Sleep, memory & pediatric survivorship
Examining fast and slow sleep spindles and their association with post-sleep memory consolidation in long-term pediatric cancer survivors using EEG.
Research
I develop computational approaches for non-invasive neuroimaging, connecting methodological advances in AI with questions in cognition, sleep, pediatric cancer survivorship, and neuromodulation.
Examining fast and slow sleep spindles and their association with post-sleep memory consolidation in long-term pediatric cancer survivors using EEG.
Characterizing cerebral network disruption after medulloblastoma surgery and supporting cognitive rehabilitation through real-time fMRI neurofeedback.
Developing self-supervised frameworks for resting-state fMRI connectivity and sleep EEG dynamics with region-aware and explainable representations.
Current directions
My current work at St. Jude combines computational modeling, clinical neuroscience, and non-invasive brain technologies.
Establishing a simulation pipeline for low-intensity transcranial focused ultrasound in sickle cell disease, integrating Brainsight, SimNIBS-based anatomical extraction, and BabelBrain to study targeting and pediatric safety constraints.
Building on postdoctoral work in EEG-fNIRS fusion, auditory processing, and overparameterized neural decoding to understand complementary electrophysiological and hemodynamic signals.
Extending doctoral research on music perception, emotion, meditation, mind-wandering, and cognitive-state decoding from EEG signals and functional networks.
Creating reusable analysis pipelines, public EEG datasets, and computational resources that support transparent, collaborative brain research.
Methods
My work spans experimental design, multimodal acquisition, signal processing, functional connectivity, deep learning, high-performance analysis, and scientific software development.