Most problems, from theoretical problems in combinatorics to real-world applications, comprise hidden structural properties not directly captured by the problem definition. A key to the recent progress in automated reasoning and combinatorial optimization has been to automatically uncover and exploit this hidden problem structure, resulting in a dramatic increase in the scale and complexity of the problems within our reach. The most complex tasks, however, still require human abilities and ingenuity. In this talk, I will show how we can leverage human insights to effectively complement and dramatically boost state-of-the-art optimization techniques. I will demonstrate the effectiveness of the approach with a series of scientific discoveries, from experimental designs to materials discovery.
Ronan Le Bras is a Ph.D. candidate in computer science at Cornell University. He received his M.S. and B.S. from Ecole Polytechnique Montreal in computer engineering and in software engineering. His research interests include computational methods for large-scale combinatorial optimization, reasoning, learning and human computation. His work is motivated by a range of applications, especially in the emerging field of computational sustainability. It has led to a series of scientific discoveries in areas such as graph theory, combinatorics, and discrepancy theory as well as materials science, experimental design and conservation biology. His work appears in the proceedings of AAAI, IJCAI, HCOMP, SAT, CP and VLDB.