Biography

Benjamín Béjar Haro received the Electrical Engineering degree from both the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, and the Technische Universität Darmstadt (TUD), Darmstadt, Germany, in 2006 under the framework of the Double Degree Exchange program. He received the Ph.D. degree in Electrical Engineering from the Universidad Politécnica de Madrid (UPM), Madrid, Spain, in 2012. From Sept. 2011 to Sept. 2013 he was a member of the Vision, Dynamics and Learning lab at the Johns Hopkins University (JHU), Baltimore, MD, USA, as part of the MSE program in Biomedical Engineering.

He has held research appointments as a visiting Ph.D. student at the Università degli Studi di Udine, Udine, Italy, in 2009 and, at the Hong Kong University of Science and Technology (HKUST), Hong Kong, China, in 2011.

In 2012, he was awarded with the 2012 Best Paper Award in Medical Robotics and Computer Assisted Intervention Systems from the Medical Image Computing and Computer Assisted Intervention Society (MICCAI).

He served as a postdoctoral researcher and lecturer in the Audiovisual Communications Laboratory (LCAV) at EPFL from Oct. 2013 to Oct. 2017. From Oct. 2017 to Dec. 2019 he was with the Mathematical Institute for Data Science at Johns Hopkins University as an Associate Research Scientist and was later promoted to an Assistant Research Professor at the Department of Biomedical Engineering. He joined the Swiss Data Science Center (SDSC) in Jan. 2020 as a Sr. Data Scientist and Lecturer at the SDSC EPFL office in Lausanne, Switzerland. Since Nov. 2021 he leads the newly created office of SDSC at the Paul Scherrer Institute as Group Leader working on data science projects related to large scale infrastructures. He also holds an appointment as an Adjunct Associate Research Scientist at the Mathematical Institute for Data Science at Johns Hopkins University. He has ample expertise is in the areas of machine learning, digital signal and image processing, computer vision, time-series analysis, inverse problems, sparsity, and convex optimization for a wide range of applications.