Using Engineering, Mathematics and Computer Science in analyzing neuroimaging data, electrophysiological data, and behavior to help understand Learning and Memory.
Using a variety of techniques ranging from Machine Learning, to Complex Dynamical Simulation, to Virtual Reality Simulation, I study Learning and Memory and computational models of Learning and Memory.
The UIUC Holodeck Project - https://theholodeckproject.github.io/ - This project uses off-the-shelf video game virtual reality hardware to test various aspects of cognition. In this project, I serve as project leader, constructing virtual environments to study various aspects of learning and memory.
INSIGHT Project - http://insight.beckman.illinois.edu/ - In this project, a comprehensive, multidisciplinary brain training study with a large neuroimaging component, I use machine learning approaches to study large and complex data sets of behavioral, deomgraphic and neuroimaging data.
Phase-Locked Loop Neural Networks Project - In this project, I use a phase-locked loop network to emulate aspects of memory representations in the phase relationship between coupled oscillators. One early discovery I made was that without any memory component, a network of coupled oscillators can be simulated and measured efficiently in order to identify and classify ictal and interictal seizure data (see publications). This network has many advantages, but one key element is that it is extremely efficient (easy to simulate and easy to implement in hardware components).