Mathieu Salzmann
EPFL IC IINFCOM CVLAB
BC 309 (Bâtiment BC)
Station 14
1015 Lausanne
Web site: Web site: https://cvlab.epfl.ch/
EPFL IC IINFCOM CVLAB
BC 309 (Bâtiment BC)
Station 14
1015 Lausanne
Web site: Web site: https://sin.epfl.ch
EPFL IC IINFCOM CVLAB
BC 309 (Bâtiment BC)
Station 14
1015 Lausanne
Web site: Web site: https://ssc.epfl.ch
Fields of expertise
Computer Vision
Machine Learning
Machine Learning
News
- Our work in collaboration with S. Süsstrunk and R. Baroni on comics reconfiguration is displayed at the EPFL Pavilion A- 1 paper accepted to NeurIPS 2023
- 5 papers accepted to ICCV 2023
- 8 papers accepted to CVPR 2023
- 1 paper accepted to ICML 2023
- I am Area Chair for ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024
- I am Action Editor for TMLR
- I am Associate Editor for IEEE TPAMI
- As of May 1st 2020, I have a 50% position with ClearSpace
- As of Sept 2019, I have a Courtesy Appointment with the EPFL College of Humanities
Current work
My main interests are:- Deep learning for 2D and 3D visual scene understanding
- Efficient and robust deep learning
- Domain adaptation and generalization
- Interpretable machine learning
Biography
Mathieu Salzmann is a Senior Scientist and Lecturer at EPFL, and, since May 2020, a part-time Artificial Intelligence Engineer at ClearSpace. Previously, he was a Senior Researcher and Research Leader in NICTA’s computer vision research group. Prior to this, from Sept. 2010 to Jan 2012, he was a Research Assistant Professor at TTI-Chicago, and, from Feb. 2009 to Aug. 2010, a postdoctoral fellow at ICSI and EECS at UC Berkeley under the supervision of Prof. Trevor Darrell. He obtained his PhD in Jan. 2009 from EPFL under the supervision of Prof. Pascal Fua. Mathieu Salzmann’s research lies at the intersection of machine learning and visual recognition. He has published over 100 articles at top-tier peer-reviewed machine learning and computer vision venues, including CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, IEEE TPAMI, IJCV, JMLR, and IEEE TNN-LS. He regularly acts as an Area Chair for these venues and is an editorial board member for IEEE TPAMI and TMLR.Publications
Other publications
Teaching & PhD
Teaching
Computer Science
Digital Humanities
Communication Systems
PhD Students
Bouquet Yann Yanis, Davydov Andrey, Javed Saqib, Li Shuangqi, Ozaydin Ali Baran, Perez Malo Lucas, Shukla Megh Hiren, Zhang Tianzong, Zhao Chen,Past EPFL PhD Students
Bednarík Jan , Bhattacharjee Deblina , Guo Shuxuan , Katircioglu Isinsu , Kiciroglu Sena , Lis Krzysztof Maciej , Liu Chen , Nakka Krishna Kanth , Vidit Vidit , Yu Kaicheng ,Courses
Introduction to machine learning
Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.
Machine learning for DH
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.