Andrew Spott
Lead Software Engineer at EMode Photonix
Boulder, Colorado, United States
Summary
👤
Senior🎓
Top SchoolAndrew Spott is a Lead Software Engineer with 17 years of technical experience and a PhD in Physics, now building nonlinear and quantum integrated photonics design software in Boulder. He has spent over six years specializing in machine learning and deep learning, leading data science teams at TruU and Submittable to deliver behavioral biometrics and automated review systems that materially improved security and equitable fund distribution. At TruU he architected end-to-end pipelines—from iOS data collection to PyTorch model development and production inference—while also building internal deep-learning tooling for reproducible, out-of-memory training. At Submittable he combined product and engineering leadership to launch an “Accelerated Review” ML product used to scale high-stakes grant reviews. He contributes to open-source tooling for scientific figure export (helping tikzplotlib handle legend edge cases), reflecting a practical focus on reliable research-to-production workflows. Andrew blends rigorous academic training with hands-on engineering leadership across ML, data infrastructure, and domain-specific scientific software.
17 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at University of Colorado Boulder
Stackoverflow
3,547reputation
230kreached
4answers
74questions
Github Skills (12)
latex10
python10
matplotlib10
implement7
algorithm7
algorithms7
git6
vim6
lifetime6
ios6
swift6
clang6
Programming languages (22)
Github contributions (5)
nschloe/tikzplotlib
May 2017 - Jun 2017
:bar_chart: Save matplotlib figures as TikZ/PGFplots for smooth integration into LaTeX.
Role in this project:
Backend Developer
Backend DeveloperContributions:6 commits, 1 PR, 3 comments in 25 days
Contributions summary:Andrew primarily focused on improving the `tikzplotlib` library's ability to handle lines in matplotlib figures, specifically addressing how lines not in the legend are handled. Their contributions included bug fixes, adding explanations of the fix in code, and implementing a function to determine if a line is present in the legend. They also updated existing code to improve the handling of legend entries. This resulted in better integration of matplotlib figures into LaTeX environments.
matplotlibpgfplotstikzpythonlatex
spott/petsc-cpp
Sep 2013 - Apr 2015
Contributions:10 releases, 52 commits, 14 PRs in 1 year 7 months
cppc-plus-pluspetsc
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