Andrew Walther is a Biostatistics PhD candidate at UNC Chapel Hill (expected graduation 2026), where his dissertation focuses on efficient estimation of interventions for spatial cluster randomized trials with spatial dependence and spillover effects. His research combines rigorous statistical methodology with practical applications in precision oncology and pharmaceutical data science.

As a Graduate Research Assistant in Data Science at UNC (2021-2024), Andrew developed machine learning algorithms to optimize therapeutic selection for colorectal cancer treatments using genomic and demographic data. This work exemplifies his ability to bridge statistical theory and translational medicine, creating tools that directly inform clinical decision-making.

Andrew brings extensive industry experience through multiple high-impact internships. Most recently, he completed a Data Scientist internship at Google (Summer 2025), where he developed ML enhancements for Google Play monetary fraud detection. At Chiesi USA (Spring 2025), he served as a Biostatistician performing exploratory analysis of the CDC WONDER database and developing longitudinal models to evaluate trajectory differences. His two internships at Gilead Sciences showcase his versatility: as a Data Scientist (Summer 2024), he engineered a reinforcement learning content selection enhancement for pharmaceutical marketing, and as a Biostatistician (Summer 2023), he developed biomarker-based prediction models using clinical and proteomic data to estimate disease severity of Idiopathic Pulmonary Fibrosis.

Andrew earned his MS in Biostatistics from UNC Chapel Hill (2020-2023) and holds a BS in Mathematics & Physics from Creighton University (double major). At Creighton, he was actively involved in leadership and research, serving as Treasurer of the Society of Physics Students (2019-2020) and Student Ambassador for the Creighton Center for Undergraduate Research & Scholarship (2018-2020). He achieved Dean’s List honors for all eight semesters of his undergraduate career (Fall 2016 - Spring 2020), with semester GPAs of at least 3.5.

In 2020, Andrew was selected as an NIEHS Environmental Biostatistics Trainee at UNC, a prestigious honor that supports his training at the intersection of environmental health and biostatistics. He has also served as a Biostatistics Teaching Assistant at UNC (Fall 2022), teaching Introduction to Statistical Computing and Data Management.

Andrew’s technical skills span Python, R, SQL, and data science methods, with particular expertise in machine learning, technical writing, and cross-disciplinary collaboration. He is motivated to address exciting and meaningful problems across healthcare, technology, and pharmaceutical domains, bringing a unique combination of theoretical rigor and practical industry experience to his research.