Rachael V. Phillips is a Senior Data Analyst at CTML. She has a PhD in Biostatistics, MA in Biostatistics, BS in Biology, and BA in Mathematics. Her primary PhD advisor was Mark van der Laan. She also received significant mentorship from Susan Gruber, Alan Hubbard, and Romain Pirracchio. Broadly, Rachael's research integrates targeted learning and causal inference to realistically approximate answers to causal questions with statistical confidence. Motivated by issues arising in healthcare, the research projects she's pursued include the development of clinical algorithm frameworks and guidelines, and real-world data analysis methodologies for generating and evaluating real-world evidence (RWE). She was recently awarded the Chin Long Chiang Award for Excellence in Biostatistics Research for "exemplary dissertation research and scholarship". At CTML, Rachael collaborates with pharmaceutical, regulatory, clinical, and academic researchers (e.g., at Novo Nordisk, the FDA, and UC San Francisco Medical Center). She is also on the core development team of the tlverse open-source software project, with major contributions to the super learner (sl3), highly adaptive lasso (hal9001), and cross-validation (origami) packages.
Targeted Learning, Causal Inference, Machine Learning, Semiparametric Statistics, Real-world Evidence, Federated Learning, Open-source Software, Human-Computer Interaction, Statistical Analysis Pre-specification and Automation
