Alexandre Alahi

EPFL ENAC IIC VITA
GC C1 383 (Bâtiment GC)
Station 18
1015 Lausanne

EPFL ENAC IIC VITA
GC C1 383 (Bâtiment GC)
Station 18
CH-1015 Lausanne

EPFL ENAC IIC VITA
GC C1 383 (Bâtiment GC)
Station 18
CH-1015 Lausanne

Expertise

Transportation
Mobility
Computer Vision
Machine Learning
Deep Learning
Human-Robot Interaction
Socially-aware Artificial Intelligence
Ambient Intelligence
Alexandre Alahi is an associate professor at EPFL leading the Visual Intelligence for Transportation laboratory (VITA). Before joining EPFL in 2017, he spent multiple years at Stanford University as a Post-doc and Research Scientist.
His research lies at the intersection of Computer Vision, Machine Learning, and Robotics applied to transportation & mobility. To make Artificial Intelligence (AI) driven systems such as autonomous vehicles a safe reality, his lab works on a new type of Artificial Intelligence (AI), namely socially-aware AI, i.e., an AI augmented with social intelligence.
In 2022&2023, Alexandre was recognized as the top 100 Most Influential Scholar in Computer Vision over the past 10 years.
His research team received the editor's choice award from the journal Image and vision computing (2021) for their work on human motion prediction, the honorable mention at an ICCV workshop (2019) for their work on human pose estimation,
the CVPR Open Source Award (2012) for their work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for their sparsity-driven algorithm that has tracked more than 100 million pedestrians to date.
His work has been licensed to several companies and covered internationally by BBC, abc, PBS, Euronews, Wall street journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.

Prix et distinctions

0

0

2022

2022

Enseignement et PhD

Doctorant·es actuel·les

Vladimir Dominic K. Somers, Valentin Gerard, Weijiang Xiong, Mariam Hassan, Yasaman Haghighi, Parsa Rahimi Noshanagh, Mohamed Ossama Ahmed Abdelfattah, Reyhaneh Hosseininejad, Megh Shukla, Po-Chien Luan, Bastien Van Delft, Lan Feng, Ahmad Rahimi, Yang Gao, Yasamin Borhani

A dirigé les thèses EPFL de

George Adaimi, Parth Ashit Kothari, Lorenzo Bertoni, Yuejiang Liu, Saeed Saadatnejad, Brian Alan Tappy-Sifringer, Mohammadhossein Bahari, Melika Behjati

A co-dirigé les thèses EPFL de

Prabhu Teja Sivaprasad

Cours

Deep learning for autonomous vehicles

CIVIL-459

L'Apprentissage Profond (AP) remodèle l'avenir du transport et de la mobilité. Dans ce cours, nous montrerons comment il peut être utilisé pour apprendre aux véhicules autonomes à détecter des objets, à faire des prédictions et à prendre des décisions. (Texte traduit par l'AP)

Introduction to machine learning for engineers

CIVIL-226

Les étudiants en génie civil ont déjà reçu leur premier cours d'introduction CS-119(h) sur l'Information, le calcul et la communication. Par conséquent, cette classe se concentrera sur les bases de l'apprentissage automatique (ML). Chaque cours débutera par une application du ML au génie civil.

Programming and software development for engineers

CIVIL-127

Cours de programmation Python pour améliorer les compétences des étudiants en matière de programmation et les aider à écrire de meilleurs logiciels. Le cours enseignera les meilleures pratiques et techniques telles que le refactoring, le débogage et les tests unitaires.