Daniel Gervini

Daniel Gervini is Professor and Data Science Undergraduate Coordinator in the Department of Mathematical Sciences at University of Wisconsin-Milwaukee. His research centers on functional data analysis, robust statistics, and point process modeling, with applications to spatial statistics and multivariate analysis.

Educational credentials include a Ph.D. in Mathematics from University of Buenos Aires (1999) and M.S. from the same institution (1995). He held postdoctoral positions at University of Zürich Biostatistics Department (2001-2004) and research fellowship at University of Buenos Aires (1996-1999).

Research develops novel methods for functional data including warped regression, outlier-resistant estimation, and point process modeling. Recent NSF-funded projects focus on spatio-temporal covariation in replicated point processes and statistical methods for point-process time series. Applications include bike-sharing demand analysis and neuroimaging.

Publication trends showcase progression from foundational robust functional methods to advanced spatio-temporal point process modeling. Recent work emphasizes computational statistics applications to urban systems and environmental data.

Develops extensive open-source statistical software packages in MATLAB for functional data analysis, point process modeling, and spatio-temporal prediction. Maintains active research blog on statistical analysis of COVID-19 pandemic data.