Giulia’s main research interests are in the integration of mathematical optimization and machine learning. Her doctoral research is focused on exploring some of the opportunities this synergy offers, with the aim of tightly integrating the learning process with the optimization one. Various are the decision-making situations that could benefit from a learning-based approach, especially in highly heuristic optimization frameworks such as the Mixed-Integer Programming one.
Giulia’s recent and current projects include a classification question on Mixed-Integer Quadratic Programs and a learning approach for tackling decisions in the branch-and-bound framework.
P. Bonami, A. Lodi, G. Zarpellon, 2017. Learning a Classification of Mixed-Integer Quadratic Programming Problems. in W.J. Van Hoeuve, Ed., Integration of Constraint Programming, Artificial Intelligence, and Operations Research – CPAIOR 2018, Lecture Notes in Computer Science 10848, Springer-Verlag, Berlin Heidelberg, 2018, 595-604.
A. Lodi, G. Zarpellon. 2017. On learning and branching: a survey. Volume 25, Issue 2, pp207-236
G. Zarpellon, M. Fischetti, A. Lodi, 2018. Learning MILP Resolution Outcomes Before Reaching Time-Limit. DS4DM-2018-009