Experience

For more details, please see my CV.

Edwards Lifesciences

Consultant: Machine Learning (June 2022 - Present) – Irvine, CA

Research and development team in the transcatheter heart valve (THV) division.

• Prototyping ML-driven tools and dashboards for patient recruitment logistics and retention.

• Building interpretable clinical prediction models to predict and prevent adverse events.

• Investigating the use of machine learning-generated synthetic patient data to improve predictive model performance and statistical inference for potential gains in trial diversity and cost savings

• Developed a quantifiable, data-driven protocol to evaluate potential synthetic data partners with Edwards

Machine Learning Intern (June 2021 - Sept. 2021) – Irvine, CA

Worked in the Transcatheter Aortic Valve Replacement (TAVR) Global Data Science Team developing interpretable machine learning methods for cardiology applications.

• Conceived, developed, and pitched to senior leadership a machine learning patient matching algorithm and dashboard for TAVR precision medicine. The product was a top 5 finalist in a company-wide “shark tank” competition and was presented to the senior executives including the CEO.

• Conceived and developed an unsupervised clustering framework for patient phenotyping related to adverse events.

Tools: Python, R

University of Southern California

Research Assistant, Data Science (March 2019 - June 2022) – Los Angeles, CA

Consulting with the USC Department of Health Economics, Schaeffer Center, and the Keck School of Medicine applying machine learning and causal inference to explore and evaluate potential treatments for cardiovascular and respiratory disease.

• Developed NIH R21 Grant Proposal: evaluating the efficacy and predicting outcomes of continuous inotropic therapy for end-stage heart failure in the U.S.

• Advised 6 USC Health Economics PhD students on their cost-effectiveness analyses surrounding AI applications in healthcare. Supervised an undergraduate student on data cleaning, analysis, and manuscript development.

Tools: R, Python, SQL, SAS

Stanford University

Research Assistant, Data Science – Priest Lab (May 2019 - Jun 2021) – Palo Alto, CA

Collaborating with cardiologist Dr. James Priest, M.D. to analyze and model the impacts of cardiovascular disease and related surgery on pulmonary and neurological function

Paper published in leading cardiovascular journal JAHA (second author) and another manuscript in development (first author)

Tools: R, Python (Sci-kit Learn, Pandas)

Research Assistant, Data Science – QSU (Jun 2018 - Jun 2021) – Palo Alto, CA

Consulting with clinicians from the School of Medicine through the Quantitative Sciences Unit (QSU) working on multiple projects and manuscripts across various medical disciplines

Tools: Python (Sci-kit Learn, Pandas, Matplotlib), R

MarketPsych

Data Science Intern (March 2018 - April 2019) – San Luis Obispo, CA

Research into cryptocurrency trading strategies for a finance sentiment data aggregation startup.

• Quantitative analysis of BTC trading patterns based on news and social media NLP using Matplotlib, Pandas, and Numpy.
• Development of sentiment analysis-based bitcoin (BTC) trading algorithms using random forests

Glynt.ai

Data Science Intern (March 2017 - Sep 2017) – Mountain View, CA

Data cleaning, validation, and analysis for SaaS startup that aggregates utility consumer data and trends with Python.