Learning (Discrete) Optimization
The interaction between Machine Learning and Mathematical Optimization is currently one of the most popular topics at the intersection of Computer Science and Applied Mathematics. While the role of Continuous Optimization within Machine Learning is well known, and, on the applied side, it is rather easy to name areas in which data-driven Optimization boosted by / paired with Machine Learning algorithms can have a game-changing impact, the relationship and the interaction between Machine Learning and Discrete Optimization is largely unexplored and this project concerns one aspect of it, namely the use of modern Machine Learning techniques within / for Discrete Optimization.
Andrea Lodi received the PhD in System Engineering from the University of Bologna in 2000 and he has been Herman Goldstine Fellow at the IBM TJ Watson, NY in 2005–2006. He has been full professor of Operations Research at DEI, University of Bologna between 2007 and 2015. Since 2015, he is Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at the École Polytechnique de Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science and his work has received several recognitions including the IBM and Google faculty awards. He is the co-principal investigator (together with Yoshua Bengio) of the project "Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution", recently generously funded by the Canadian Federal Government under the CFREF Programme.