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Administrative data

Volkan Cevher

volkan.cevher@epfl.ch http://lions.epfl.ch

EPFL STI IEL LIONS
ELE 233 (Bâtiment ELE)
Station 11
1015 Lausanne

Expertise

Machine Learning
Optimization
Reinforcement Learning
Deep learning
Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park, from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. He was also a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University from 2010-2020. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and an Amazon Scholar. His research interests include machine learning, optimization theory and methods, and automated control. Dr. Cevher is an IEEE Fellow ('24), an ELLIS fellow, and was the recipient of the ICML AdvML Best Paper Award in 2023, Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.

Awards

Marie Curie International Reintegration, Transnational Mobility Award

2010

ERC-StG

European Research Council

2011

Best Paper Award at AdvML-Frontiers Workshop

2023

Google Faculty Research Award

2019

Fellowship to IEEE (Institute of Electrical and Electronics Engineers)

IEEE

2023

Selected Publications

Infoscience

Teaching & PhD

PhD Students

Arshia Afzal, Melis Ilayda Bal, Leyla Naz Candogan, Ioannis Mavrothalassitis, Pol Puigdemont Plana, Yongtao Wu, Zhengqing Wu, Wanyun Xie, Zhenyu Zhu

Past EPFL PhD Students

Elias Abad Rocamora (2026), Pedro Abranches De Carvalho (2026), Ahmet Alacaoglu (2021), Cosimo Aprile (2018), Ilija Bogunovic (2019), Leello Tadesse Dadi (2026), Marwa El Halabi (2018), Baran Gözcü (2019), Ya-Ping Hsieh (2020), Rabeeh Karimi Mahabadi (2023), Ali Kavis (2023), Igor Krawczuk (2024), Anastasios Kyrillidis (2014), Fabian Latorre (2023), Yen-Huan Li (2018), Thomas Michaelsen Pethick (2026), Paul Thierry Yves Rolland (2022), Mehmet Fatih Sahin (2023), Thomas Sanchez (2022), Arda Uran (2021), Luca Viano (2026), Alp Yurtsever (2019)

Courses

EECS Seminar: Advanced Topics in Machine Learning

ENG-704

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.

Mathematics of data: from theory to computation

EE-556

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees, describe scalable solution techniques and algorithms, and illustrate the trade-offs involved.

Online learning in games

EE-735

This course provides an overview of recent developments in online learning, game theory, and variational inequalities and their point of intersection with a focus on algorithmic development. The primary approach is to lay out the different problem classes and their associated optimal rates.

Reinforcement learning

EE-568

This course describes theory and methods for Reinforcement Learning (RL), which revolves around decision making under uncertainty. The course covers classic algorithms in RL as well as recent algorithms under the lens of contemporary optimization.

Training Large Language Models

EE-628

This PhD-level course dives deep into the training of Large Language Models (LLMs), focusing on the complementary roles of datasets, pre-training and post training methodologies in shaping model performance and scalability.

Current courses

EE-556 - Mathematics of Data: From Theory to Computation (6ECTS)
EE-568 - Reinforcement Learning  (6ECTS)
EE-628 - Training Large Language Models (4ECTS)
EE-735 - Online Learning in Games (3ECTS)