◉ Built the sensors & wearables program in partnership with product to remotely assess and monitor sleep, mobility, cognition, anxiety, depression, and fatigue in people living with MS. Selected and implemented first wearable device, patient reported outcomes and cognitive assessments:◉ Led data science efforts to identify, research, and test over 50 wearable devices, patient reported outcome measures, and cognitive testing suites to select scientifically sound and clinically relevant solutions. ◉ Collaborated closely with product, design, nurse care partners, and neurologists to build integrated remote patient monitoring that fills current gaps in MS care.◉ Spearheaded data acquisition of selected wearable device in 55 users, including proof of concept study of the device plus patient reported outcomes in 15 people living with MS.◉ Analyzed proof of concept data from selected wearable device to determine important features and develop algorithms to signal needed intervention from a nurse care partner.

◉ Guided and executed analyses to build new cognitive exams, including: iterating on exam design, collecting normative data, identifying outliers, scoring, and validating in clinical populations. ◉ Provided production backend code in Python on AWS for scoring cognitive exams.◉ Collaborated with clinical, product, UX/UI, and commercial teams to deliver data and insights needed for grants, clinical sites, users, and business partners.

◉ Built a prediction algorithm for sleep apnea, using features suitable for a wearable device.◉ Selected and engineered features from the combination of two overnight sleep studies (22,000+ subjects). Predicted sleep apnea using a logistic regression model in scikit-learn to achieve a recall rate of 81%.◉ Launched a web application using Dash and AWS to predict sleep apnea from user input.

◉ Created a signal processing pipeline of EEG time series data in R, Python, and Matlab to detect and quantify discrete sleep events.◉ Analyzed overnight sleep EEG recordings to investigate the relationship between sleep and overnight changes in health metrics using linear and logistic lasso regression.◉ Spearheaded a lab visit from Girls Inc. where 14 middle school girls learned about sleep and the brain via handling sheep brains, being wired up with EEG electrodes, and observing an MRI brain scan.

◉ Developed a new method to separate burst from suppression in EEG recordings of anesthesia using PCA and K-means clustering. Analyses performed in Matlab and R.◉ Discovered that a traditional marker used to determine if a patient is deeply anesthetized is misleading. Provides important clinical implications for anesthetic levels received in surgery.

◉ Characterized how two parts of the brain--the cortex & thalamus--interact during sleep using computational modeling and signal processing of recordings from patients with epilepsy. ◉ Awarded a NSF Graduate Research Fellowship, a Chateaubriand Fellowship, and a Fulbright to France (declined). ◉ Fostered a collaboration with labs in Marseille and Lyon, France to obtain and analyze these brain data, which resulted in 4 publications.

Assessed biomarkers and cognitive performance in schizophrenia using EEG, fMRI, and MRI