dr. Çiçek Güven

Bio

I am an assistant professor in  in the Cognitive Science and Artificial Intelligence department at Tilburg University. My research interests in general are network analysis, graph structured data, and data mining. Data science projects with a social aspect motivates me a lot. My recent research is over behavioral link analytics and knowledge based link prediction. I have a mathematical background,  my thesis  involved algebraic graph theory and combinatorics.  I also worked in the industry for a period of four years in banking and consultancy environments as a quant and a data analyst, working with financial and trade data mainly.   For the near future, I want to focus on problems that brings combinatorics and machine learning together,  for example for deep learning network design and optimisation purposes.

Courses

• Introduction to Machine Learning
• Bachelorthesis CSAI
• Master thesis/Data Science in Action
• Research Project and Thesis CSAI

Recent publications

1. Image-based body shape estimation to detect malnutrition

   Mohammedkhan, H., Güven, Ç., Balvert, M., & Postma, E. (2023). Image-based body shape estimation to detect malnutrition. Paper presented at Intelligent Systems Conference, Amsterdam.

2. Feature Importance for Clustering

   Nápoles, G., Griffioen, N., Khoshrou, S., & Güven, Ç. (Accepted/In press). Feature Importance for Clustering. In Lecture Notes in Computer Science (LNCS) series Springer.

3. The unique coclique extension property for apartments of buildings

   Brouwer, A., Draisma, J., & Güven, Ç. (Accepted/In press). The unique coclique extension property for apartments of buildings. Innovations in Incidence Geometry — Algebraic, Topological and Combinatorial, 1-16.

4. School Dropout Prediction and Feature Importance Exploration in Malaw…

   Çolak, H., Güven, Ç., & Nápoles, G. (2022). School Dropout Prediction and Feature Importance Exploration in Malawi Using Household Panel Data: Machine Learning Approach. Journal of Computational Social Science.

5. Which is the best model for my data?

   Nápoles, G., Grau, I., Güven, Ç., Özdemir, O., & Salgueiro, Y. (2022). Which is the best model for my data? arXiv.