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Meet Tenure Track Assistant Professor Davide Mottin and his research in algorithms for network (a.k.a. graph) analysis

Photo: Sebastian Krog Knudsen

A few years ago Davide Mottin joined the Data-Intensive Systems research group at Department of Computer Science as a Tenure Track Assistant Professor. Davide’s main research interests lies on algorithms that capture and predict phenomena in data that forms network structures, such as biological connections, social interactions, or message exchange.

His approach is interdisciplinary, and spans the popular areas of machine learning, data mining, data analysis, and data management. Data networks are increasingly employed to comprehend biological phenomena (e.g., the spread of a virus), social influence (e.g., misinformation), or interpreting human language. The study of algorithms that extract relevant information in an efficient manner is of uttermost importance to unravel complex interaction patterns and discovering relationships in the data.

Academic background

Davide got his PhD in Computer Science at the dbTrento group in the University of Trento, Italy, under the supervision of Themis Palpanas and Yannis Velegrakis. During his PhD, he visited Yahoo! Labs, Barcelona and Microsoft Research Asia, Beijing.  Before joining the Computer Science Department at Aarhus University, he worked as a Research Associate at the Center for Geoscience (GFZ) in Potsdam, Germany, and Postdoctoral Researcher at the Hasso Plattner Institute, in the group of Knowledge Discovery and Data Mining working with Emmanuel Müller.

Davide started his research career studying methods for easing the access to data for novice users. His interested evolved towards the analysis of networks with data mining and machine learning approaches. Currently, his interests are in the broad area of graph analysis, especially answering questions about scalability, interpretability, and prediction of user behaviors.

Selected publications