Martin Jaggi

Edit profile

martin.jaggi@epfl.ch +41 21 693 70 59 http://mlo.epfl.ch

Google Scholar ID

Group Webpage:

mlo.epfl.ch

EPFL IC IINFCOM MLO
INJ 341 (Bâtiment INJ)
Station 14
1015 Lausanne

+41 21 693 70 59
Office: INJ 341
EPFL > IC > IINFCOM > MLO

+41 21 693 70 59
EPFL > IC > IC-SIN > SIN-ENS

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

+41 21 693 70 59
EPFL > IC > IC-SSC > SSC-ENS

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

vCard
Administrative data

Fields of expertise

Machine Learning, Optimization

Biography

Martin Jaggi is an Associate Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.

Teaching & PhD

Teaching

Computer Science

Communication Systems

PhD Students

Chayti El Mahdi, Fan Dongyang, Fan Simin, Harma Simla Burcu, Hashemi Diba, Hägele Alexander, Kosson Atli, Messmer Bettina Ursula, Mohtashami Amirkeivan, Pagliardini Matteo, Swamy Vinitra,

Past EPFL PhD Students

Cordonnier Jean-Baptiste Francis Marie Juliette , Drumond Lages De Oliveira Mario Paulo , Gupta Prakhar , He Lie , Karimireddy Sai Praneeth Reddy , Koloskova Anastasiia , Lin Tao , Vogels Thijs ,

Courses

Machine learning

Machine learning methods 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.

Topics in Machine Learning Systems

This course will cover the latest technologies, platforms and research contributions in the area of machine learning systems. The students will read, review and present papers from recent venues across the systems for ML spectrum.