Artificial Intelligence & Machine Learning

The modern world is full of artificial, abstract environments that challenge our natural intelligence. The goal of our research is to develop Artificial Intelligence that gives people the capability to master these challenges, ranging from formal methods for automated reasoning to interaction techniques that stimulate truthful elicitation of preferences and opinions. Another aspect is characterizing human intelligence and cognitive science, with applications in human-computer interaction and computer animation.

Machine Learning aims to automate the statistical analysis of large complex datasets by adaptive computing. A core strategy to meet growing demands of science and applications, it provides a data-driven basis for automated decision making and probabilistic reasoning. Machine learning applications at EPFL range from natural language and image processing to scientific imaging as well as computational neuroscience.

Affiliated People
Antoine Bosselut
Antoine Bosselut

Tenure Track Assistant Professor

[email protected] INR 234
Maria Brbic
Maria Brbic

Tenure Track Assistant Professor

[email protected] INJ 330
Charlotte Bunne
Charlotte Bunne

Tenure Track Assistant Professor

[email protected] BC 250
Nicolas Flammarion
Nicolas Flammarion

Tenure Track Assistant Professor

[email protected] INR 110
Caglar Gulcehre
Caglar Gulcehre

Tenure Track Assistant Professor

[email protected] BC 204
Tanja Kaeser
Tanja Kaeser

Tenure Track Assistant Professor

[email protected] INF 234
Martin Schrimpf
Martin Schrimpf

Tenure Track Assistant Professor

[email protected] BC 206
Robert West
Robert West

Associate Professor

[email protected] INN 310
Amir Zamir
Amir Zamir

Tenure Track Assistant Professor

[email protected] INJ 230