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Meet Cigdem Aslay, our new Tenure Track Assistant Professor

Cigdem Aslay
Photo: Sebastian Krog Knudsen

In 2020, Sigdem Aslay started as Tenure Track Assistant Professor in the Data-Intensive Systems research group at the Department of Computer Science.

Main research interest

Cigdem's main research interest lies in developing large-scale data mining and machine learning methods to model, understand and improve the functioning of social, information, and networked systems. A common thread in her research is to design scalable algorithms and data-based solutions, built on a sound theoretical foundation, to address real-world problems on graphs and social networks. For her research, she resorts to mathematical methods of proof which are borrowed from the areas of algorithms, statistics, and computational complexity.

Academic background

Cigdem Aslay received her PhD from the University of Pompeu Fabra, Barcelona, Spain in December 2016 under the supervision of Francesco Bonchi and Ricardo Baeza Yates. During her PhD, she was hosted by Yahoo Research Barcelona Lab as a PhD student in the Web Mining Group, working on social influence algorithms. She was also a visiting student at the University of British Columbia for a long period under the supervision of Laks Lakshmanan. 

Before starting her PhD, she obtained her MSci from the Erasmus Mundus joint master programme in Data Mining and Knowledge Management with a full EU scholarship. She also holds a BSci in Statistics, awarded by METU in 2006, and has four years of professional industrial experience working as a statistical modeling consultant at the Nielsen Company.

Upon the completion of her PhD, she worked as a postdoctoral researcher in the Algorithmic Data Analytics Lab at ISI Foundation, working with Francesco Bonchi. Before joining Aarhus University as an assistant professor, she was a postdoctoral researcher in the Data Mining Group at Aalto University, working with Aristides Gionis.

Favorite publications

Her favorite three publications, one on frequent subgraph mining problem, one on combating political polarization in social networks, and one on social influence algorithms, are listed below:

  • "TipTap: Approximate Mining of Frequent k-Subgraph Patterns in Evolving Graphs", published in ACM Transactions on Knowledge Discovery from Data (TKDD) 2021.
  •  "Co-exposure maximization in online social networks", published in Advances in Neural Information Processing Systems (Neurips) 2020.
  • "Viral marketing meets social advertising: Ad allocation with minimum regret", published in Proceedings of the VLDB Endowment (PVLDB) 2015.