries such as pynndescent show a focus on algorithmic optimization and parallelism as well as careful refactoring. Colleagues rely on him for pragmatic solutions that balance research rigor with maintainable code, and he often juggles multiple complex projects simultaneously.
14 years of coding experience
12 years of employment as a software developer
B.A., Biology, Computer Science, B.A., Biology, Computer Science at Cornell University
PhD, IPQB, PhD, IPQB at University of California, San Francisco
A Python nearest neighbor descent for approximate nearest neighbors
Role in this project:
Back-end Developer
Contributions:4 reviews, 23 commits, 11 PRs in 10 months
Contributions summary:James primarily contributed to refactoring and optimizing the `pynndescent` library. Their work involved removing problematic heap push operations, eliminating dead code, and fixing potential bugs in the weighted Minkowski distance function. They also improved the codebase by parallelizing the deheap_sort function and fixing docstrings, demonstrating a focus on performance and code quality.
Contributions:16 commits, 2 PRs, 26 comments in 3 years 5 months
Contributions summary:James contributed significantly to the `scipy/scipy` repository, primarily focusing on the `cluster/hierarchy.py` module, particularly the `dendrogram` function. Their work involved adding new functionalities, such as the `axis` keyword for plotting on custom axes, and refactoring the code for better readability and maintainability. The user also addressed several bugs related to label formatting and subtree functionality, and added tests to improve the coverage and reliability of the plotting functionality.
scipypythonscientific-computing
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