WORK HISTORY

Jan 2018 – Current

Health Data Science Fellow

Insight Health Data Science

I am currently a Data Science Fellow at Insight, where I have been consulting with a Bay Area wearables startup to help improve product accuracy. See my demo!

• Employed PostgreSQL and SQLAlchemy to analyze clinical information from MIMIC-III database (>40k patients)
• Matched >6k arterial pulse waveform recordings to specific admissions in the database
• Extracted 14 clinically relevant features from waveform data using Python (SciPy, NumPy, Pandas)
• Achieved 70% precision/recall with random forest classification predicting age group based on arterial waveform

Sept 2011 – Oct 2017

Graduate Student, Neuroscience Department

University of California, San Francisco

I moved to San Francisco to get my PhD in Neuroscience at UCSF, where I built an automated pipeline (Matlab/Igor Pro) for real-time neuronal classification from electrophysiological features – I later created a Python package to replicate the classification/analysis phase of this pipeline (NeuroSpyke).

• Achieved > 80% precision using discriminant analysis to classify neuronal subtypes based on electrophysiological features, resulting in first author publication
• Integrated electrophysiological and imaging data from heterogeneous data sources, automating the experimental process to increase efficiency by >200%
• Performed simultaneous electrophysiological recordings and calcium imaging using two-photon microscopy to achieve novel insights into subcellular neuronal processes

EDUCATION

2017

PhD in Neuroscience

University of California, San Francisco

Awarded National Science Foundation (NSF) Graduate Research Fellowship

2011

BS in Neuroscience

Brown University

Magna cum laude, Elected to Sigma Xi, the Scientific Research Society