Hello, my name is Juan M. Banda. I am a Lead Data Scientist at Stanford Health Care, where I work on bringing artificial intelligence and machine learning into clinical practice. My current work focuses on the responsible evaluation, deployment, and monitoring of AI systems in health care, including generative AI and large language models. Recent projects include MedHELM, a benchmark for clinically grounded medical LLM evaluation, and frameworks for evaluating and monitoring AI systems deployed at Stanford Health Care. Previously, I was an Assistant Professor of Computer Science at Georgia State University, where I led Panacea Lab, with research focusing on applications to precision medicine, medical informatics, astroinformatics and other domains. My work addresses domain-specific problems with data science methods and practices. As an engineer at heart and practice for the last 20 years, I have used Python, Bash, ontologies, and NLP tools to build pipelines to annotate over 68 million clinical notes. I have built custom ETLs to map over 8 million patient electronic health records, from 4 institutions, to common data models (OMOP) for large scale analytics and machine learning purposes. I have designed pipelines, databases, and processes to build research infrastructure for my role and in my previous research labs. I have used R, SQL, Matlab, Perl, Java, Javascript, and other languages to acquire, clean and operationalize data from multiple sources. I have mined over 9 billion Tweets for NLP tasks to gain insights from them. In my earlier days, I built content-based image retrieval systems for NASA’s SDO mission, with capacity to process and index over 40,000 images daily, and provide computer vision-aided similarity search for images. I started my engineering days designing and developing point-of-sale systems written in Visual Basic. Apart from my technical skills, I have strong communication and writing skills (over 90 refereed publications) and management skills (I have managed over 40 employees and 23 students). With the desire of improving patient outcomes, medical care and building things that change people’s lives, I am committed to releasing all my work via open-source licenses following the FAIR data sharing principles. I am an active collaborator of the Observational Health Data Sciences and Informatics and my work has been funded by the Department of Veteran Affairs, National Institute on Aging as well as NASA, NSF, and NIH.