Alexander Shapiro

Alexander Shapiro	Georgia Tech, USA
Alexander Shapiro is a Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He has published more than 130 research articles in peer review journals and is a coauthor of several books. His research is widely cited and he was listed as an “ISI Highly Cited Researcher” in 2004 (ISI = Institute for Scientific Information). He is on the editorial board of several professional journals, such as Mathematics of Operations Research, ESAIM: Control, Optimization and Calculus of Variations, Computational Management Science. He was an area editor (Optimization) of Operations Research, currently he is the Editor-in-Chief of the Mathematical Programming, Series A, journal. He gave numerous invited keynote and plenary talks, including invited section talk (section Control Theory & Optimization) at the International Congress of Mathematicians 2010, Hyderabad, India. In 2013 he was a recipient of Khachiyan prize awarded by the INFORMS Optimization Society.

Georgia Tech, USA

Alexander Shapiro is a Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He has published more than 130 research articles in peer review journals and is a coauthor of several books. His research is widely cited and he was listed as an “ISI Highly Cited Researcher” in 2004 (ISI = Institute for Scientific Information).

He is on the editorial board of several professional journals, such as Mathematics of Operations Research, ESAIM: Control, Optimization and Calculus of Variations, Computational Management Science. He was an area editor (Optimization) of Operations Research, currently he is the Editor-in-Chief of the Mathematical Programming, Series A, journal.

He gave numerous invited keynote and plenary talks, including invited section talk (section Control Theory & Optimization) at the International Congress of Mathematicians 2010, Hyderabad, India. In 2013 he was a recipient of Khachiyan prize awarded by the INFORMS Optimization Society.

Risk averse and distributionally robust multistage stochastic optimization
In many practical situations one has to make decisions sequentially based on data available at the time of the decision and facing uncertainty of the future. This leads to optimization problems which can be formulated in a framework of multistage stochastic optimization. In this talk we consider risk averse and distributionally robust approaches to multistage stochastic programming. We discuss conceptual and computational issues involved in formulation and solving such problems. As an example we give numerical results based on the Stochastic Dual Dynamic Programming method applied to planning of the Brazilian interconnected power system.

Risk averse and distributionally robust multistage stochastic optimization

In many practical situations one has to make decisions sequentially based on data available at the time of the decision and facing uncertainty of the future. This leads to optimization problems which can be formulated in a framework of multistage stochastic optimization. In this talk we consider risk averse and distributionally robust approaches to multistage stochastic programming. We discuss conceptual and computational issues involved in formulation and solving such problems. As an example we give numerical results based on the Stochastic Dual Dynamic Programming method applied to planning of the Brazilian interconnected power system.