The Board of the Swiss Federal Institutes of Technology has announced the appointment of Pascal Frossard as Full Professor of Electrical Engineering and Electronics in the School of Engineering (STI). Currently Associate Professor at EPFL, he joined the EcoCloud faculty in 2018 to help the research centre drives its cloud computing programs.
Before joining EPFL in 2003, Professor Frossard was a member of the research staff at the IBM TJ Watson Research Center at Yorktown Heights, NY, USA. His core research areas include interpretable machine learning, data science, graph signal processing, image representation and analysis, computer vision and immersive communication systems. His research contributions include the analysis of geometric properties of deep networks, deep nets robustness analysis, and representation learning for graph signals.
Professor Frossard has won international acclaim for expertise in signal and image processing, and its applications in intelligent systems and biomedicine. He is known for an innovative research approach that combines different disciplines in the natural and engineering sciences and facilitates essential academic and industrial partnerships. Over the years, he has won many distinctions. These include, inter alia, the Swiss National Science Foundation Professorship Award in 2003, IBM Faculty Award in 2005, IBM Exploratory Stream Analytics Innovation Award in 2008, Google’s 2017 Faculty Award, IEEE Transactions on Multimedia Best Paper Award in 2011, and IEEE Signal Processing Magazine Best Paper Award in 2016. He is a Fellow of IEEE. His work has been widely published in reputed journals. In his most recent publication,* the authors introduce a representation learning algorithm for graphs, which simultaneously learns a low-dimensional space and coordinates for the nodes in that space.
Professor Frossard’s appointment as Full Professor will undoubtedly strengthen EPFL’s key strategic research areas.
* Simou, Effrosyni & Thanou, Dorina & Frossard, Pascal. (2020). node2coords: Graph Representation Learning with Wasserstein Barycenters. IEEE Transactions on Signal and Information Processing over Networks. PP. 1-1. 10.1109/TSIPN.2020.3041940.