Roshan Rao
Principal Research Scientist at Biohub
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolRoshan Rao is a Principal Research Scientist based in New York with 12 years of experience bridging machine learning research and applied bioinformatics. He holds a PhD in Computer Science from UC Berkeley and has advanced from academic research to industry roles at Meta, EvolutionaryScale, and now Biohub, focusing on ML systems for biological data. Roshan contributes to impactful open-source work—he helped build foundational PyTorch tooling and datasets for TAPE, a widely used benchmark for protein embeddings—demonstrating strengths in dataset engineering, model tooling, and evaluation pipelines. His background includes hands-on experience in scientific computing and visualization from early roles at the Space Telescope Science Institute, giving him an uncommon combination of astrophysics-flavored engineering and biological ML expertise. Colleagues describe him as a pragmatic researcher who turns complex scientific problems into reproducible, production-ready models and datasets.
12 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of California, BerkeleyBachelor of Science - BS Applied Mathematics and Computer Science, Bachelor of Science - BS Applied Mathematics and Computer Science at Brown University
English, Kannada, Russian
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (9)
pytorch10
machine-learning10
python10
language-modeling10
data-science9
nlp9
deep-learning9
wcf6
tensorflow4
Programming languages (7)
Github contributions (5)
songlab-cal/tape
Aug 2019 - Aug 2021
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:2 releases, 1 review, 329 commits in 1 year 11 months
Contributions summary:Roshan's initial commits focus on setting up the foundational PyTorch code and datasets for protein embedding tasks, a core focus of the repository. Their contributions include the creation of a Pfam tokenizer and datasets for various tasks, particularly masked language modeling. Furthermore, the user added data conversion scripts, along with the creation of models, and metrics, indicating a focus on setting up training and evaluation pipelines for the protein embedding tasks.
benchmarkbioinformaticssemi-superviseddomainssemi
rmrao/evo
Feb 2021 - Sep 2021
Contributions:31 commits, 45 pushes, 1 branch in 7 months
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