Full Professor

Scientifi c Manager and Coordinator of LIME, his areas of research activities and expertise are the design and development of computational models and numerical algorithms for the solution of decision making problems. Particular attention has been devoted to the development of (i) decision support systems and services for health care and clinical decision problems, (ii) optimization models for the effective and effi cient organization and management of health care services delivery. He has been involved in many research projects at national and international level. He is co-author of several papers in refereed journals on numerical methods for nonlinear optimization, computational engineering and parallel algorithms, and models and methods for the healthcare organization and management.

G. Ielpa, R. Guido, D. Conforti, Scheduling Outpatient Day Service Operations for Rheumatology Diseases, Flexible Services and Manufacturing Journal, online 8 May 2019.
L.F. Lucca, D. Lofaro, L. Pignolo, E. Leto, M. Ursino, M. D. Cortese, D. Conforti, P. Tonin, A. Cerasa, Outcome prediction in disorders of consciousness: the role of coma recovery scale revised, BMC Neurology (2019) 19:68.
R. Guido, M.C. Groccia, D. Conforti, An efficient matheuristic for offline patientto-bed assignment problems, European Journal of Operational Research 268(2), 486-503 (2018).
A. Cerasa, D. Lofaro, P. Cavedini, I. Martino, A. Bruni, A. Sarica, D. Mauro, G. Merante, I. Rossomanno, M. Rizzuto, A. Palmacci, B. Aquino, P. De Fazio, G.R. Perna, E. Vanni, G. Olivadese, D. Conforti, G. Arabia, A. Quattrone, Personality biomarkers of pathological gambling: A machine learning study Journal of Neuroscience Methods 294, 7-14 (2018)

Optimization Models and Methods for Planning and Management of Healthcare Services. 
Optimization Models and Methods for Healthcare and Clinical Decision Support. 
Healthcare and Clinical Decision Support Systems and Services. 
Optimization Models and Methods for Machine Learning Problems and Data Analysis.
Models, Methods and Applications of Integer Linear and Nonlinear Stochastic Programming. 
Sequential and Paralle Algorithms for Nonlinear Programming Problems. 
Optimization Models and Methods for the Management of Electric Energy. 
Curvilinear Trajectory Approaches for Unconstrained Optimization Problems. 
Automatic Differentiation Techniques for Nonlinear Programming Algorithms.

Several topics are currently available. Interested students are suggested to drop by during office hours.