Ville Satop\u00e4\u00e4 is an Assistant Professor of Technology and Operations Management at INSEAD. Before joining INSEAD, Ville received his MA and Ph.D. 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.<\/p>\n
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In terms of research, Ville is an applied Bayesian statistician. His research explores different areas of forecasting: judgmental and statistical forecasting, modeling crowdsourced predictions, combining and evaluating different predictions, and information elicitation. This involves developing general theory and methodology but also specific projects that analyse real-world data, such as hospital mortality rates, domestic tourism, or urban crime.<\/p>\n
At INSEAD Ville teaches\u00a0Business Model Analysis & Innovation<\/em>\u00a0(MBA),\u00a0Bayesian Analysis\u00a0<\/em>(Ph.D),\u00a0Discrete Stochastic Processes<\/em>\u00a0(Ph.D.), and\u00a0Artificial Intelligence in Business\u00a0<\/em>(Executive Ed.). He has also co-developed an online course called\u00a0Transforming Business with Artificial Intelligence<\/em>\u00a0(Executive Ed.).<\/p>\n <\/p>\n Ville has several research papers published in the top statistics journals (e.g.,\u00a0the Journal of American Statistical Association<\/em>\u00a0and\u00a0Annals of Applied Statistics<\/em>), top management journals (e.g.,\u00a0Management Science\u00a0<\/em>and\u00a0Operations Research<\/em>), and top field journals (e.g.,\u00a0International Journal of Forecasting<\/em>\u00a0and\u00a0Health Services Research<\/em>). 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\u00a0Deans’ Commendation for Excellence in Teaching<\/em>\u00a0multiple times and has also won the\u00a0Best Teacher Award.<\/em><\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":" Estimates of\u00a0program\u00a0burden, i.e., the number of children suffering of severe malnutrition in a given region and period of time are critical to humanitarian relief\u00a0programs; yet no principled approach exists to estimate such numbers based on the kind of data typically available to humanitarian organizations. In this on-going collaboration with the\u00a0World\u00a0Food\u00a0Program, we develop a fundamentally new approach that can estimate burden and other important quantities from prevalence and admissions data alone. To demonstrate our approach, we estimate these quantities for each administrative district in Somalia and compare our results with past survey-based estimates.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]