Ville Satopaa

Ville Satopaa is an Assistant Professor of Technology and Operations Management at INSEAD. His current research explores different areas of forecasting: judgmental and statistical forecasting, modeling crowdsourced predictions, combining and evaluating different predictions, and information elicitation. It involves developing general theory and methodology but also specific projects that analyse real-world data. In addition to working on forecasting, Ville has experience in applying Bayesian statistics to rank hospitals in terms of disease-specific mortality rates.

Before joining INSEAD, Ville received his MA/PhD degrees in Statistics from the Wharton School of the University of Pennsylvania. He also holds a BA in Mathematics and Computer Sciences from Williams College.

At INSEAD Ville teaches Business Model Analysis & Innovation (MBA), Bayesian Analysis (Ph.D), Discrete Stochastic Processes (Ph.D.), and Artificial Intelligence in Business (Executive Ed.). He has also co-developed an online course called Transforming Business with Artificial Intelligence (Executive Ed.).

Ville has several research papers published in the top statistics journals (e.g., the Journal of American Statistical Association and Annals of Applied Statistics), top management journals (e.g., Management Science and Operations Research), and top field journals (e.g., International Journal of Forecasting and Health Services Research). His research has been acknowledged with various awards, including winning the Section on Bayesian Statistical Science Student Paper Competition in 2015 and being selected as a runner-up for Decision Analysis Society (DAS) 2020 Student Paper Award and as a finalist for the Best Paper Award by the 2020 INFORMS Workshop on Data Mining and Decision Analytics. In the MBA programme, he has received the Deans' Commendation for Excellence in Teaching multiple times and has also won the Best Teacher Award.

RESEARCH AREAS

Forecasting, Probability Modeling, Bayesian Statistics

TEACHING AREAS

Operations Management, Discrete Stochastic Processes