Biography
Michele Samorani is a fifth-year doctoral student in Operations and Information Management at the Leeds School of Business. Before joining the program, he earned a Bachelors and a Masters degree in Computer Science from University of Bologna.
Michele’s research focuses on the use of business intelligence and data mining techniques to improve business processes in health care. In his dissertation, these techniques are used to improve the drug discovery process of a pharmaceutical company, the appointment scheduling of an outpatient mental health clinic, and the real-time decision making of outpatient clinics. His work on drug discovery won the INFORMS Data Mining Best Student Paper Award in 2009.
His research is published (or accepted for publication) in Decision Support Systems and INFORMS Journal on Computing.
Publications
Article
June 2010
Authors: Marco Better, Fred Glover, and Michele Samorani
Two-group classification is a key task in decision making and data mining applications. We introduce two new mixed integer programming formulations that make use of multiple separating hyperplanes.
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Article
May 2010
Author: Michele Samorani, Manuel Laguna, et al.
This work extends the propositionalization approach recently proposed for multi-relational data mining in two ways: it generates expressive attributes exhaustively and it uses randomization to sample a limited set of complex (“deep”) attributes.
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Article
January 2011
Author: Michele Samorani and Manuel Laguna
Metaheuristic approaches based on neighborhood search escape local optimality by applying predefined rules and constraints, such as tabu restrictions (in tabu search), acceptance criteria (in simulated annealing), and shaking (in VNS).
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