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My primary research investigates attention, learning, and expertise using MEG (magnetoencephalography) and frequency-tagging of complex stimuli, with the aim to transition experimental settings towards more naturalistic environments. Leveraging powerful advancements in machine-learning, my colleagues and I developed a multivariate approach that enhanced the sensitivity of frequency-tagging, allowing subtle neural changes to be reliably captured and uncovering relationships between selective attention and musical training. 

I am also heavily involved in the development of multimodal neuroimaging methods, with a focus on integrating MEG and MRS (magnetic resonance spectroscopy) to establish critical connections between electrophysiological brain signals and their underlying biochemistry. This would provide a deeper understanding of neural mechanisms that drive sensory, cognitive, and behavioral processes in health and disease. Specifically, my methods are developed to accommodate advanced j-edited MRS techniques for measuring GABA and glutamine/glutamate neurotransmitter concentrations that are critically linked to cognitive function. Presently, I am involved in collaboration projects between ̽»¨¾«Ñ¡, Massachusetts General Hospital, Harvard Medical School, and MIT.