Stephen Salerno develops statistical methods, machine learning tools, and open-source software for prediction, inference, and decision-making in clinical and public health research. His work sits at the intersection of biostatistics, artificial intelligence, and public health, with a focus on making statistical and AI/ML methods more accessible, interpretable, and useful for population health.
Salerno’s research includes methods for drawing valid statistical inference from AI- and machine learning-generated outcomes, high-dimensional survival analysis, semi-competing risk prediction, selection bias, clinical quality measurement, and health policy. He applies these methods to cancer epidemiology, lung cancer survival, dialysis quality measures and other settings where complex data can inform care and public health systems.
Salerno earned his PhD and master of science in biostatistics from the University of Michigan and his bachelor’s degree in biometry and statistics from Cornell University. His dissertation developed deep learning methods for correlated survival endpoints, motivated by the Boston Lung Cancer Study. He completed postdoctoral training in biostatistics at the Fred Hutchinson Cancer Center. He also contributes to data science education and public-interest data initiatives, including Statistics in the Community.
Areas of focus:
- Biostatistics
- Artificial intelligence and machine learning
- High-dimensional survival analysis
- Clinical quality measurement and health policy
- Prediction methods
Featured publications
- ipd: an R package for conducting inference on predicted data
Bioinformatics
February 2025 - What’s the weight? Estimating controlled outcome differences in complex surveys for health disparities research
Statistics in Medicine
October 2025 - High‑dimensional survival analysis: methods and applications
Annual Review of Statistics and Its Application
October 2022