Emanuel Burgos
Machine Learning Intern at Gensaic
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolEmanuel Burgos is a computational biologist and machine learning intern with eight years of combined research and engineering experience, currently working at Gensaic after earning an MS in Biomathematics, Bioinformatics, and Computational Biology from MIT. He blends hands-on molecular biology expertise from a multi-year stint in Mandel Lab with strong Python, statistical modeling, and ML skills to build computational models that probe biological dynamics. Emanuel has contributed backend analysis features to the widely used anvi'o 'omics platform, adding INSeq/Tn-Seq statistics and performance improvements that bridge wet-lab data and scalable bioinformatics pipelines. At MIT he also led diversity-focused graduate workshops, demonstrating a commitment to inclusive scientific communities alongside technical depth. Based in Cambridge, he focuses on turning complex biological datasets into interpretable models that inform experimental design and discovery.
8 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Biomathematics, Bioinformatics, and Computational Biology, Master of Science - MS, Biomathematics, Bioinformatics, and Computational Biology at Massachusetts Institute of TechnologyBachelor of Science - BS, Molecular Biology, 3.81, Bachelor of Science - BS, Molecular Biology, 3.81 at University of Wisconsin-Madison
Github Skills (6)
bioinformatics10
python10
metagenomics10
numpy9
data-analysis9
database-design9
Programming languages (3)
Github contributions (5)
merenlab/anvio
Jun 2019 - Jul 2019
An analysis and visualization platform for 'omics data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 commits, 3 PRs, 4 comments in 25 days
Contributions summary:Emanuel primarily contributed to the development of gene-level coverage statistics within the anvio platform. Their work involved modifying and extending existing classes and functions in `dbops.py` and `tables/genelevelcoverages.py` to incorporate INSeq/Tn-Seq statistical methods, as well as fixing performance issues. They also made updates related to total read mapping, suggesting involvement in data processing and analysis pipelines. The user's modifications included adding and refactoring code related to INSeq stats and adding new database tables.
pythonmetagenomicssciencemetatranscriptomicsphylogenomics
eburgoswisc/barseq
Jul 2020 - Oct 2022
Contributions:2 PRs, 15 pushes, 3 branches in 2 years 3 months
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