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Administrative data

Olivier Lévêque

olivier.leveque@epfl.ch +41 21 693 81 12 http://ipgold.epfl.ch/~leveque/index.html

Nationality: CH

EPFL IC IINFCOM LTHI
INR 132 (Bâtiment INR)
Station 14
1015 Lausanne

Expertise

Information theory
Random matrices
Stochastic calculus
Olivier Lévêque was born in Switzerland in 1971. He received the physics diploma from EPFL in 1995 and completed his PhD in mathematics at EPFL in 2001. Since then, he has been with the Laboratory of Information Theory at EPFL. He spent the academical year 2005-2006 at the Electrical Engineering Department of Stanford University, where he was appointed as lecturer. His research interests include stochastic analysis, random matrices, wireless communications and information theory.

PUBLICATIONS

Google Scholar Profile

Infoscience

Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks

A. Özgür; O. Lévêque; D. Tse

IEEE Transactions on Information Theory. 2007. DOI : 10.1109/TIT.2007.905002.

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Scaling Laws for One and Two-Dimensional Random Wireless Networks in the Low Attenuation Regime

A. Özgür; O. Lévêque; E. Preissmann

IEEE Transactions on Information Theory. 2007. DOI : 10.1109/TIT.2007.904979.

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Second-order hyperbolic S.P.D.E.'s driven by homogeneous Gaussian noise on a hyperplane

R. C. Dalang; O. Lévêque

Transactions of the American Mathematical Society. 2006. DOI : 10.1090/S0002-9947-05-03740-2.

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Information-Theoretic Upper Bounds on the Capacity of Large Extended Ad Hoc Wireless Networks

O. Lévêque; E. Telatar

IEEE Transactions on Information Theory. 2005. DOI : 10.1109/TIT.2004.842576.

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Second-order linear hyperbolic SPDEs driven by isotropic Gaussian noise on a sphere

R. C. Dalang; O. Lévêque

Annals of Applied Probability. 2004. DOI : 10.1214/aop/1079021472.

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Teaching & PhD

Past EPFL PhD Students

Ayfer Özgür Aydin

Past EPFL PhD Students as codirector

Alla Merzakreeva, Marc Desgroseilliers, Serj Haddad

Courses

Information, Computation, Communication

CS-119(h)

The course objectives are to introduce the students to algorithmic thinking, to get them familiar with the foundations of communication and computer sciences and to develop a first set of skills in programming with the Python language.

Information, Computation, Communication

CS-119(a)

On one side, this course covers the concepts of algorithms, the representation of information, signal sampling and compression, and an overview of systems (CPU, memory, etc.). On the other side, an introduction to programming is given.

Markov chains and algorithmic applications

COM-516

The study of random walks finds many applications in computer science and communications. The goal of the course is to get familiar with the theory of random walks, and to get an overview of some applications of this theory to problems of interest in communications, computer and network science.