Martino Trevisan

Short Bio

I am an Assistant Professor at the Department of Engineering and Architecture of the University of Trieste. I received my M.Sc. (2015) and Phd (2019) in Computer Engineering from Politecnico di Torino. During my career, I visited Télécom ParisTech (Paris), Cisco Systems labs (San Josè, US), AT&T (Bedminster, US) and the Universidade Federal de Minas Gerais (Brazil). I published more than 40 papers in prestigious journals and conferences in the field of networking and big data. I published in journals including IEEE/ACM Transactions on Networking, ACM Transactions on the Web, IEEE TNSM and IEEE Transactions on Mobile Computing. I presented my contributions at conferences such as ACM Conext, ACM HyperText and ACM IMC.

Research Interests

My research interests are mainly focused on big data methodologies for Web and Internet analysis. Given the complexity of today's networks and the scale at which data are generated, it is fundamental to design novel methodologies to understand network traffic, with the goal of optimizing network management, finding anomalies and contrasting cyber attacks. To this end, I leverage recent advances in machine learning and big data to design automatic methodologies to cope with the large and diverse data coming from the Internet and the Web. In particular, I am active in the field of network traffic classification, passive network measurements and optimization of the QoE perceived by Internet users.I have been studying the operation of Online Social Networks and their implications on user behaviour. Online social networks represent a phenomenon of paramount importance, and there is a great need for methodologies to understand how information spreads within them. I work for designing novel methodologies to model online phenomena and extract knowledge from the large graphs representing the interaction of users, finding communities and studying online behaviour.I am also interested in the Web ecosystem at large, working in measuring the operation of online advertising and tracking platforms, their impact on users’ privacy and the role of legislation. Privacy of data is becoming a crucial topic for the future of the Web, and, as such, I am active in proposing solutions to enhance data processing transparency and in designing scalable algorithms for data anonymization, allowing to fairly regulate the trade-off between privacy and data utility.

Teaching and Thesis

Please check the Teaching page for information about courses and classes. If you are intersted in a thesis (@UniTS or abroad), check the proposals on the Thesis page.

Further Information

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