School Lecturers

Marco Bee is associate professor in Economic Statistics at the University of Trento (Italy).
After spending one year as a visiting scholar at the Department of Mathematics of the Indiana University and receiving a Ph.D. in Theoretical Statistics from the University of Trento in 1998, from 1999 to 2005 he has held positions at the Risk Management department of Banca Intesa in Milan. His current research interests can be classified into two broad categories:
  1.  computational and applied statistics;
 2. quantitative finance and risk management, with particular emphasis on the computational issues often arising in this field.
His papers have been published in various important computational statistics, quantitative finance and applied economics journals, such as "Computational Statistics and Data Analysis", "Journal of Computational and Graphical Statistics", "Physical Review E", "Quantitative Finance", "Journal of Empirical Finance", "Insurance: Mathematics and Economics" and "Journal of Economic Dynamics and Control.
Diego Giuliani is Assistant Professor (in economic statistics) at the Department of Economics and Management of the University of Trento.
His main research interests broadly focus on the use and development of statistical methods to analyze micro-geographic data, with particular applications to regional science, economic geography, criminology and health economics. Diego is member of the research group STATA, Statistics: Theory and Applications, of the University of Trento and is affiliated to the Global Health section of the AidData Research Consortium.

Nikolaus Umlauf works as a post-doc researcher at the Department of
Statistics at the Universität Innsbruck.
His research focuses on complex (Bayesian) distributional regression models that can combine commonly used approaches for modeling highly nonlinear data with methods used in machine learning. The applications of this modeling framework are diverse, from economic problems to meteorological, medical and remote sensing, etc. He is co-author of the R package bamlss, the C++ library BayesX and its corresponding R interface package R2BayesX, as well as the the R
package exams.

Achim Zeileis is Professor of Statistics at the Faculty of Economics and
Statistics at Universität Innsbruck.
His research interests are at the intersection of classical parametric statistics and flexible statistical learning methods. Special focus is given to open-source software (typically in R) that ties the methods together and facilitates their application in practice with fields of interest ranging from psychology over economics to
He is co-editor-in-chief of the open-access Journal of Statistical Software, ordinary member of the R Foundation for Statistical Computing, and contributor to the open-source software repositories CRAN and R-Forge.

Maria Michela Dickson is Post-Doc researcher in Economic Statistics at
University of Trento. She held a PhD in Economic Statistics at University
“Sapienza”, Rome. Her research interests are in sampling methodologies and
estimation techniques for finite populations, with particular reference to
methods and algorithms for spatial data in economy, ecology and
criminology. She worked on several national and european projects as
statistic consultant, in collaboration with many Universities and research
centers. She is author of many publications on these topics and she is
referee for international journals in the field.
Flavio Santi, PhD - Research fellow at the Department of Economics and Management
of the University of Trento and the Institute of Statistics of the Autonomous
Province of Trento (ISPAT). He has recently contributed to two research projects of the University of Trento on economic development of Alpine regions and on the transition process to employment of University of Trento graduates. His research interests mainly focus on spatial statistics and spatial econometrics, especially from a methodological and a computational point of view. As a research fellow, he is currently working on record-linkage and data-mining techniques for the analysis of administrative data on the labour market of the province of Trento.



Scientific coordination

Organizing secretariat