Pascal Frossard

EPFL STI IEL LTS4
ELE 241 (Bâtiment ELE)
Station 11
1015 Lausanne

Awards

SNSF Professorship

Swiss National Science Foundation

2003

IBM Faculty Award

2005

Exploratory Stream Analytics Innovation Award

IBM Research

2008

Teaching & PhD

PhD Students

Amel Abdelraheem, Tuna Alikasifoglu, Mahdi Amiri, Jérémy Jean Philippe Baffou, Cem Bilaloglu, William Cappelletti, Alba Carballo Castro, Adam Hazimeh, Vincent Jung, Manuel Madeira, Elisa Messori, Sevda Ögüt, Yiming Qin, Abdellah Rahmani, Vasiliki Rizou, Ke Wang, Yike Zhao

Past EPFL PhD Students

Ana Karina De Abreu Goes (2015), Clémentine Léa Aguet (2023), Zafer Arican (2010), Beril Besbinar (2022), Eirina Bourtsoulatze (2013), Isabela Cunha Maia Nobre (2022), Stefano D'Aronco (2018), Nikolaos Dimitriadis (2026), Xiaowen Dong (2014), Alhussein Fawzi (2016), Ádám Dániel Ivánkay (2023), Dan Jurca (2007), Sofia Karygianni (2015), Renata Khasanova (2019), Effrosyni Kokiopoulou (2009), Javier Alejandro Maroto Morales (2024), Apostolos Modas (2022), Seyed Mohsen Moosavi Dezfooli (2019), Guillermo Ortiz Jimenez (2023), Arnaud Pannatier (2024), Hermina Petric Maretic (2021), Ivana Radulovic (2008), Mattia Rossi (2020), Ortal Yona Senouf (2026), Yamin Sepehri (2024), Jelena Simeunovic (2024), Effrosyni Simou (2022), Dorina Thanou (2016), Vijayaraghavan Thirumalai (2012), Ivana Tosic (2009), Tamara Tosic (2013), Clément Vignac (2023), Elif Vural (2013), Jean-Paul Wagner (2008), Ahmet Caner Yüzügüler (2023)

Past EPFL PhD Students as codirector

Luigi Bagnato (2012), Alessandro Favero (2025), Angelos Katharopoulos (2022), Harshitha Machiraju (2024), Suraj Srinivas (2022)

Courses

EECS Seminar: Advanced Topics in Machine Learning

ENG-704

Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theoretical and experimental results.

Network machine learning

EE-452

Fundamentals, methods, algorithms and applications of network machine learning and graph neural networks

Signal processing

EE-350

In this course, we introduce the main methods in signal processing.