Patrice Abry, Physicist, Physics laboratory


CNRS Research Director
Director of IXXI

2023: EURASIP Fellow


Born in 1966, Patrice Abry studied at the ENS Cachan from 1986 to 1990. He then continued his studies in Lyon, at the University Claude Bernard Lyon 1 where he obtained a DEA in 1991 and a PhD thesis in 1994 - Wavelet transforms. Multiresolution analyses and pressure signals in turbulence - under the supervision of Patrick Flandrin.

He joined the CNRS in 1995 and is now Research Director at the Physics laboratory of ENS de Lyon, in the Signals, Systems and Physics (SiSyPh) research team.

In 2020, he received the Michel Monpetit Prize from the French Academy of Sciences. In 2023, he has been elevated Fellow of the European Association for Signal Processing (EURASIP).

Awards and honours

2023: EURASIP Fellow

2020: Michel Monpetit Prize of the French Academy of Sciences

2016: Best Paper Award, European Signal Processing Conference. (EURASIP), Budapest Joint Work on Multifractal Bayesian Inference, with H. Wendt, S. Combrexelle, J.-Y. Tourneret, S. Mac Laughlin.

2013: Best Paper Award, IEEE Int. Sympos. Computer-Based Medical Syst. (CBMS), Porto, Portugal

2011: IEEE Fellow, elected “contributions to the theory of fractal and multifractal analysis in signal and image processing”

2007: Invited Lecture at the French Academy of Sciences: Advances in Information Sciences, presented by their authors

2007: Young Research Team Award, Del Duca Foundation, French Academy of Sciences, Institut de France

2005: Appreciated Reviewer Distinction, IEEE Transactions on Signal Processing

2000: EURASIP (EURopean Association for SIgnal Processing) Best Paper Award, 2000

1994: Best PhD Thesis in Automatic and Signal Processing for Years 1993 and 1994, granted jointly by CNRS

(National Council for Scientific Research), AFCET


Scaling, Fractals and WaveletsScaling, Fractals and Wavelets
Patrice Abry, CNRS
Paulo Gonçalves, INRIA Rhone-Alpes
and Jacques Lévy Véhel, INRIA Orsay
ISTE Ltd., 2009
ISBN: 9781848210721

This book is organized around the notions of scaling phenomena and scale invariance. The various stochastic models commonly used to describe scaling are introduced: self-similarity, long-range dependence and multi-fractals. These models are compared and related one to the other. Second, they introduce fractional integration, a mathematical tool closely related to the notion of scale invariance. Also, they define stochastic processes with prescribed scaling properties (self-similar processes, locally self-similar processes, fractionally filtered processes, iterated function systems). A number of applications where the scaling paradigm proved fruitful are detailed: image processing, financial and stock market fluctuations, geophysics, scale relativity and fractal time-space.