Quinn Lanners

About Quinn Lanners

I am a Biostatistics & Bioinformatics Ph.D. candidate at Duke University working with Profs. Cynthia Rudin, David Page, and Alexander Volfovsky. I research the use of machine learning to facilitate interpretable causal inference, with a particular focus on developing methods to handle the increasing complexity of real-world data. My recent work includes estimating dynamic treatment regimes for ICU patients, developing an interpretable method for observational causal inference, and creating a variant of multilabel learning to improve the prediction of rare clinical events. My collaborations span statistics, computer science, medicine, and chemistry. In Summer 2024, I interned as a Research Scientist at Meta where I worked on a domain-adapted model-based approach for offline counterfactual evaluation of ads ranking models. Before starting my Ph.D, I worked as a data scientist at Optum. I functioned as an end-to-end machine learning expert where I built and deployed machine learning models and automated the development team\'s deployment pipeline. I worked with Prof. Thomas Laurent on deep learning as an undergraduate at Loyola Marymount University. My professional and academic background makes me especially adept at understanding complex concepts and problems and implementing practical solutions across multiple disciplines.

Experience

1. Research Scientist

   Upstart

   Jun 2025 — Present · Seattle, WA, US

   Conducting research to improve targeting in direct mail marketing using machine learning and causal inference.

Education

• Loyola Marymount University

  Bachelor’s Degree, Major - Applied Mathematics, Minor - Biochemistry

  2015 — 2019

• Duke University School of Medicine

  Doctor of Philosophy - PhD, Biostatistics

• Chanhassen High School

  High School, General Studies

  2011 — 2015

Skills

• Spss
• Customer Service
• Communication
• Matlab
• Research
• At Multitasking
• Team Leadership
• Marketing
• Social Media
• Science
• Mathematics
• Teamwork
• Python
• Chemistry
• Collaborative Problem Solving
• Leadership
• Microsoft Office