School of Engineering
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Assistant Professor of Statistics and of Electrical Engineering
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.
Robert and Barbara Kleist Professor in the School of Engineering, Emeritus
BioDutton's group develops and applies computer aids to process modeling and device analysis. His circuit design activities emphasize layout-related issues of parameter extraction and electrical behavior for devices that affect system performance. Activities include primarily silicon technology modeling both for digital and analog circuits, including OE/RF applications. New emerging area now includes bio-sensors and the development of computer-aided bio-sensor design.