Dear ROW community,
https://sites.google.com/view/row-series/home
Robust optimization
Distributional robust optimization
Robust integer optimization
Robust combinatorial optimization
Robust machine and deep learning
Robust control problems
Organizers: Ahmadreza Marandi (TU Eindhoven) and Jannis Kurtz (University of Amsterdam)
Junior ROW by Dr. Justin Starreveld and Irina Wang
Date: Feb 20, 2026
Time of the talk by Justin Starreveld: 15:00 CET
Time of the talk by Irina Wang: 15:30 CET
Info of the talk by Justin:
Title: Dealing With Uncertainty When Optimizing Industrial Decarbonization Pathways
Abstract: In this research we used mathematical optimization to inform strategic decisions surrounding the deployment of hydrogen in the Netherlands. One of the main challenges in this problem context is uncertainty about the future. The relevant time horizon extends from 2025 until 2050, and there is a lot of uncertainty regarding energy prices, governmental policies, technological development, etc. To address this uncertainty we developed and applied new methods for Robustness Analysis and Robust Optimization.
BIO: Justin Starreveld received his PhD from the University of Amsterdam, under the supervision of Prof. Dick den Hertog and Prof. Zofia Lukszo. His PhD research focuses on mathematical optimization under uncertainty, with an emphasis on applying such methods in practice. Prior to this, Justin obtained bachelor's and master's degrees in Econometrics from Erasmus University Rotterdam, where his passion for Operations Research was ignited. He currently works as an AI & Data Science Consultant at EyeOn, a Dutch consultancy firm that specializes in forecasting and supply chain planning.
Info of the talk by Irina:
Title: Learning Uncertainty Sets in Dynamic Robust Optimization
Abstract: We present a data-driven technique to automatically learn uncertainty sets in dynamic decision making under uncertainty. We formulate the learning problem as a control design problem where the control policy involves solving a robust optimization problem parametrized by the past disturbances, as well as the parameters of the uncertainty set. We propose a learning procedure to dynamically predict the parameters of the uncertainty set to minimize a closed-loop performance metric while satisfying probabilistic guarantees of constraint satisfaction. Our approach allows for uncertain data that is correlated across time periods, and can learn a wide range of commonly used uncertainty sets. By modeling our training problem objective and constraints using coherent risk metrics, we derive finite sample probabilistic guarantees of constraint satisfaction in multi-stage settings.
BIO: Irina Wang is a PhD candidate in the department of Operations Research and Financial Engineering at Princeton University. Irina received a bachelor degrees in Operations Research and Information Engineering from Cornell University. Her research interests include robust optimization, decision-focused learning, optimization-based control, and stochastic multi-level optimization. She is the recipient of several honors and awards including a Princeton Wallace Memorial Fellowship, an INFORMS Computing Society Student Paper Award, and a Princeton School of Engineering and Applied Sciences Excellence Award.
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* February 20, 2026: Justin Starreveld (University of Amsterdam) & Irina Wang (Princeton University)
* March 6, 2026: Emilio Carrizosa (University of Seville)
* April 17, 2026: Soroosh Shafiee (Cornell University)
* May 1, 2026: Bo Zeng (University of Pittsburgh)
* May 15, 2026: Halil Ibrahim Bayrak (TU Munich) & Menglei Jia (Shanghai Jiao Tong University)
* May 29, 2026: Peter Zhang (Carnegie Mellon University)
* June 12, 2026: Igor Malheiros (University of Montpellier) & TBA
* June 26, 2026: Amir Ardestani Jaafari (University of British Columbia)