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/

+41 21 693 56 55
EPFL > STI > STI-SEL > SEL-ENS

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

+41 21 693 56 55
Office:  BM 4101
EPFL > STI > STI-DEC > STI-DO

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

+41 21 693 56 55
EPFL > VPA > VPA-FAC > OSSC

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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 Students

Aguet Clémentine Léa, Bilaloglu Cem, Cappelletti William, Chen Haolin, Dimitriadis Nikolaos, Favero Alessandro, Ivánkay Ádám Dániel, Machiraju Harshitha, Maroto Morales Javier Alejandro, Milutinovic Marija, Ortiz Jimenez Guillermo, Pannatier Arnaud, Rahmani Abdellah, Senouf Ortal Yona, Sepehri Yamin, Simeunovic Jelena, Vignac Clément Arthur Yvon,

Past EPFL PhD Students

Arican Zafer , Bagnato Luigi , Besbinar Beril , Bourtsoulatze Eirina , Cunha Maia Nobre Isabela , D'Aronco Stefano , De Abreu Goes Ana Karina , Dong Xiaowen , Fawzi Alhussein , Jurca Dan , Karygianni Sofia , Katharopoulos Angelos , Khasanova Renata , Kokiopoulou Effrosyni , Modas Apostolos , Moosavi Dezfooli Seyed Mohsen , Petric Maretic Hermina , Radulovic Ivana , Rossi Mattia , Simou Effrosyni , Srinivas Suraj , Thanou Ntorina , Thirumalai Vijayaraghavan , Tosic Ivana , Tosic Tamara , Vural Elif , Wagner Jean-Paul , Yüzügüler Ahmet Caner ,

Courses

Signal processing

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

Network machine learning

Fundamentals, methods, algorithms and applications of network machine learning

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.