Keynote Speakers

Fangzhen Lin

Fangzhen Lin is Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (Hong Kong). He received his Ph.D. in computer science from Stanford University, and before coming to Hong Kong, spent several years as a post-doctoral researcher at the University of Toronto. His main research area is
Knowledge Representation and Reasoning.
He received the Croucher Foundation Senior Research Fellowship award in 2006, a Distinguished Paper Award at IJCAI-1997, a Best Paper Award at KR-2000, an Outstanding Paper Honorable Mention at AAAI-2004, the Ray Reiter Best Paper award at KR-2006, and an Honorable Mention for his planner R at the AIPS-2000 planning competition. He is currently an Associate Editor and Chair of the Awards Committee of
Artificial Intelligence journal, and  had been on the Advisory Board of Journal of Artificial Intelligence Research. He was program co-chairs of KR 2010 and
LPNMR 2009, and has served on the program committees of numerous international
conferences in AI.  More information about him can be found on his web page.

Abstract of Professor Lin’s talk:

Title: From Satisfiability to Linear Algebra

Satisfiability of boolean formulas (SAT) is an interesting problem for many reasons. It was the first problem proved to be NP-complete by Cook. Efficient SAT solvers have many applications. In fact, there is a huge literature on SAT, and its connections with other optimization problems have been explored. In this talk, I discuss a way to map clauses to linear combinations, and sets of clauses to matrices. Through this mapping, satisfiability is related to linear programming, and resolution to matrix operations.


Pascal Van Hentenryck

Van Hentenryck leads the Optimization Research Group at NICTA, whose research focuses on optimization, algorithmic decision theory, logistics and supply chains, energy systems, and disaster management. He is also a professor at the University of Melbourne.

Van Hentenryck is the recipient of two Honorary degrees, the 2002 Informs ICS Award for research excellence at the intersection of operations research and computer, the 2006 ACP Award for research excellence in constraint programming, the 2010-2011 Philip J. Bray Award for Excellence in Undergraduate Teaching at Brown University. He is a 2013 IFORS distinguished lecturer and a fellow of the Association for the Advancement of Artifical Intelligence. Van Hentenryck is the author of five MIT Press books and has developed a number of innovative optimization systems that are widely used in academia and industry. His research on disaster management has been deployed to help federal agencies in the United States mitigate the effects of hurricanes on coastal areas.

Abstract of Professor Van Hentenryck’s talk:

Title: Computational Disaster Management

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Natural disasters have dramatic impacts on human lives and on the socio-economic welfare of entire regions; They are identified as one of the major risks of the East Asia and Pacific region. Dramatic events such as Hurricane Katrina and the Tohoku tsunami have also highlighted the need for decision-support tools in preparing, mitigating, responding, and recovering from disasters.

In this talk, I will present an overview of some recent progress in using optimization for disaster management and, in particular, in relief distribution, power system restoration, and evacuation planning and scheduling. I will argue that optimization has a significant role to play in all aspects of disaster management, from policy formulation to mitigation, operational response, and recovery, using examples of systems deployed during hurricanes Irene and Sandy. Moreover, I will indicate that disaster management raises significant computational challenges for AI technologies which must optimize over complex infrastructures in uncertain environments. Finally, I will conclude by identifying a number of fundamental research issues for AI in this space.