Author Archives: Yingqian Zhang

About Yingqian Zhang

I am an assistant professor in the Information Systems group at Eindhoven University of Technology. I received a Ph.D. degree from School of Computer Science at University of Manchester in 2005. Before coming to TU/e, I worked as an assistant professor in Econometrics Institute at Erasmus University Rotterdam (the Netherlands), as a Postdoc researcher in the Algorithmics group at TU Delft (the Netherlands), in the Computational Intelligence group at TU Clausthal (Germany). I also worked as a faculty research assistant in the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. My research expertise lies in the area of Artificial Intelligence – machine learning and multi-agent systems. I am particularly interested in data-driven optimization (or prescriptive analytics), optimization and coordination in multi-agent system, using techniques from data mining and machine learning, algorithmic design, mathematical modelling, and applied game theory. I enjoy optimizing decision making with big data, designing efficient algorithms, conducting simulations, and developing models and mechanisms involving cooperative or self-interested players. The application domains of my current research include e-commerce, transportation and logistics, manufacturing, and sharing economy.

Best Student Paper Award @ ICAART 2019

Very proud that our PhD student, Reza Refaei Afshar, has received the Best Student Paper Award for his work presented at the 11th International Conference on Agents and Artificial Intelligence held on 19 – 21 February, in Prague, Czech Republic. The paper titled “A Reinforcement Learning Method to Select Ad Networks in Waterfall Strategy” was co-authored by me, Murat Firat and Uzay Kaymak.

Nowadays, one of the most important sources of income for publishers who own websites is through online advertising (online ADs). For online publishers, it is difficult to design good strategies to manage their online AD auction systems due to highly dynamic real-time bidding environment. This paper proposes a machine learning based decision support system for publishers, which is built from historical AD auction data. The proposed method demonstrates its effectiveness in terms of the increased expected revenue for publishers.

The paper is available at: https://research.tue.nl/en/publications/a-reinforcement-learning-method-to-select-ad-networks-in-waterfal

PhD position available on Data-Driven Maintenance and Service Logistics for Maritime Assets

PThe PhD position is part of the NWO funded project “MARCONI: Maritime Remote Control Tower for Service Logistics Innovation.”  In this project, we aim to develop and demonstrate innovative service logistics concepts that exploit actual data on the state of maritime assets and the availability of the relevant maintenance resources. These concepts are aimed at (1) reducing maintenance costs, (2) increasing safety, by lowering the probability of unplanned system downtime and (3) reducing the number of unnecessary sailing movements (emissions) through smarter planning and/or clustering of maintenance activities. The ambition is to demonstrate the actual functioning of a remote service logistic control tower, with the long-term goal of developing and exploiting a scalable supply chain function in the maritime world. The PhD student will be a part of the research work-package on ‘Developing Service Logistics Decision Models’ led by TU/e. In the PhD project, there will be a close collaboration with the other partners of the MARCONI project: Boskalis, Damen, Gordian, Maastricht University, NLDA, Thales, RH Marine, Royal Netherlands Navy, and University of Twente. 

See more information and apply here before March 10 2019: https://jobs.tue.nl/en/vacancy/phd-on-datadriven-maintenance-and-service-logistics-for-maritime-assets-455559.html

Paper accepted by AAAI2019!

Super excited and proud! Our paper “Learning optimal classification trees using a binary linear program formulation” was accepted by AAAI (Thirty-Third AAAI Conference on Artificial Intelligence)! Acceptance rate 16.2% over 7700 submissions!
We show the power of optimization methods for deriving interpretable machine learning models.

"Best Paper Award for 2017" by Omega

Very happy that my paper “Fair task allocation in transportation”, co-authored with Charlie Ye and Rommert Dekker, was selected by Omega as “Best Paper Award for 2017”!
In this paper, we point out that in many cases, such as those in Sharing Economy, when allocating tasks and resources, cost should not be the major consideration. Distributing tasks/resources in a fair way among players is a more socially desired outcome. For achieving such outcomes, we design an efficient algorithm. We show by experiments that fairness often comes with a very small price in terms of cost. Check the paper out if you are interested!
https://doi.org/10.1016/j.omega.2016.05.005

PhD and Postdoc positions on data driven decision making available!