Research Interests (non-technical overview)
Scientific data visualization and management; computational neuroscience; biocomputing; bioinformatics; biotechnology and biomedical technology; neuroinformatics; distributed information systems; simulation; scientific computing; neural networks; autonomous systems; computer graphics; computer vision; nonlinear dynamics; complex systems.
"So what is this mind of ours: what are these atoms with consciousness? Last week's potatoes! They now can remember what was going on in my mind a year ago --- a mind which has long ago been replaced."Richard P. Feynman
My long-term research goal is to understand the computational principles underlying biological nervous system function for application to machine intelligence. Involved in this investigation include issues of computational neuroscience, artificial intelligence, neural networks, bioinformatics, nonlinear dynamics, robotics, scientific computing, scientific visualization, and collaborative computing.
In the nearer term, I wish to determine how neuron structural and behavioral complexity (small-scale dynamics) contributes to nervous system operation (large-scale behavior), such as in learning and sensorimotor systems.
My collaborators and I have been investigating information transfer across the synapses that connect nerve cells. This synaptic coding process is the functional unit of nervous systems, and as such the computational unit of neural networks. Part of my recent work has focused on the interaction between stochastic events ("errors", e.g., synaptic transmission failure) and neuron dynamics and the implications of this for synaptic coding.
I have also been working recently on modeling the growth and activity of biological neural networks grown in vitro. These dissociated cortical tissue preparations are viewed, on the one hand, as potential "neuro-electronic hybrid computers" and, on the other, as models for epilepsy. I hope that my work will explain why these networks show pathological bursting activity and how to stop this bursting and get them to behave like normal neural tissue.
My methods have been based on the hypothesis that single neurons perform nontrivial operations, and that the coding of neural input to output can be understood if one views the neuron as a nonlinear dynamical system. This has involved very enjoyable collaborations with neuroscientists, mathematicians, and physicists.
As a founding faculty member here at UWB, and a near-founding-faculty member at HKUST, I have been involved in the development and teaching of a broad cross-section of the computer science core, as well as a range of upper-level undergraduate and graduate electives. Subjects I have taught include: introductory, medium-level, and advanced programming, programming tools, object-oriented design, data structures and algorithms, discrete mathematics, calculus, technical writing for software professionals, computer graphics, computer vision, visualization, multimedia, computer architecture, artificial intelligence, neural networks, complex systems, signal processing, and expert systems.
I have been involved in four recent and ongoing teaching initiatives:
- Initiation of the Master of Science in Computing and Software Systems degree.
- Developing a "signal computing" course as the CS counterpart of signal processing (supported by the National Science Foundation under Grants No. 0443118 and 0816701).
- Redesigning my expert systems class to become an introduction to rule-based systems as components within larger, enterprise systems.
- UWB faculty members Steve Collins, Alan Leong, and I have founded the UW Bothell Biotechnology and Biomedical Technology Institute, which will house research, community, industry, and government outreach, and an inter-programmatic minor (supported by a UWB Worthington Academic Distinction Award).