I worked as a research assistant at Harvard Medical School's Laboratory of Neuroscience at Boston VA Healthcare for 2 years, and at Columbia's Neural Acoustic Processing Lab since January 2024. 

At Harvard, I got to work directly with Principal Investigators across 5 different projects to better understand the role of specific brain features in regulating sleep and wakefulness, and ultimately find new clinical treatments for sleep disorders. The studies I worked on focused on analyzing how neural structures in the basal forebrain regulate sleep/wake cycles, investigating the role of specific GABA receptors in controlling sleep depth, and identifying the neural correlates of sleep homeostasis. Thanks to my mentors, I learned and taught a wide range of technical lab skills like stereotaxic surgery, opto/chemogenetic stimulation, microtome sectioning, immunohistochemistry, brain imaging, and sleep-scoring.

At Columbia, I worked on the auditory attention decoding problem, developing machine learning models to understand which sounds someone is paying attention to based on their EEG brainwaves. So far, I have worked on 2 different studies awaiting publication. I conducted 40+ biosignal recordings with participants using g.tec's EEG cap, built end-to-end neural signal processing pipelines from preprocessing and feature extraction to model evaluation, and successfully decoded auditory attention using the stimulus reconstruction approach. Finally, I iteratively improved our neural decoding model's performance by adding different biosignals like pupil dilation and skin conductance to its training data.

During my time at HMS, I worked on an automated sleep-scoring program that used a deep convolutional neural network to classify mouse EEG/EMG data into "Wake," "NREM," "REM," or "Artifact" categories. We used transfer learning of GoogleNet to reduce the time it takes to score a 24-hour sleep recording from about 6 hours to 20 minutes while performing at human-level accuracy. 

Here is a demo of the GUI I created for the initial program using the plotly python library: