I am a biosignal researcher interested in the intersection of data science and wearable sensing to uncover users' intentions and behavioral states using real-world human biosignal recordings. I have 7 years expertise in non-invasive human biosignals, motor control, and human-computer interaction, having led hands-on research studies with 50+ human users, written 9 first-author, peer-reviewed publications in wearable biosensing, and mentored 8 researchers. I am skilled in quantitative techniques including machine learning (TensorFlow/PyTorch), signal processing for multidimensional time series data, and scalable Python workflows for analyzing large datasets. I am currently working as a contract research analyst at Meta Reality Labs Research.

Previously, I worked as a postdoctoral researcher at the University of Washington, co-advised by Bing Brunton and Raj Rao, developing generalizable machine learning models that could decode multidimensional neural time series data (recorded via electrocorticography) during naturalistic arm movements. For my PhD, I designed experiments and denoised neural time series data (EEG, EMG) during impaired balance control induced by a virtual reality headset in the lab of Dan Ferris.

Outside of work, I am an avid hiker/backpacker who enjoys rock climbing and science fiction. If you are interested in contacting me, please send me an email.