EMANUELE BORGONOVO

My teaching concerns courses in quantitative methods at the interface between mathematics and computer science at all levels. I have led the construction and  direction of the Bachelor in Economics Management and Computer Science, the degree that has tied the knot between these important disciplines at Bocconi University. I am responsible for the courses Mathematics I at the BEMACS degree, Fundamentals of Business Analytics Course at the MSc’s in Management and International Management. Starting this year, I will teach the new course “Design and Analysis of Computer Experiments” at the PhD in Statistics and Computer Science. At the Doctorate in Business Administration of SDA Bocconi business school, I teach the course “Machine Learning for Business Analytics”.
I won the 2020 Teaching Innovation Award of Bocconi University.

Full Professor, Director of the Department of Decision Sciences.

Co-editor-in-Chief of the European Journal of Operational Research, President 2020-2022 of the Decision Analysis Society of INFORMS, member of the Scientific Committee of the Silvio Tronchetti Provera Foundation.

PhD at the Massachusetts Institute of Technology. MSc in Nuclear Engineering with core in Mathematics and Physics from Politecnico di Milano.

Recipient of several national and international awards, my research is at the basis of new methods for sensitivity analysis in fields ranging from Machine Learning to risk assessment.

Click here to find our recent subroutine for Mikado plots, a visualization tool for second order interactions: https://github.com/emanueleborgonovo/MikadoPlots

Click here for the webinar series of the Decision Analysis Society of INFORMS:

https://connect.informs.org/das/events/webinars

Click here for the book on Sensitivity Analysis

https://www.springer.com/gp/book/9783319522579

Click here for the book on Managers and Numbers

http://www.egeaeditore.it/ita/prodotti/matematica-statistica-demografia…

My research focuses on methods and models for informing a decision-making process. I have developed the Differential Importance Measures, inserted since 2002 in the “NASA Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners”, the  d-importance measure, the finite change sensitivity indices and other methods aimed at improving the palette of tools for the sensitivity analysis of computer codes. I am working on new notions such as information density, on new feature importance measures for applications in machine learning, on mathematical methods for trend analysis and interaction quantification. For input-output mappings with multivariate responses, I am studying the extension of probabilistic sensitivity measures through the combination with optimal transport theory, a connection that leads to a novel and elegant framework. The applications of his methods range from business plans, to risk assessment models used for space risk analysis at NASA, to image datasets of artificial intelligence, to simulators used in hydrology for precipitation forecasting.