Keynote Speakers

Ovidiu Daescu (The University of Texas at Dallas, USA)
E-mail: [email protected]

Title:Facility location - beyond the classic generalized Fermat-Torricelli (Weber) problem

Abstract: One of the best known problems in facility location is the general version of the Fermat-Torricelli (Weber) problem, that asks to find a point that minimizes the sum of the distances to a finite number of given points in Rd, for some constant d. In this talk I will address recent results that generalize it further, from finding a point to finding a line segment, of special interest when the dimension d is low, i.e. d=2,3.

Bio: Dr. Ovidiu Daescu received the Engineer Diploma in Computer Science and Automation from the Technical Military Academy, Bucharest, Romania in 1991, and MS and PhD degrees in Computer Science and Engineering from the Department of Computer Science and Engineering, University of Notre Dame, IN, USA in 1997 and 2000, respectively. He joined the Department of Computer Science of the University of Texas at Dallas in 2000, where he currently is Professor and Interim Department Head. His ongoing research interests include algorithm engineering, computational and discrete geometry, spatial data structures, and bio-medical computing. He has published extensively in top venues and his research has been funded by various national, state and local, public and private agencies and corporations.

Guangmo (Amo) Tong (University of Delaware, USA)
E-mail: [email protected]

Title: Learning towards combinatorial decision making

Abstract: Real-world decision-making systems are often subject to uncertainties that have to be resolved through observational data. Therefore, we are frequently confronted with combinatorial optimization problems of which the objective function is unknown and thus must be debunked using empirical evidence. In contrast to the common practice that relies on a learning-and-optimization strategy, this talk will discuss the possibility of regression between combinatorial spaces, aiming to infer high-quality optimization solutions from samples of query-decision pairs, without the need to learn the objective function. I will first discuss how generic approaches may be designed with guaranteed performance and then demonstrate the practical feasibility through classic combinatorial optimization problems as well as applications in social contagion management.

Bio: Dr. Guangmo Tong is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware. He received a Ph.D. in Computer Science at the University of Texas at Dallas in 2018. He received his BS degree in Mathematics and Applied Mathematics from Beijing Institute of Technology in July 2013. His research interests include combinatorial optimization, machine learning, and computational social systems.