My long-term goal is to understand intelligence; understanding its underlying principles would give us access to artificial intelligence (AI), and I believe that learning algorithms are essential in this quest. Learning algorithms could give computers the ability to capture operational knowledge (not necessarily in symbolic / verbal form) from examples.

My main research area is the application of operations research to transportation and telecommunication planning. A large portion of my work deals with the development of efficient metaheuristics for solving difficult problems in this area. As co-director of the Laboratory on Intelligent Transportation Systems of the Centre for Research on Transportation, I am also very interested in all real-time transportation planning problems.

My research interests focus on the development of metaheuristics, such as tabu search and genetic algorithms, for solving discrete optimization problems in the transportation domain. I am particularly interested in vehicle routing problems with different side constraints, like service time windows at customer locations. These problems can model many real-world applications such as distribution of goods by commercial vehicles, courier services, para-transit services, etc. I also study dynamic variants of these problems when customer requests dynamically occur over time and must be integrated in real-time into the current routes.

My research interests are centered around discovering fundamental computational principles that underlie the extraordinary capabilities to learn from the environment, understand it and adapt to it that characterize intelligence. The development of novel machine learning algorithms based on such principles, and trained on very large data sets, is at the heart of the latest technological breakthroughs in artificial intelligence.