I research the use of machine learning to facilitate interpretable causal inference, with a particular focus on medical applications. My recent work includes estimating dynamic treatment regimes for ICU patients, developing a matching based method for observational causal inference, and reviewing machine learning in polymer biomaterials. My collaborations span statistics, computer science, medicine, and chemistry.

Papers

Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, and Alexander Volfovsky. Estimating trustworthy and safe optimal treatment regimes, 2023. [Accepted to AISTATS 2024] Link

Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, and David Page. Variable importance matching for causal inference. In Uncertainty in Artificial Intelligence, pages 1174–1184. PMLR, 2023. Link

Samantha M McDonald, Emily K Augustine, Quinn Lanners, Cynthia Rudin, L Catherine Brinson, and Matthew L Becker. Applied machine learning as a driver for polymeric biomaterials design. Nature Communications, 14(1):4838, Link

Quinn Lanners and Thomas Laurent. Neural machine translation. Honors thesis, Loyola Marymount University, 2019. Link


Conference Presentations

Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, and David Page. Variable importance matching for causal inference. Presented at INFORMS Annual Meeting in Phoenix, AZ, USA, 2023.

Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, and David Page. Matching using feature importance: An auditable approach to causal inference. Presented at the 9th International Conference of Computational Social Science in Copenhagen, Denmark, 2023.

Quinn Lanners Neural machine translation. Presented at Optum/UHG/UHC Analytics Conference in Eden Prairie, MN, USA, 2019.

Quinn Lanners and Lambert Doezema. The current state of atmospheric gas concentrations in california - as observed through data collected through nasa’s student airborne research program. Presented at the Southern California Conferences for Undergraduate Research at California State Polytechnic University, Pomona, 2017.


Articles

Neural Machine Translation: A guide to Neural Machine Translation using an Encoder Decoder structure with attention. Link

Deploying a Scikit-Learn Model on AWS Using SKLearn Estimators, Local Jupyter Notebooks, and the Terminal. Link

Choosing a Scikit-learn Linear Regression Algorithm. Link

Why You Should Know How to Deploy Your Models in the Cloud. Link