Stanford University
Showing 1-20 of 14,400 Results
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Oliver O. Aalami, MD
Adjunct Professor, Bioengineering
BioDr. Oliver Aalami is a vascular surgeon and the Director of Digital Health at the Stanford Byers Center for Biodesign. His primary mission is to advance healthcare access through digital health education, research, and translation. At Stanford, he serves as the course director for Biodesign for Digital Health and Building for Digital Health and is a co-founder of Spezi (formerly CardinalKit), an open-source framework developed to support sensor-based mobile research.
His recent work focuses on the intersection of AI and patient care, including the development of an FDA-cleared open-source computer vision model for opportunistic abdominal aortic diameter quantification on routine CT scans. Additionally, he is developing LLMonFHIR, a system that allows consumers to "chat" with their medical records (FHIR resources) on mobile devices, as well as AI-assisted coaching tools to guide patients through therapy. -
Maricela Abarca
Data Curator for Interdisciplinary Sustainability, Data Management Services
BioAs a Data Curator for the Doerr School of Sustainability community, I provide resources in data acquisition, management, and analysis in the interdisciplinary and evolving field of sustainability. I can provide guidance on best practices in open data and data science methods, including AI and machine learning.
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Mohammad Abbasi, PhD
RSE / Data Scientist and Data Manager at Transitional AI lab, Psych/Public Mental Health & Population Sciences
BioI am a Research Software Engineer (RSE) | Data Scientist & Data Manager at the Stanford Translational AI (STAI) Lab. I lead data engineering and AI infrastructure across large-scale neuroscience and healthcare projects, building standards-first, end-to-end pipelines for data collection, curation, preprocessing, and multimodal integration. My work emphasizes reproducibility, scalability, and interoperability through BIDS-style schemas, schema validation, containerized deployments, and CI/CD across heterogeneous computing environments.
I have designed and maintained containerized preprocessing workflows for thousands of subjects across major datasets, automating modality-specific steps such as registration, intensity normalization, bias-field correction, motion/confound estimation, quality control, and downstream metadata exports. I ensure these pipelines are robust, well-documented, versioned, and reusable across projects, sites, and modalities.
