Regina Burachik
University of South Australia

Short Bio: Regina Burachik is an Associate Professor for University of South Australia. She publishes extensively in nonsmooth/convex/nonconvex optimization, maximally monotone operators, and set-valued analysis. She co-authored the Springer research-level book: ”Set-Valued Analysis and Monotone Mappings”. Her interest is in theoretical as well as practical aspects of optimization, including variational inequalities, and convex and nonconvex duality theory. She is associate editor of many international journals, including SIOPT, JOTA, Optimization letters, and Set Valued Analysis. She is also frequently acting as guest editor for special issues/volumes. Regina regularly gives keynotes/plenary talks at national and international conferences. Regina likes to promote mathematics and actively mentors younger researchers.

Jacques Desrosiers
HEC Montréal

Title: Cycles, Pricing, and Pivots

Abstract: It is known that all directed cycles necessary to reach an optimal network flow solution are observed on the so-called residual network. Each of these accommodates positive flow values and forms a direction. A degenerate pivot is induced when the selected cycle in fact does not exist. The concepts of cycles can be transferred to linear programs and alternative necessary and sufficient optimality conditions are expressed on the linear programming residual problem. We propose a family of algorithms with non-degenerate pivots and also show that the local search heuristics for vehicle routing problems, such as 2-opt, 3-opt, swap, relocate… are indeed directed cycles on the (appropriate!) residual network.

Short Bio: Jacques Desrosiers received his Ph.D. degree in Mathematics from the University of Montréal in 1979. Since 1989 he is Full Professor in the Department of Decision Sciences at HEC Montréal. He is also a member of the GERAD Operations Research Center. His main research interests include large scale optimization for vehicle routing and crew scheduling in air, rail, and urban transportation.

Mirjam Dür
University of Augsburg

Short Bio: Mirjam Dür was born in Vienna, Austria, where she received a M.Sc. degree in Mathematics from the University of Vienna in 1996. She received a PhD in applied mathematics from University of Trier in 1999. After that, she worked as an assistant professor at Vienna University of Economics and Business Administration, as a junior professor at TU Darmstadt, as an Universitair Docent at the University of Groningen, The Netherlands, and as a professor of Nonlinear Optimization in Trier.
Since October 2017, she is a professor of Mathematical Optimization in Augsburg, Germany.
Her expertise includes all aspects of mathematical optimization, mostly in continuous and combinatorial optimization, optimization over convex cones and in particular optimization over the cone of copositive matrices which establishes interesting links between continuous and discrete optimization. She is especially interested in global optimization, i.e. algorithmic solution methods which avoid computing local optima but provide global optima instead.

Ivana Ljubic
ESSEC Business School of Paris

Short Bio: Ivana Ljubic is Professor of Operations Research at the ESSEC Business School of Paris. Prior to joining ESSEC in 2015, she was appointed at the University of Vienna, where she also received her habilitation in Operations Research in 2013. Ivana Ljubic holds a PhD degree in computer science from the Vienna University of Technology (2004) and a master's degree in mathematics from the University of Belgrade (2000).
Research interests of Ivana Ljubic include combinatorial optimization, optimization under uncertainty, bilevel optimization, with applications in telecommunications, logistics, network design or bioinformatics. She is a member of Editorial Board of European Journal of Operational Research, Computers and Operations Research and Journal of Global Optimization, and an Associate Editor of Omega. She currently serves as a chair of the INFORMS Telecommunication and Network Analytics Section.

Melvyn Sim
National University of Singapore

Title: The Dao of Robustness

Abstract: We propose a framework for optimization under uncertainty that we call robustness optimization, which is similar in purpose to, but philosophically different from, robust optimization. Unlike robust optimization approaches, we do not restrict nature to an uncertainty set but allow her to take its cause and even render solutions infeasible. Among these solutions, we favor those with the least adversarial impact on the model under uncertainty. Moreover, the decision maker does not have to size the uncertainty set, but instead specifies an acceptable target, or loss of optimality compared to the baseline model, as a tradeoff for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with the robustness optimization, termed as the adversarial impact measure, which relates to the maximum level of model infeasibility that may occur relative to the magnitude of deviation from the baseline uncertainty. We also provide a representation theorem of the decision criterion and uncover different types of adversarial impact measures. Similar to robust optimization, we show that robustness optimization via minimizing the adversarial impact can also be done in a tractable way, i.e., it preserves the complexity of the underlying problems including, inter alia, linear, discrete, data-driven and dynamic optimization problems. We also provide computational studies to show that for the same price of robustness, the solutions to our robustness optimization models can withstand greater impact of uncertainty compared to classical robust optimization models, and doing so without incurring additional computational effort. This is a joint work with Daniel Long and Mingloing Zhou.

Short Bio: Dr. Melvyn Sim is Professor and Provost's Chair at the Department of Analytics & Operations, NUS Business school. His research interests fall broadly under the categories of decision making and optimization under uncertainty with applications ranging from finance, supply chain management, healthcare to engineered systems. He is one of the active proponents of robust optimization and has given invited talks in this field at international conferences. Dr. Sim won second places in the 2002 and 2004 George Nicholson best student paper competition and first place in the 2007 Junior Faculty Interest Group (JFIG) best paper competition. He serves as an associate editor for Operations Research, Management Science, Mathematical Programming Computations and INFORMS Journal on Optimization.

Marc Teboulle
Tel Aviv University

Short Bio: Marc Teboulle is the Eric & Sheila Samson Chair and Professor of Optimization at the School of Mathematical Sciences of Tel Aviv University. He received his D.Sc. from the Technion, Israel Institute of Technology, and has held a position of Applied Mathematician at Israel Aircraft Industries, and academic appointments at Dalhousie University, and the University of Maryland.
His research interests are in the area of continuous optimization, including theory, algorithms, and its applications to many areas of science and engineering. He currently serves on the editorial board of SIAM J. Optimization, the Journal of Optimization Theory and Applications, SIAM Mathematics of Data Science. He is the Corresponding Editor for ESMAI-Control, Optimization and Calculus of Variations, and has previously served a Area Editor of Continuous Optimization for Mathematics of Operations Research. He is a SIAM Fellow.