Computing Systems for Data-Driven Science
Foundations of Data Science
My research interests are at the intersection of theoretical, computational and systems neuroscience. The theoretical work builds on methods of communications/networking, information theory, machine learning, nonlinear dynamical systems, signal processing and systems identification. The computational research centers on the massively parallel emulation of neural circuits and architectures. The experimental work employs methods of genetics, neurophysiology and nanotechnology.
In silico, my focus is on Neural Computing Engines (NCEs) and on Massively Parallel Neural Computation (MPNC). The aim of the NCEs project is to develop formal theoretical methods for neural encoding and decoding, spike processing and the functional identification of neural circuits and architectures. Massively parallel algorithms in the spike domain are the subject of the MPNC project.
In vivo, my focus is on Reverse Engineering the Fruit Fly Brain. My current work primarily addresses the early olfactory system of the Drosophila.
More detailed information is available under Bionet Research.