I am a postdoctoral researcher in Harvard University, working with the team of Zitnik Lab at the Department of Biomedical Informatics of Harvard University. I am very fortunate to receive the 2022 Wojcicki and Troper Fellowship of Harvard Data Science Initiative for supporting my research in learning graph representations for biomedical applications.

Academic Bio

I received a Ph.D. in Computer Science from the DaSciM group of LIX, École polytechnique in Paris, France. During my graduate studies, I worked as a doctoral researcher in Noah’s Ark Lab at Huawei Technologies France. I was very fortunate to be advised by:

Before that, I graduated from the department of Electrical and Computer Engineering at National Technical University of Athens (NTUA). My diploma thesis focused on deep learning for time series forecasting for predictive route optimization. The goal of the thesis was the application of the predictive optimization on real-time fuel-price prediction and route planning of cargo vehicles.

Research Focus

I am working on machine learning for structured data with a special focus on graphs and generally non-Euclidean structures. My research is concentrated mainly on building powerful graph learning models, that are able to extract knowledge in diverse real-world applications, ranging from communication networks to bioinformatics. During my research journey so far, I encountered challenging problems in the expressivity of Graph Neural Networks for molecular structure modeling, contributing to more accurate predictions in bioinformatics tasks. Also, I met the demanding needs of the telecommunications industry, where graph learning models have shown a crucial impact.