Quinn Lanners
Senior Research Scientist at Upstart
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolQuinn 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 MedicineHigh School, General Studies, High School, General Studies at Chanhassen High SchoolBachelor’s Degree, Major - Applied Mathematics, Minor - Biochemistry, Bachelor’s Degree, Major - Applied Mathematics, Minor - Biochemistry at Loyola Marymount University
English
Stackoverflow
46reputation
2kreached
5answers
0questions
Github Skills (15)
keras6
pandas6
flask-sqlalchemy6
csv6
lstm6
selenium6
python6
jinja26
flask6
mlmodel2
django1
machine-learning1
postgresql1
econometrics1
scikit-learn1
Programming languages (4)
Github contributions (5)
qlanners/nmt_tutorial
Jun 2019 - Jan 2021
Contributions:22 commits, 21 pushes, 1 branch in 1 year 7 months
qlanners/mlb_salary_comp
Aug 2019 - Jul 2020
Using machine learning and econometrics to compare salary data among MLB players
Contributions:28 PRs, 41 pushes, 21 branches in 11 months
econometricsmachine-learning
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