Andrea Ridolfi
Short Bio
I am a professor of Signal Processing and Communication Technologies at Bern University of Applied Sciences. Since 2004 I hold a lecturer position at EPFL, teaching “Mathematical Principles of Signal Processing” (Doctoral School, 2004 – 2011), “Statistical Signal and Data Processing through Applications” (Master Program, (2004 – ongoing), and Signal Processing and Machine Learning for Digital Humanities (Master, 2017 – 2019, co-taught with Mathieu Salzmann). Previously, I have been working as Project Manager and R&D Engineer at EPFL (2011-2014), coordinating the LCAV activities within the NSF – Nanotera project Opensense, and as Project Manager and R&D Engineer with the biomedical signal processing group at CSEM (2006-2011).Web site: Web site: https://ssc.epfl.ch
Web site: Web site: https://sin.epfl.ch
Fields of expertise
Signal Processing, Stochastic Processes, Point Processes, Sensor Networks
Biography
I am a professor of Signal Processing and Communication Technologies at Bern University of Applied Sciences. Since 2004 I hold a lecturer position at EPFL, teaching “Mathematical Principles of Signal Processing” (Doctoral School, 2004 – 2011), “Statistical Signal and Data Processing through Applications” (Master Program, (2004 – ongoing), and Signal Processing and Machine Learning for Digital Humanities (Master, 2017 – 2019, co-taught with Mathieu Salzmann). Previously, I have been working as Project Manager and R&D Engineer at EPFL (2011-2014), coordinating the LCAV activities within the NSF – Nanotera project Opensense, and as Project Manager and R&D Engineer with the biomedical signal processing group at CSEM (2006-2011).Teaching & PhD
Teaching
Communication Systems
Computer Science
Courses
Statistical signal and data processing through applications
1. Fundamentals of Statistical Signal and Data Processing: Signals and systems from the deterministic and the stochastic point of view; Processing and analysing signals and systems with a mathematical computing language.
2. Models, Methods, and Algorithms: Parametric and non-parametric signal models (wide sense stationary, Gaussian, Markovian, auto-