Ken Kreutz-Delgado's current research concerns the development of biologically inspired sensorimotor intelligent learning systems that can effectively function in unstructured, nonstationary environments and provide insight into human information processing and neuropathological disorders, such as Parkinson's disease. He is the lead researcher responsible for the development of EEG-based neurocomputation signal processing algorithms for the US Army Research Laboratory's Cognitive and Neuroergonomics Collaborative Technology Alliance (CAN-CTA), an Army-funded consortium of universities and research institutes. In furtherance of this activity, Ken is affiliated with, and collaboratively interacts closely with researchers at, the UCSD Swartz Center for Computational Neuroscience, which is responsible for managing the UCSD component of the Army CAN CTA research activity.
Ken is also a co-PI on the recently funded collaborative NSF EFRI research activity "Distributed Brain Dynamics in Human Motor Control," which involves the development of novel imaging methods to monitor and record body and brain activity during real-world tasks. The resulting data will be used to develop detailed, large-scale models of activity in the brain's basal ganglia-cortical networks, where Parkinson's disease takes its toll, with the ultimate goal of ameliorating the symptoms of PD. These research activities involve the sophisticated use of statistical signal processing, statistical learning theory and pattern recognition, adaptive sensory-motor control, nonlinear dynamics and multibody systems theory, and optimization theory.
Before joining the faculty at UC San Diego, Ken was a researcher at the NASA Jet Propulsion Laboratory, California Institute of Technology, involved with the development of adaptive, intelligent telerobotic systems for use in space exploration and satellite servicing and repair. His technical contributions in robotics include the development of a spatial operator algebra for the analysis and control of complex, multibody systems which exploits an algorithmic analogy to the recursions of discrete-time Kalman filtering and smoothing (work that resulted in a NASA Technology Achievement Award), the application of nonlinear dynamical reduction for robust sensory-motor control of multilimbed robotic systems. and the use of learning theory and differential topology for the development of trainable nonlinear representations for sensory-motor control.
Beginning in the mid-1990s, Ken began research into the sparse solution recovery problem and dictionary learning. He is also currently involved with research into the development of deep learning architectures combined with reinforcement learning as providing reasonable models of brain cognitive function for tasks such as situation awareness, object recognition, and sequential gaming.
Ken is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the IEEE SPS Technical Committee. He holds a Ph.D. in Engineering Systems Science and MS and BA degrees in Physics, from the University of California, San Diego.