Summary
Quinn Lanners is a Senior Research Scientist and Biostatistics PhD with eight years of experience applying interpretable machine learning and causal inference to high-stakes domains like healthcare and advertising. Their doctoral work at Duke produced scalable, interpretable methods that sped up causal analysis by 100x and reduced adverse ICU events by over 20 percentage points when operationalized. Quinn has translated research into practice at Meta (offline counterfactual evaluation for ranking) and Optum (production multimodal time-series and deployment automation), bridging theoretical rigor with end-to-end engineering. Now at Upstart, they focus on improving targeting through causal ML while maintaining a strong interdisciplinary collaboration record across statistics, CS, medicine, and chemistry. Comfortable moving from PySpark pipelines and Kubernetes deployment to provable model behavior, Quinn combines deep technical breadth with an uncommon track record of turning interpretable methods into real-world impact.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Biostatistics, Doctor of Philosophy - PhD, Biostatistics at Duke University School of Medicine
High School, General Studies, High School, General Studies at Chanhassen High School
Bachelor’s Degree, Major - Applied Mathematics, Minor - Biochemistry, Bachelor’s Degree, Major - Applied Mathematics, Minor - Biochemistry at Loyola Marymount University
English