Michele Peruzzi

Overview

Michele Peruzzi is an Assistant Professor of Biostatistics at the University of Michigan–Ann Arbor's School of Public Health. His research focuses on Bayesian methods for spatial and multivariate data, particularly in environmental health applications, high-resolution sensor data, and scalable statistical computing. He develops software tools like the meshed and spamtree R packages to address computational challenges in large-scale analyses.

Education

• PhD in Statistics, Università Bocconi (2019)
• MSc in Economic and Social Sciences, Università Bocconi (2013)
• BSc in Economic and Social Sciences, Università Bocconi (2010)

Research Focus

Peruzzi's work addresses three core areas: (1) scalable Bayesian geostatistical methods for environmental sensors (e.g., satellite imaging, air quality), (2) response surface modeling for large health datasets, and (3) climate change impacts on ecosystems and human health. His methods emphasize computational efficiency and reproducibility.

Key Contributions

• Developed meshed Gaussian processes for partitioned domain analysis
• Pioneered spatial multivariate trees for big data regression
• Advanced phenology modeling via satellite data for continental-scale vegetation analysis

Affiliations

Based in Ann Arbor, MI, Peruzzi holds office in SPH II M4531. His work intersects statistics, environmental science, and public health, with applications in climate change mitigation and precision medicine.