My research in machine learning includes theory and algorithm synthesis, with applications to signal processing, fault detection and prediction in regimes from health care to complex environmental systems.
Health Care Applications of Machine Learning
I collaborate with the OHSU Point of Care Laboratory (POCL) and with Jeff Kaye director of OHSU's Layton Againg & Alzheimer's Disease Center. My work with these colleagues is aimed at detecting behavioral changes that are predictive of emerging health problems, particularly cognitive decline. This work makes use of a number of novel unobstrusive in-home monitoring technologies (see POCL) to provide early detection of health-related changes.
Deniz Erdogmus, Steve Kazmierczak, and I have a new NSF-funded project aimed at detecting errors in clinical laboratory measurements.
I've enjoyed a collaboration with Antonio Baptista and OGI's Center for Coastal and Land-Margin Research. Our work on the CORIE project is has improved reliability of measurements and modeling in the Columbia River estuary. We developed and deployed a system to detect biofouling of salinity sensors deployed in the estuary that cut data loss in half. We have applied learning technology as key elements in a (problem-portable) data assimilation (Bayesian model / data fusion) system. Ours is the first data assimilation system to operate successfully in a strongly non-linear river-estuarine-ocean system. Our novel model surrogates, trained to emulate the dynamics of extremely large (10^7 degrees of freedom) finite element hydronamics models, are a critical enabling technology for this work. The surrogates vastly accelerate the forward model evaluation by factors of one to twelve thousand, enabling a dramatic increase in ensemble prediction capability.
Environmental Observation and Forecasting Systems
Stochastic Learning Dynamics
I have a long-standing interest in the stochastic dynamics of artificial and natural learning systems as described by the master equation from statistical physics. My students and I have previously applied these theoretical tools to the asymptotics of stochastic approximation algorithms, and were led to new algorithms that consistently reach the theoretical maximum convergence rate with simplicity and elegance. Under NSF-funding, my students and I are developing rigorous perturbation methods for approximate solutions, and applying them to biological spike-timing-dependent plasticity (STDP) learning rules. The approximation methods have applications to a range of Markov jump processes, such as the chemical master equation.
Computational Neuroscience
Dr. Pat Roberts (OHSU), Prof. Nathan Sawtell (Columbia University) and myself have a NIH/NSF Collaborative Research in Computational Neuroscience project on sensory-motor processing and memory in the mormyrid weakly electric fish. The fish have an electro-location system that uses the animal's electric organ discharge (EOD) to navigate, identify objects, and find prey. The electrosensory lateral line lobe (ELL) of the mormyrid brain integrates motor command, proprioceptive, and electrosensory information in a cerebellar-like structure. As part of its function, the ELL generates memories comprising the expected sensory signal from the fish's own electric discharge. These memories are adapted over time through spike-timing-dependent plasticity (STDP). The project integrates modeling and neurophysiology experiments to determine how realistic patterns of excitation are processed in ELL, and how plasticity is controlled by recurrent connections from higher centers. As part of the project, we are developing a novel computer-controlled stimulus system that provides precise control of the spatio-temporal profile of the electric images on the fish's skin.
