I am currently a member of the newly formed, fast-growing Machine Learning for Smart Mobility (MLSM) group at the Technical University of Denmark (DTU), where I’m working on machine learning models for understanding urban mobility and the behavior of crowds. My Ph.D. was focused on the detection, analysis, and prediction of traffic anomalies due to special events, such as concerts, basketball games and demonstrations. I was specialized in developing models that successfully combine data from different resources, and at the same time, I have enriched my knowledge on theory and application of machine learning and statistical models (prediction, classification, clustering, time series forecasting, regression models, etc.) using Python. I presented my ideas in several conferences, and through this process, I learned to communicate my ideas with ease and to sweep my enthusiasm on the projects I was involved.

I hold an MSc in Geoinformatics (2012-2014) and a Diploma in Rural and Surveying Engineering (2007-2012), both from National Technical University of Athens (NTUA). Between November 2013 and May 2014, I was a research engineer within the Future Urban Mobility group at the Singapore-MIT Alliance, an external lab of the Massachusetts Institute of Technology (MIT) that is located in Singapore. I had the chance to work in an international environment that initiated me into several innovations in the field of transport, as well as the domain of Big Data Analytics. I learned to adapt quickly to the requirements of a completely different working environment, where continuous vigilance, excellent collaboration with team members, and fast adaptability were required.

For more details:

Check my Résumé!


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