I work in the emerging and challenging field of big data. My research focuses on how to process efficiently very large amounts of data using parallel and/or distributed architectures. In this context, I’m particularly interested in computation that extracts new (and interesting) knowledge from the Web or from other very large datasets.
To achieve this goal, I’ve been researching possible ways to distribute the process of deriving new information from very large RDF datasets using clusters of machines. Next to this, I studied the problems of compressing and querying very large graphs with dozen billions edges. Recently, I’ve became interested in performing data-intensive generic computation on both static and streaming data. If you are interested, please check out my publication list to have a better idea of my research area.
I received a number of awards for my research, and I’m very honored for that. A few papers that I co-authored have received either a honorable mention or a best paper award at top conferences. In 2010, my work on forward inference with MapReduce has won the IEEE SCALE challenge. In 2012, the Network Institute awarded me the prize “Most Promising Young Researcher Award”. In 2013, my PhD was awarded with the qualification cum laude, which was given only to 5% of the theses in our department.