Soroosh Shafiezadeh Abadeh

Dr.

Soroosh Shafiezadeh Abadeh

Alumni

ETH Zurich

Optimization and control are the revolutionary contribution of modern research to automation

Soroosh Shafieezadeh Abadeh was a postdoctoral student in the Automatic Control Lab at ETH Zurich from October 2020 until August 2021. He received his doctoral degree in Management of Technology from Ecole Polytechnique Fédérale de Lausanne in 2020. He received a Bachelor and a Master degree in Electrical Engineering (major in Automatic Control) from the University of Tehran in 2011 and 2014, respectively. His current research interests are focused on optimization under uncertainty, the design of large-scale algorithms for solving stochastic and distributionally robust optimization problems, and the development of statistical tools for data-driven decision-making problems.

Scientific Publications

Published

Discrete Optimal Transport with Independent Marginals is #P-Hard

SIAM Journal on Optimization

Vol 33 No 2 Pages 589-614

Bahar Taskesen, Soroosh Shafiezadeh Abadeh, Daniel Kuhn, Karthik Natarajan

2023

Doi.org

Published

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Mathematical Programming

Vol 199 Pages 1033-1106

Bahar Taskesen, Soroosh Shafiezadeh Abadeh, Daniel Kuhn

2021

Collection

Research projects as Researcher

Title

Principal Investigators

Online distributionally robust optimization with streaming data

Florian Dörfler, John Lygeros, Daniel Kuhn

Summary

Recent years have seen a surge of academic and industrial interest in distributionally robust optimization, where the probability distribution of the uncertain problem parameters is itself uncertain and one seeks decisions that are optimal in view of the most adverse distribution within a given ambiguity set. In this project we will study online distributionally robust optimization with streaming data. The key motivation is that dynamic stochastic processes (as encountered in control, estimating, and filtering problems) demand recursive and online solutions with real-time computational constraints. We plan to implement our approaches on various energy system platforms.

People involved