TReNDS Center & Georgia State University
My overarching research goal is to explore the interdisciplinary realms of Neuroscience, Applied Dynamical Systems, and Computer Science for a better understanding of the human brain, and to apply those insights into building modern technologies. My research interests span across multiscale and multimodal approaches for the study of neural dynamics ranging from intracellular electrophysiology for modeling single neurons and neural networks, all the way to whole brain modeling of neuroimaging data. My current work is focused on the study of spatially dynamic human brain networks in resting state fMRI recordings, their coupling and differential modulation under pathological conditions. I am also involved in simultaneous fMRI/EEG experiments, data collection and analysis from human subjects performing reading related tasks. My previous postdoctoral work was focused on the computational modeling of thoracic sympathetic postganglionic neurons in mice that integrate synaptic inputs from the spinal cord and regulate vasculature and thermoregulatory systems. As part of my graduate research, I developed mathematical and computational approaches for the study of biological neural network dynamics, multistability of central pattern generators, their phase spaces, and bifurcations. I also employed symbolic dynamics for the study of global bifurcations and chaos in individual neuronal models and emergent network behaviors in half center oscillators and swim central pattern generators in sea slugs. My work involved a combination of dynamical systems analysis with modern clustering, machine learning and GPU parallelization. In addition, I studied the origin and intricacy of homoclinic bifurcatons underlying complex dynamics in a variety of Lorenz-like systems such as lasers from nonlinear optics and various circuit models, through kneading invariants and symbolic methods. My prior area of expertise as a Software Development Engineer was large scale data processing using grid computing technologies. With a thorough background in Computer Science and professional training in various facets of Neuroscience and Applied Dynamical Systems, I have the necessary skills to pursue advanced interdisciplinary research. In addition, I am greatly interested in exploring how the generality of our modeling approaches could lead to novel methodologies and nonlinear science applications in biological, medical and engineering systems. I started working independently as well as collaborating with startup organizations using consumer grade devices and sensors for the development of real world applications in the areas of human motion capture, biomechanical modeling, biosensing and brain computer interfaces. I would like to continue to explore relevant industry collaborations while keeping my core focus on scientific research.