Professor Roberto Cominetti, Universidad Adolfo Ibáñez, Chile

Roberto Cominetti obtained a degree in Mathematical Engineering from Universidad de Chile (1986) and holds a Ph.D. in Applied Mathematics from Université Blaise Pascal in France (1989).

His research interests are in the fields of convex optimization, transportation, and game theory. He has authored more than 50 research papers and one book, and has been plenary speaker at numerous international conferences.

In 2015, Cominetti was appointed as full professor with the Faculty of Engineering and Sciences at Universidad Adolfo Ibáñez. Previously he worked at Universidad de Chile, first with the Department of Mathematical Engineering and later at the Department of Industrial Engineering. For over 10 years he was a member of the Centro de Modelamiento Matemático. He has held short term visiting positions at universities in Brazil, Canada, France, Germany, Italy, Poland, Spain, Uruguay, and the USA.

Optimization and Games in Transportation

In this talk I will present a survey of some applications of optimization and game theory in equilibrium models for transportation systems. Starting from my early work on equilibrium flows for congested transit systems and some of the applications that these models have had along the years, I will move to more recent work on stochastic traffic equilibrium, risk-averse route choice, dynamic equilibrium, and the limiting behavior of the Price-of-Anarchy for highly congestion networks.

Professor Alejandro Jofré, Universidad de Chile, Chile

Professor Alejandro Jofré is Director of the Centre for Mathematical Modelling at University of Chile. He obtained the degree of Doctor and Habilitation on Applied Mathematics in France, and later postdoc at University of California. He held appointments as professor at the Universities of Paris 1-Sorbonne and University of California-Davis, and visiting professor at several universities and centres, including Ecole Polytechnique, Princeton U., U. Washington-Seattle, UC Davis, UBC-Vancouver, HP Palo Alto, Bonn and NUS-Singapore.

Professor Jofré has a record of around 50 publications covering areas in optimization, stochastic optimization, game theory, economic equilibrium and risk analysis, and electricity markets, and advised more than 20 PhDs and master theses. He has led more than twenty research projects and developed optimization tools for pricing, planning and market behaviour analysis for energy systems, risk analysis for network, telecommunication markets and sustainable exploitation of natural resources such as copper, energy and forestry. He has been plenary and invited speaker in several major international conferences such as International Conference on Stochastic Programming, Mathematical Programming, ICIAM and the Annual SIAM Conference in San Diego last year.

Professor Jofre is currently member of the Council of the Mathematical Optimization Society and member of the Scientific Board of three Research Centres/Initiatives of excellence in France, Japan and Ecuador. Furthermore, he is currently associate editor of eight mathematical and engineering journals, including Journal Optimization Theory and Application, Set-valued and Variational Analysis, Energy Systems, Optimization and Engineering, and recently joined Mathematical and Financial Economics and Journal of Industrial and Management Optimization. Finally, Professor Jofre is member of SIAM, Mathematical Optimization Society, Econometric Society, IEEE Society and the Economic Theory Society.

Stochastic optimization and game theory on energy markets

In this talk we develop a stochastic optimization-game theory model representing an energy market, which includes the transmission network and a few number of agents with oligopolistic behavior. We consider a general network with nonlinear externalities and nonlinear pricing rules.  As a tool for the modeling and analysis we also use mechanism design theory.

Professor Stephen Wright, University of Wisconsin-Madison, USA

Stephen J. Wright holds the George B. Dantzig Professorship, the Sheldon Lubar Chair, and the Amar and Balinder Sohi Professorship of Computer Sciences at the University of Wisconsin-Madison.

His research is in computational optimization and its applications to many areas of science and engineering. Prior to joining UW-Madison in 2001, Wright held positions at North Carolina State University (1986-90), Argonne National Laboratory (1990-2001), and the University of Chicago (2000-2001). He graduated with a Ph.D. from the University of Queensland in 1984. He has served as Chair of the Mathematical Optimization Society and as a Trustee of SIAM. He is a Fellow of SIAM. In 2014, he won the W.R.G. Baker award from IEEE.

Wright is the author/coauthor of widely used text/reference books in optimization including “Primal Dual Interior-Point Methods” and “Numerical Optimization“. He has published widely on optimization theory, algorithms, software, and applications.

Wright is current editor-in-chief of the SIAM Journal on Optimization and previously served as editor-in-chief or associate editor of Mathematical Programming (Series A), Mathematical Programming (Series B), SIAM Review, SIAM Journal on Scientific Computing, and several other journals and book series.

Some Optimization Problems in Electrical Power Systems

Electrical power grids are a rich source of problems in optimization and data analysis. This talk describes work on two such problems. In the first, we formulate a bilevel optimization problem to identify possible vulnerabilities by finding the attack that causes maximal disruption. In the second, we describe multivariate logistic regression (MLR) and deep learning approaches for identifying outages in a grid from real-time sensor network data. We show that when these classifiers are trained to recognize the “signature” of outages under a variety of network conditions, they can identify outages correctly in the vast majority of cases. An extension of our approach allows identification of optimal sensor locations.

Optimization in Data Analysis: Survey and Recent Developments

Optimization methodology has proved to be essential to formulating and solving problems in data analysis, machine learning, and computational statistics. Such problems are characterized by fairly elementary objective functions but a very large amount of data. Algorithms need to take account of the statistical / learning context, the expense of computing function and derivative information, nonsmoothness, and (increasingly) nonconvexity. The talk will sketch canonical problem formulations, fundamental algorithmic techniques, and issues of current research focus.