Yingqian Zhang is an Associate Professor in the Information Systems group at Eindhoven University of Technology (TU/e). Her research interests lie in the area of Artificial Intelligence for decision-making, particularly, machine learning, data-driven optimization, multi-agent systems, and socially aware algorithms.
She developes AI driven decision-making approaches in several problem domains, including logistics, transportation, manufacturing, and e-commerce.
Yingqian Zhang is promotor of
- Mohsen Abbaspour Onari
- Abdo Abouelrous
- Luca Begnardi
- Robbert Reijnen
- Jesse van Remmerden
- Ya Song
I optimize decisions in a data-rich environment using AI.
Yingqian Zhang received her PhD in Computer Science from the University of Manchester, UK. She has worked as an Assistant Professor in Econometrics Institute a t Erasmus University Rotterdam, and as a postdoc researcher in the Algorithmics group at TU Delft. She was a visiting professor in the Institute for Advanced Computer Studies at University of Maryland, College Park, USA in 2015.
Yingqian is on the board of BNVKI (Benelux Association for Artificial Intelligence), on the editorial board of journal "Annals of Mathematics and Artificial Intelligence", and serves yearly as a member of the technical Program Committee for major AI conference such as IJCAI, AAAI, AAMAS, ECAI. She is on the executive committee of the working group DSO (Data Science meets Optimisation) of EURO (Association of European Operational Research Societies) to promote collaboration between AI and OR communities on the direction of machine learning and optimization.
Policies for the dynamic traveling maintainer problem with alertsEuropean Journal of Operational Research (2023)
Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization(2023)
Optimizing the inventory and fulfillment of an omnichannel retailer: a stochastic approach with scenario clusteringComputers & Industrial Engineering (2022)
Setting reserve prices in second-price auctions with unobserved bidsINFORMS Journal on Computing (2022)
Operator Selection in Adaptive Large Neighborhood Search using Deep Reinforcement LearningarXiv (2022)