Pascal Frossard

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Full Professor

pascal.frossard@epfl.ch +41 21 693 56 55 http://lts4.epfl.ch/frossard

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

41 21 693 56 55
41 21 693 26 01
Office: ELE 241
EPFL > STI > IEM > LTS4

Web site: Web site: https://lts4www.epfl.ch/

EPFL SEL-SEL-ENS
ELD 241 (Bâtiment ELD)
Station 11
CH-1015 Lausanne

41 21 693 56 55
Office: ELE 241
EPFL > STI > STI-SEL > SEL-ENS

EPFL DCC STI-DO
ELD 241 (Bâtiment ELD)
Station 11
CH-1015 Lausanne

41 21 693 56 55
EPFL > STI > STI-DEC > STI-DO

Web site: Web site: https://sti.epfl.ch/page-1740.html

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Administrative data

Fields of expertise

Graph and Network Signal Processing 
Image and Video Processing 
Machine Learning and Data Science 
Artificial Intelligence 

Teaching & PhD

Teaching

Electrical and Electronics Engineering

PhD Programs

Doctoral program in computer and communication sciences

Doctoral Program in Electrical Engineering

Doctoral Program Digital Humanities

PhD Students

Aguet Clémentine Léa, Besbinar Beril, Cunha Maia Nobre Isabela, Dimitriadis Nikolaos, Haymann Ines Ellen Germaine, Ivánkay Ádám Dániel, Katharopoulos Angelos, Machiraju Harshitha, Maroto Morales Javier Alejandro, Modas Apostolos, Ortiz Jimenez Guillermo, Pannatier Arnaud, Petric Maretic Hermina, Senouf Ortal Yona, Sepehri Yamin, Simeunovic Jelena, Simou Effrosyni, Srinivas Suraj, Vignac Clément Arthur Yvon, Yüzügüler Ahmet Caner,

Past EPFL PhD Students

Arican Zafer , Bagnato Luigi , Bourtsoulatze Eirina , D'Aronco Stefano , De Abreu Goes Ana Karina , Dong Xiaowen , Fawzi Alhussein , Jurca Dan , Karygianni Sofia , Khasanova Renata , Kokiopoulou Effrosyni , Moosavi Dezfooli Seyed Mohsen , Radulovic Ivana , Rossi Mattia , Thanou Ntorina , Thirumalai Vijayaraghavan , Tosic Ivana , Tosic Tamara , Vural Elif , Wagner Jean-Paul ,

Courses

Signal processing

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

EECS Seminar: Advanced Topics in Machine Learning

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.