Nate Sutton is a computational neuroscientist and software developer with 13 years of experience, currently a postdoctoral researcher at Brandeis University after completing a Ph.D. in Bioengineering with a Neurotechnology and Computational Neuroscience concentration. He blends deep domain knowledge in sensory perception, spatial navigation, learning and memory with practical skills in processing large electrophysiology and genomics datasets. Technically fluent across C++, Java, and Python, he is a full-stack developer comfortable shipping backend systems, web frontends, and robust data pipelines. Nate has a track record of optimizing scientific software—contributing a tested FastTree command-line wrapper to the Biopython project—and applying statistical and machine learning methods to biological problems. Past roles span academic research, team training and project leadership, and hands-on lab work, giving him a rare combination of wet-lab experience and production-grade coding. He maintains an active public profile of publications and code, reflecting a commitment to reproducible, open computational biology and neuroscience.
13 years of coding experience
6 years of employment as a software developer
Master of Science (M.S.), Biomedical Informatics, 3.98/4.00, Master of Science (M.S.), Biomedical Informatics, 3.98/4.00 at Arizona State University
Bachelor of Science (B.S.), Biology, Bachelor of Science (B.S.), Biology at Quinnipiac University
Doctor of Philosophy (Ph.D.), Bioengineering - Neurotech & Computational Neuroscience Concentration, 4.0/4.0, Doctor of Philosophy (Ph.D.), Bioengineering - Neurotech & Computational Neuroscience Concentration, 4.0/4.0 at George Mason University
Official git repository for Biopython (originally converted from CVS)
Role in this project:
Back-end Developer
Contributions:14 commits, 3 comments in 3 months
Contributions summary:Nate primarily contributed to the development of a command-line wrapper for the FastTree program within the Biopython library. Their work involved creating a Python class (`FastTreeCommandline`) to interface with the FastTree executable, adding various command-line options, and integrating the wrapper into the Biopython application structure. Furthermore, they addressed input/output argument issues, added type safety checks, and included unit tests to ensure functionality. These additions enhance Biopython's phylogenetic analysis capabilities.
Contributions:78 pushes, 2 branches in 9 years 3 months
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