Isel Grau is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e). Her research interests lie in the area of Artificial Intelligence, particularly recurrent neural networks, cognitive networks, (semi-)supervised classification, time-series analysis, data-driven decision making, and explainable AI. Her main research activity focuses on making machine learning black boxes more interpretable, formalizing prior knowledge from experts and using their feedback to improve AI models, identifying and correcting bias, and exploring these challenges in healthcare or business settings. She aims to develop more trustable and meaningful decision support systems that put human experts in the loop.
Isel Grau is promotor / co-promotor of:
- Mohsen Abbaspour Onari
I want to contribute to AI solutions that are accurate and useful, but also transparent and fair.
Isel Grau received her Ph.D. in Computer Science from the Vrije Universiteit Brussel (VUB), Belgium. Her Ph.D. research focused on machine learning interpretability and semi-supervised classification. During her postdoctoral research at the Artificial Intelligence Laboratory of the VUB, she closely collaborated with institutions such as the Interuniversity Institute of Bioinformatics Brussels, the Universitair Ziekenhuis Brussel, and industry partners on interdisciplinary projects. She was a visiting researcher at the Warsaw University of Technology in 2022. She has active collaboration with researchers from Vrije University Brussel, Hasselt University, Universidad de Talca, Universidad de Granada, Warsaw University of Technology, and Tilburg University.
Semiconductor Demand Forecasting using Long Short-term Cognitive Networks34th Benelux Conference on Artificial Intelligence and 31st Belgian-Dutch Conference on Machine Learning, BNAIC/BeNeLearn 2022 (2022)
Which is the best model for my data?arXiv (2022)
Long short-term cognitive networksNeural Computing and Applications (2022)
Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19(2022)
Challenges in describing the conformation and dynamics of proteins with ambiguous behaviorFrontiers in Molecular Biosciences (2022)