The University of Texas Health Science Center at Houston
McGovern Medical School
Department of Neurology
My research focuses on developing mathematical approaches for extracting hidden features from complex signals and conceptualizing neurobiological data, using ideas and methods from topology, geometry, signal processing, information theory and Artificial Intelligence. In particular, we focus on studying the structure of the EEG signals in different brain states—during active behavior, learning, wakefulness vs. sleep, and we are particularly interested in understanding the alterations of the EEG structures in neurodegenerative disorders (notably in Alzheimer Disease) and in epilepsy.
A principal line of research in my lab addresses mechanisms of learning and memory, including principles of spatial cognition. We use topological data analyses, neuronal network modeling, deep learning and complexity theory to integrate “neuronal” data (spike times, synaptic network architectures, parameters of synaptic plasticity, etc.) with “organismal” data (behavior, learning dynamics, cognitive representations of physical reality). Multidisciplinary approaches help establishing causal connections between the two neurobiological scales and understanding how the brain converts neuronal firing patterns into cognitive maps of the world. To better understand the latter, we develop experimental methods for objective quantification of cognitive dynamics in healthy subjects and assessments of learning and memory (in)capacity at early stages of Alzheimer Disease. In particular, we study how complexity of learning tasks influences learning and memory dynamics across different ages, using a variety of cognitive tests, including Virtual Reality techniques.
Education & Training
PhD, University of Rhode Island, 2000