Rate My Professor Çiçek Güven

Güven's research focuses on network analysis and machine learning techniques applied to graph-structured data. She investigates how networks emerge, their characterization, evolution, and the identification of meaningful substructures, with an emphasis on explainability in linking structural properties to prediction outcomes. Her work spans applications such as brain networks, population networks, and electrical grids. Committed to data science projects with social impact, she contributes to initiatives like the ILUSTRE Lab consortium for sustainable water management and energy transition in Curaçao, the Child Growth Monitor by the Zero Hunger Lab for detecting malnutrition in children using AI, and the ICON project of the Zero Poverty Lab exploring connections between brain networks and poverty. Key publications include "Harnessing firm similarities with graph neural networks for superior stock predictions" (2025, co-authored with Tein Baaijens and Gonzalo Nápoles), "ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements" (2025, Journal of Imaging), "A closer look at the pairwise similarity based graphs and ways to capture relationships beyond pairs" (2025), and "Explaining CNN-based body measurement estimation in children using grad-RAM" (2026). She also co-created the ARAN dataset and has presented on AI applications for power load and renewable energy forecasting.