Current role
Statistician III
University of Wisconsin–Madison
School of Medicine and Public Health
About / Jonah Amponsah
A quantitative lens.
A human purpose.
I’m Jonah, a statistician working at the intersection of methods, implementation, and health.
My work connects statistical thinking and machine learning with practical questions in clinical prediction and healthcare delivery. I’m interested in how we can make models more interpretable, evaluate them rigorously, and use evidence to inform better decisions.
Across explainable AI, cancer care, and population health, I bring a focus on the question behind the analysis: what can we learn, and how can that knowledge improve practice?
Explore my research →
Background
Training & current role.
Education
Academic training
- PhD in BiostatisticsUniversity of Kansas Medical Center
- MS in Data ScienceSouth Dakota State University
- MS in StatisticsSouth Dakota State University
- MS in MathematicsYoungstown State University
- BS in Mathematics and StatisticsUniversity of Cape Coast
Dissertation: Using Explainability to Improve Predictive Modeling Performance
Expertise & research interests
Questions that connect.
Methodological rigor, interpretable evidence, and the translation of research into healthcare practice.
Methods
Explainable AI & Predictive Modeling
Developing interpretable prediction methods for rare outcomes, with a focus on SHAP-guided augmentation, class imbalance, and rigorous model evaluation.
SHAPrare eventssynthetic dataevaluation
Translation
Implementation Science
Connecting implementation questions to experimental designs and quantitative evaluation, with applications to testing workflows and evidence-based cancer care.
cluster trialsSMARTMOSThybrid designs
Applications
Cancer & Population Health
Examining genomic testing, geographic disparities, and population health needs to inform care delivery and the prioritization of intervention resources.
oncologygenomicshealth equityspatial methods
Scientific exchange