Xiangjian (Aaron) Zeng

Aaron Zeng is a graduate student in electrical engineering and computer science whose research harnesses closed-loop auditory stimulation to enhance cerebrospinal fluid (CSF) flow during sleep. CSF performs vital housekeeping functions, clearing metabolic waste from the brain; inadequate circulation is suspected to drive cognitive decline in aging and neurodegenerative disorders. His preliminary findings show that auditory stimuli can drive measurable increases in CSF flow for aging populations, and sleep brain features can be used to predict stimulation effects and guide delivery. To translate this discovery into therapeutic intervention, he develops the closed-loop auditory modulation of sleep system using MATLAB’s Signal Processing Toolbox for real-time filtering and the Deep Learning Toolbox for machine learning-based phase prediction. He implements custom graphical interfaces with MATLAB’s UI tools for seamless control and monitoring during overnight sleep experiments. As a MathWorks Fellow, Aaron will advance this computational framework toward multi-phase systems. By enabling phase-locked auditory stimulation, this platform holds the promise to reshape how we intervene in aging brain physiology.