Esther Rodrigo Bonet received the B.Sc. degree in Physics from Universidad de Zaragoza, Zaragoza, Spain, in 2016, and the M.Sc. degree in Applied Computer Science from Vrije Universiteit Brussel, Brussels, Belgium in 2018. She was awarded the FWO grant from the Belgian government in 2020. Her research interests include machine learning, deep learning, graph deep learning with a special focus on explainability.

Explainable Physics-guided Deep Learning for Air Pollution Inference 

Air pollution has become a world-wide concern due to its negative impact on the population’s health and well-being. To mitigate its effects, it is essential to accurately monitor pollutant concentrations across regions and time. Traditional solutions rely on physics-driven approaches, leveraging equations of particle motion to predict pollutants’ shift in time. Despite being reliable and easy-to-interpret, they are computationally highly expensive. Recent works have shown that following a deep-learning data-driven approach significantly reduces the computational expenses and provides accurate predictions; yet, at the cost of lower interpretability.

This PhD aims to develop innovative air pollution monitoring solutions with high accuracy, manageable complexity and high interpretability. To this end, the focus will be put on designing graph-based deep learning models that obey well-studied physical equations. With this purpose, graph convolutional networks (GCNs) and recurrent neural networks (RNNs) will be leveraged. In addition, innovative data fusion techniques will be incorporated into the models to merge data from multiple modalities. The research is envisioned to produce state-of-the-art models that combine the best of both physics- and deep-learning-based approaches for monitoring air pollution. Additionally, the lack of explainability is becoming a major drawback for GCNs, especially in health-related domains such as air pollution where a model’s predictions might directly affect humans. For this reason, existing explainability techniques will be researched and novel methods will be designed. The developed techniques, based on (explainable) GCNs and RNNs, could also lead to various applications in modelling other natural processes such as weather prediction and water monitoring, and applications on the Internet such as recommender systems or fake news analysis.

Achievements (Honors & Awards)