Andrew Wells is an Applied Scientist at AWS with 12 years of experience blending research-grade machine learning and robust software engineering. He holds a Ph.D. from Rice University where he applied ML to robot task-motion planning and stochastic synthesis for human-robot collaboration, and he has translated that research rigor into production-focused roles at Tesla and AWS. His open-source work includes implementing R-trees and spatial data structures for the high-performance mlpack C++ library, reflecting deep expertise in algorithms and backend systems. Based in Austin, he brings a rare combination of formal verification-style thinking from academic robotics and hands-on systems engineering for large-scale cloud and automotive applications.
12 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Rice University
Bachelor's degree, Philosophy, Bachelor's degree, Philosophy at The Catholic University of America
mlpack: a fast, header-only C++ machine learning library
Role in this project:
Back-end Developer
Contributions:58 commits in 3 months
Contributions summary:Andrew's commits focus on adding and modifying code related to rectangle type trees, specifically within the `mlpack` machine learning library. Their primary contribution is the initial implementation of code for R-trees and its associated components, including split node functionality and the descent heuristic. The user also worked on modifying and implementing various methods for these data structures.
Contributions:10 pushes, 1 branch in 5 years 2 months
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