Bhavesh Patel
Research Associate Professor @California Medical Innovations Institute
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WORK HISTORY
Research Associate Professor @California Medical Innovations Institute
San Diego, CA, US
EDUCATION
Arts et Métiers - École Nationale Supérieure d'Arts et Métiers
Master of Engineering - MEng, Engineering
University of California, Berkeley
Doctor of Philosophy (Ph.D.), Mechanical Engineering
University of California, Berkeley
Master of Science - MS, Mechanical Engineering
University of California, Berkeley
Master of Arts - MA, Mathematics
Lycée Saint-Louis
Classes Préparatoires aux Grandes Ecoles (CPGE), Mathematics, Physics, Engineering Science
SKILLS
ABOUT BHAVESH PATEL
Short version: I lead a team of 8 software developers and AI research scientists at the FAIR Data Innovations Hub. We develop open source tools, standards, and guidelines that help researchers prepare and share their data and other research outcomes such that they are more open, reusable, and AI-ready. Our projects have received over $9M in funding from the National Institutes of Health (NIH) and other organizations. I wouldn’t trust me with that kind of money, but somehow they do, which I guess speaks to the importance and quality of our work.Longer version: I am a fervent supporter of Open Science, which is the practice of making research outcomes as openly available as possible within ethical, social, and legal boundaries. I believe this is critical to foster collaboration, enable reproducibility, increase equity, and accelerate discoveries.While valuable, Open Science policies often add extra burden to researchers. I experienced this firsthand as a postdoc when I was tasked with preparing and sharing datasets for an NIH project. It was time-consuming (and honestly, boring), so I started building automation scripts. My colleagues wanted to use them too, which led me to make these tools more accessible for everyone, even those without coding skills.That small side project evolved into the FAIR Data Innovations Hub, a division I founded and now lead. Our team of 8 software developers and AI research scientists builds open-source platforms and standards that make it easier for researchers to prepare and share their data, software, posters, and other research outcomes in line with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. These principles ensure research outcomes are not only reusable by humans, but also ready for machines and AI/ML applications.Since then, our work has attracted over $9M in funding from the NIH, the Microsoft AI for Good Lab, The Navigation Fund, and other organizations. Our platforms are now helping thousands of researchers worldwide make their work more open, reusable, and AI-ready.Major Skills and ExpertiseFAIR Data Practices | Open Science | Data Sharing | AI/ML Readiness | Open-Source Software | Data & Metadata Standards | Computational Modeling | Team Leadership | Grants Writing & Management | Strategic CollaborationsLinkshttps://fairdataihub.orghttps://github.com/fairdataihub
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