Designing systems that regulate themselves in the presence of uncertainty, noise, and delay. This theme focuses on classical and modern control theory, with applications in mechatronics, instrumentation, and robotics. Work includes system identification, nonlinear control, and real-time computing, often extending into philosophical questions about autonomy and agency.
The Machine Learning Control Project explores how modern data-driven techniques can augment or replace traditional control design methodologies. While classical control theory depends heavily on first-principles modeling, many real-world systems …
View projectThis undergraduate research project was funded by Puget Sound Energy via the Independent Colleges of Washington to develop a smart thermostat device based on principles of lumped-parameter modeling and estimation.
View projectSpin Transport and Control Systems Project investigated the control, measurement, and theoretical modeling of spin systems at the nanoscale. Originating from the challenges of magnetic resonance force microscopy (MRFM), the …
View project