Stanford University
Showing 8,171-8,180 of 37,200 Results
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Alfredo Dubra, PhD
Professor of Ophthalmology
Current Research and Scholarly InterestsOur lab seeks to help the early diagnosing and monitoring progression of ocular, vascular, neurodegenerative and systemic diseases through novel non-invasive optical ophthalmic imaging. We pursue this goal through a multidisciplinary approach that integrates optics, computer science, vision science, electrical engineering and other engineering disciplines.
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Piotr Dubrowski
Clinical Instructor, Radiation Oncology - Radiation Physics
BioPiotr Dubrowski is a Canadian trained and board-certified Therapy Medical Physicist. Throughout his career Piotr has had the opportunity to bring several new Cancer Clinics operational from the ground up, where he was able to hone a broad, systems-approach to Radiation Oncology. He was recently promoted to Associate Quality Director of Physics responsible for improving patient safety and workflow/technology improvements across a wide cancer care network. Piotr’s research interests focus mainly on improving the treatment planning process and increasing clinic safety through software development and hardware 3D-print prototyping. Additionally, Piotr has sought out opportunities to give back to the Global community with participation in the Stanford Global Radiation Oncology Outreach efforts expanding education and access to care in Tanzania and Kenya and throughout the developing world via online Radiation Therapy courses.
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John Duchi
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
