Roshan Nanu
Director, AI at Prompt Health
United States
Summary
👤
Senior🎓
Top SchoolRoshan Nanu is a multidisciplinary AI leader with a Ph.D. in Neuroscience and nine years of experience building and scaling AI products across healthcare and industrial SaaS. Currently Director of AI at Prompt Health, he crafts AI strategy, infrastructure, responsible-use frameworks, and leads R&D to translate models into clinical and operational impact. His background blends deep neuroscience research—designing neural implants and advanced signal-analysis pipelines—with hands-on ML engineering, including contributions to the open-source H1st framework to improve time-series model integration and usability. Comfortable bridging research, product, and engineering, he has a track record of turning complex experimental methods into production-ready systems and of growing AI teams and literacy in organizations.
9 years of coding experience
11 years of employment as a software developer
Master of Science (MS) Applied Cognition and Neuroscience, Master of Science (MS) Applied Cognition and Neuroscience at The University of Texas at DallasCalifornia Institute of TechnologyDoctor of Philosophy (Ph.D.) Neuroscience, Doctor of Philosophy (Ph.D.) Neuroscience at Brandeis University
English
Github Skills (9)
scikit-learn10
machine-learning10
time-series10
predictive-modeling10
python10
scikit10
data-science9
documentation8
automl7
Programming languages (2)
Github contributions (5)
h1st-ai/h1st
Jan 2022 - Jan 2023
Power Tools for AI Engineers With Deadlines
Role in this project:
ML Engineer
ML EngineerContributions:13 reviews, 37 commits, 16 PRs in 11 months
Contributions summary:Roshan primarily contributed to the H1st framework by updating and correcting examples, documentation, and addressing merge conflicts. They refactored the Oracle model to load and persist submodel classes, streamlining the initialization process. The user's code changes included adapting and integrating machine learning models (e.g., RandomForestClassifier) within the Oracle framework, specifically for time-series data analysis. These revisions indicate a focus on improving the framework's usability, stability, and machine learning capabilities.
predictive-maintenancecollaborationhome-automationpower-toolsdatascience-environment
nubs01/GeneQuery
Apr 2017 - Jan 2021
Contributions:21 pushes, 5 branches in 3 years 9 months
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