Assistant Professor -- Maître de Conférences Laboratoire Jean Kuntzmann (LJK) , Université Grenoble Alpes, DAO Team Full CV : [EN]/[FR] -- Google Scholar Profile Contact : firstname.lastname |a|_ univ-grenoble-alpes.fr /iutzeler Franck Iutzeler 0000-0003-2537-380X @FranckIutzeler News Nov. 2018 : I gave a talk about the Python library Pandas at the Python group of the Univ. Grenoble Alpes. The notebook of the presentation is on [GitHub] and here is a possible solution of the exercises [Notebook]. Jul. 2018 : I presented our work A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning at ICML in Stockholm: [Slides] [Poster]. We recently posted a paper on ArXiv that pushes these ideas further in terms of mathematical optimization [ArXiv] Juin 2018 : With colleages from the DAO team, we organize the Grenoble Optimization Days. This event also celebrates the axe Grenoble-Moscow, and is co-organized with Alexander Gasnikov (MIPT, Moscou). Partial funding by my CNRS I3A project. Feb. 2018 : Mini-course by Julien Mairal on Optimization and Learning, funded by the GdR MOA, in prelude to the SMAI-MODE conference. Details here. Oct. 2017 : Dmitry Grishchenko began his PhD thesis in our team. He is co-supervised by J. Malick, M.-R. Amini, and I and will work on distributed optimization algorithms. Sept. 2017 : Bikash Joshi defended his PhD thesis on some large-scale learning methods. He was co-supervised by M.-R. Amini and I and is now a data scientist in the private sector. June 2017 : The conference CAp 2017 (Conférence sur l'apprentissage automatique -- 19th annual meeting of the francophone Machine Learning community) held in Grenoble June 28-30 was a great success. It was co-organized by the AMA team at LIG and our team DAO at LJK, the general chair was M.-R. Amini. Mar. 2017 : Our course project on Distributed Optimization for Big Data was granted a development funding from IDEX Grenoble Alpes. The Goal is to raise our Convex and Distributed Optimization to the next level with state of the art technologies (Python/Jupyter, Spark, Docker) and to a wider audience. Nov. 2016 : I gave a tutorial talk about Gossip algorithms at the Machine Learning seminar SMILE in Paris. Slides: Part I -- Gossip algorithms , Part II -- Gossip and Optimization. (The animations may not work with all PDF viewers, they should do fine with Adobe's) Sept. 2016 : Jerome Malick and I taught a concentrated (6h per day during 3 days) Refresher course on Numerical Matrix Analysis and Optimization for students of different Master at Grenoble (MSIAM, MoSig, MSCIT). Details can be found in the introduction presentation. Apr. 2016 : The website of our new team DAO ``Optimization and Learning for Data Sciences'' is online! Sept. 2015 : I joined the Laboratoire Jean Kuntzmann (LJK) , Université Grenoble Alpes as an Assistant Professor (Maitre de Conférences) Feb. 2015 : I joined the Université Catholique de Louvain as a Post-Doc, in the ICTEAM Institute working mainly with Julien Hendrickx Mar. 2014 : I have designed a small script to visualize the contraction properties of alpha-averaged operators (necessits the support of HTML5 by your browser). This is still a beta version, email me if you have remarks/comments/...! Jan. 2014 : I joined the Alcatel-Lucent Chair on Flexible Radio as a Post-Doc with funding from the ERC More (Advanced Mathematical Tools for Complex Network Engineering) . working mainly with Romain Couillet and Merouane Debbah Dec. 2013 : I defended my Ph.D. thesis entitled Distributed Estimation and Optimization in Asynchronous Networks and prepared at Telecom ParisTech . Advisors : Philippe Ciblat and Walid Hachem Jury : Cédric Richard (President), Michael Rabbat (Reviewer), Julien Hendrickx (Reviewer), Geert Leus (Examiner), Pierre Borgnat (Examiner) and Pascal Bianchi (Invited). Presentation Slides. Talks Oct. 2018 : Distributed Learning with Sparse Communications and Structure Identification, Séminaire INRIA Magnet, Lille (France). Jul. 2018 : Distributed Learning with Sparse Communications and Structure Identification, International Symposium on Mathematical Programming (ISMP), Bordeaux (France). June 2018 : Distributed Learning with Sparse Communications and Structure Identification, Séminaire Polaris, Grenoble (France). June 2018 : Distributed Learning with Sparse Communications and Structure Identification, Séminaire D.A.T.A., Grenoble (France). May. 2018 : Distributed Learning with Sparse Communications and Structure Identification, Journées de Statistique, Saclay (France). Apr. 2017 : Monotonicity, Acceleration, Inertia, and the proximal gradient algorithm , Optimization and Statistical Learning, Les Houches (France). [Slides] Nov. 2016 : Gossip Algorithms: Tutorial and Recent advances , SMILE in Paris, Paris (France). Oct. 2016 : Modified fixed points iterations and applications to randomized and accelerated optimization algorithms , Workshop Cavalieri, Paris (France). Sep. 2016 : Practical acceleration for some optimization methods using relaxation and inertia , Seminaire d'Analyse non lineaire et Optimisation, Avignon (France). June 2016 : Practical acceleration for some optimization methods using relaxation and inertia , Seminaire Signal-Image de l'Insitut de Mathematiques de Bordeaux, Bordeaux (France). June 2016 : Practical accelerations for the alternating direction method of multipliers , PICOF Workshop , Autrans (France). May 2016 : Descente par coordonnéees stochastique dan l'algorithme du point fixe et application aux méthod d'optimisation , Congres d'Analyse Numerique (CANUM) , Obernai (France). Nov. 2015 : Relaxation and Inertia on the Proximal Point Algorithm , Titan Workshop , Grenoble (France). Nov. 2015 : Relaxation and Inertia on Fixed point algorithms , Journées EDP Rhone-Alpes-Auvergne (JERAA), Clermont-Ferrand (France). Mar. 2015 : Online Relaxation Method for Improving Linear Convergence Rates of the ADMM , Benelux meeting on Systems and Control, Lommel (Belgium). Aug. 2014 : Asynchronous Distributed Optimization , Journées MAS, Toulouse (France). May. 2014 : Distributed Optimization Techniques for Learning over Big Data , 2014 ESSEC/Centrale-Supélec Conference Bridging Worlds in Big Data, ESSEC CNIT Campus, La Défense Paris (France). Apr. 2014 : Distributed Asynchronous optimization using the ADMM, Large graphs and networks seminar, Université Catholique de Louvain-la-Neuve , ICTEAM institute, Louvain-La-Neuve (Belgium). [ slides ] Jul. 2013 : Distributed Optimization using a Randomized Alternating Direction Method of Multipliers , Digicosme Research Day, Digiteo, Gif-sur-Yvette. Nov. 2012 : Distributed Estimation of the Average Value in Wireless Sensor Networks , Alcatel-Lucent Chair Seminar, Supélec, Gif-sur-Yvette. Apr. 2012 : Some useful results on Matrix Products for Signal Processing , Ph.D. Candidates Seminar, Telecom ParisTech, Paris. Oct. 2011 : Distributed Maximal Value Estimation , Ph.D. Candidates Seminar, Telecom ParisTech, Paris. Topics -- Students -- Fundings Research Topics Optimization algorithms Distributed Optimization Gossip Algorithms Students PhD Students Dmitry Grishchenko (2017- at LJK, Grenoble). Co-advised (50%) with J. Malick (LJK, Grenoble) and M.-R. Amini (LIG, Grenoble) Bikash Joshi (2014-2017 at LIG, Grenoble). Co-advised (50%) with M.-R. Amini (LIG, Grenoble) Master Students Konstantin Mishchenko (Apr.-Sept. 2017 at LJK, Grenoble). Co-advised (50%) with J. Malick (LJK, Grenoble) and M.-R. Amini (LIG, Grenoble) Taha Essalih (Apr.-Sept. 2017 at LEME, Paris X). Co-advised (15%) with N. El Korso, A. Breloy (LEME, Paris X) and R. Flamary (Lagrange, Nice) Funding CNRS INSMI and INS2I - Intelligence artificielle et apprentissage automatique project PI, 8kE, 2018 with M. Clausel (IECL, U. Lorraine), M.-R. Amini (LIG, Grenoble). IDEX Grenoble Alpes - Initiatives de Recherche Stratégiques project Distributed Optimization for Large-scale Learning PI, 1 PhD funding + 3kE, 2017-2020 with J. Malick (LJK, Grenoble), M.-R. Amini (LIG, Grenoble). IDEX Grenoble Alpes - Pedagogical Initiatives project Optimisation Distribuée pour le Big Data approx. 30kE, 2017-2019 with J. Malick (PI), A. Iouditski, R. Hildenbrand, J. Lelong, L. Viry (LJK, Grenoble). PGMO project advanced nonsmooth optimization methods for stochastic programming 12kE, 2016-2018 with J. Malick (PI) (LJK, Grenoble), W. Van Ackooij (EDF, Paris), W. de Oliveira (UERJ, Rio de Janeiro, Brésil). Jeunes Chercheurs GDR ISIS/GRETSI project "ON FIRE" Calibration des futurs grands interféromètres co-PI, 7kE, 2016-2018 with N. El Korso (co-PI), A. Breloy (LEME, Paris X), R. Flamary (Lagrange, Nice). Funding for my PhD thesis Distributed Estimation and Optimization over Wireless Networks by the French Defense Agency (DGA) and Institut Carnot Telecom-Eurecom Preprints -- Journal articles -- Conferences -- Thesis Preprints - K. Mishchenko, F. Iutzeler, and J. Malick : A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm , June 2018. Journal articles 10- F. Iutzeler and L. Condat : Distributed Projection on the Simplex and l1 Ball via ADMM and Gossip, to appear in IEEE Signal Processing Letters, Aug. 2018. 9- F. Iutzeler and J. Malick : On the Proximal Gradient Algorithm with Alternated Inertia , Journal of Optimization Theory and Applications, vol. 176, no. 3, pp. 688-710, March 2018. 8- B. Joshi, F. Iutzeler and M.-R. Amini : Large-scale asynchronous distributed learning based on parameter exchanges , to appear in International Journal of Data Science and Analytics, 2018. 7- F. Iutzeler and J. M. Hendrickx : A Generic online acceleration scheme for Optimization algorithms via Relaxation and Inertia , Optimization Methods and Software, vol. 34, no. 2, 2019. 6- F. Iutzeler : Distributed Computation of Quantiles via ADMM, IEEE Signal Processing Letters, vol. 24, no. 5, pp. 619-623, May 2017. 5- P. Bianchi, W. Hachem, and F. Iutzeler : A Stochastic Coordinate Descent Primal-Dual Algorithm and Applications to Distributed Optimization , IEEE Transactions on Automatic Control, vol. 61, no. 10, pp. 2947-2957, Oct. 2016. 4- F. Iutzeler, P. Bianchi, P. Ciblat, and W. Hachem : Explicit Convergence Rate of a Distributed Alternating Direction Method of Multipliers, IEEE Transactions on Automatic Control, vol. 61, no. 4, pp. 892-904, Apr. 2016. 3- A. Abboud, F. Iutzeler, R. Couillet, M. Debbah, and H. Siguerdidjane Distributed Production-Sharing Optimization and Application to Power Grid Networks , IEEE Transactions on Signal and Information Processing over Networks, vol. 2, no. 11, pp. 16-28, March 2016. 2- F. Iutzeler, P. Ciblat, and W. Hachem : Analysis of Sum-Weight-like algorithms for averaging in Wireless Sensor Networks, IEEE Transactions on Signal Processing, vol. 61, no. 11, pp. 2802-2814, June 2013. 1- F. Iutzeler, P. Ciblat, and J. Jakubowicz : Analysis of max-consensus algorithms in wireless channels, IEEE Transactions on Signal Processing, vol. 60, no. 11, pp. 6103-6107, November 2012. International Conferences 10- K. Mishchenko, F. Iutzeler, J. Malick, M.-R. Amini: A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning, 35-th International Conference on Machine Learning (ICML), PMLR 80:3584-3592, Stockholm (Sweden), July 2018. 9- B. Joshi, M.-R. Amini, I. Partalas, F. Iutzeler, Yu. Maximov: Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification, Advances in Neural Information Processing Systems 30 (NIPS) , Dec. 2017. 8- B. Joshi, F. Iutzeler, M.-R. Amini: Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization, 10-th ACM Conference on Recommender Systems (RecSys), Boston (USA), Sept. 2016. 7- F. Iutzeler, P. Bianchi, P. Ciblat and W. Hachem: Linear Convergence Rate for Distributed Optimization with the Alternating Direction Method of Multipliers, 53-rd IEEE Conference on Decision and Control (CDC), Los Angeles (USA), December 2014. 6- P. Bianchi, W. Hachem and F. Iutzeler: A Stochastic Primal-Dual algorithm for Distributed Asynchronous Composite Optimization 2-nd IEEE Global Conference on Signal and Information Processing (GlobalSip), Atlanta (USA), December 2014. 5- P. Bianchi, W. Hachem, and F. Iutzeler : A Stochastic Coordinate Descent Primal-Dual Algorithm And Applications , 24-th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Reims (France), September 2014. 4- F. Iutzeler , P. Bianchi, P. Ciblat and W. Hachem: Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers, 52-nd IEEE Conference on Decision and Control (CDC), Florence (Italy), December 2013. 3- F. Iutzeler and P. Ciblat: Fully-distributed spectrum sensing: application to cognitive radio, 21-st European Signal Processing Conference (EUSIPCO), Marrakech (Morocco), September 2013. 2- F. Iutzeler, P. Ciblat, W. Hachem, and J. Jakubowicz : A new broadcast based averaging algorithm over wireless sensor networks, 37-th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto (Japan), March 2012. 1- F. Iutzeler, J. Jakubowicz, W. Hachem and P. Ciblat : Distributed estimation of the maximum value over a wireless sensor network, 45-th Asilomar Conference on Signals, Systems, and Computer, Pacific Grove (USA), November 2011. Local Conferences 2- F. Iutzeler and P. Ciblat : Sondage de spectre coopératif totalement distribué : application à la radio cognitive (in french) , XXIVe Colloque GRETSI, Brest (France), September 2013. 1- F. Iutzeler, P. Ciblat, W. Hachem, and J. Jakubowicz : Estimation distribuée du maximum dans un réseau de capteurs (in french) , XXIIIe Colloque GRETSI, Bordeaux (France), September 2011. Thesis F. Iutzeler : Distributed Estimation and Optimization in Asynchronous Networks , Ph.D. Thesis, Telecom ParisTech, defended December 6th, 2013. 2018/2019 Mathematics of Operations Research at Universite Grenoble Alpes -- Master 1 MSIAM (EN) Spectral Graph Theory, Game Theory, Optimal Transport, etc. Numerical Optimization at Universite Grenoble Alpes -- Master 1 MSIAM (EN) Optimization 101 Course by L. Desbat Some [Topics and references] Tutorials: Tuto 1&2: Differentiation, Convexity, Gradient algorithm [PDF] Tuto 3: Proximal operations, proximal gradient algorithm [PDF] Tuto 4: Convergence rates [PDF] Tuto 5: Linear and Quadratic Programss [PDF] Tuto 6: ADMM [PDF] Labs: on GitHub Mathématiques pour l'ingénieur at Universite Grenoble Alpes -- L2 SPI (FR) Linear Algebra, Differential Equations, etc. Convex and Distributed Optimization at Universite Grenoble Alpes -- Master 2 MSIAM/MOSIG(EN) Incremental and Stochastic Optimization for Learning, Spark, Distributed Optimization See the course website Refresher in Matrix Analysis and Numerical Optimization at Universite Grenoble Alpes -- Master 2 MSIAM/MOSIG/etc. (EN) Reminder in Matrix analysis and optimization. Syllabus and Exercices: [PDF] Notebooks: on GitHub Chap 1: Python and NumPy Basics (look at that part before the practical sessions) Chap 2-1: Matrix Part (Wed. AM) -- [Possible solution (HTML)] Chap 2-2: Optimization Part (Fri. AM) Python for Data Sciences at Universite Grenoble Alpes -- Master 1 and 2 SSD (FR/EN) Python, NumPy, Scikit-learn, etc. Notebooks: on Github Correction to the Image SVD exercise [Python] Correction of the least squares exercise [Python] Correction to 3-2 bots and planets exercises [Notebook] Correction to 4-2 supervised ML [Notebook] Projects: [List] Statistiques pour la biologie at Universite Grenoble Alpes -- L2 BIO (FR) Estimateurs,Tests, etc. 2017/2018 Numerical Optimization at Universite Grenoble Alpes -- Master 1 MSIAM (EN) Optimization 101 Course by L. Desbat Some [Topics and references] Tutorials: Tuto 1: Differentiation, Convexity, Gradient algorithm with rates [Tuto1 PDF] Tuto 3: Proximal operations, proximal gradient algorithm [Tuto3 PDF] Tuto 4: Linear and Quadratic Problems and Reformulation [Tuto4 PDF] Labs: Lab 1: Structure of an Optimization program, Gradient algorithms [Lab1 Notebooks]v2.1 [Common coding mistakes] Lab 2: Descent algorithms [Lab2 Notebooks]v1.0 Lab 3: Performance of Optimization algorithms on learning problem [Lab3 Notebooks]v2.0 Lab 4: The Proximal Gradient algorithm and sparsity [Lab4 Notebooks]v1.0 Lab 5: Linear and Quadratic Programs [Lab5 Notebooks]v1.1 [HTML Solution] Lab 6: [1-ADMM Notebooks] or [2-VRSG] Optimisation Numérique at Universite Grenoble Alpes -- ENSIMAG 2A (FR) Numerical Optimization Course by J. Malick Material and Informations: TP1: Programmation Linéaire et Quadratique [Notebook] TP2: Algorithmes de descente de gradient et (quasi-) Newton [Notebooks] TP3: Algorithmes Proximaux [TP3 Notebooks] TP4: Faisceaux [TP4 HTML] Introduction à la Recherche Operationelle at Universite Grenoble Alpes -- Master 1 SSD (FR) LP, QP, CVX, Mosek,... in R Matériel: [TP0] Introduction [HTML] [TP1] Diet Planning [HTML] - data: [Zip] - [Solutions HTML] [TP2] Dantzig Selector [HTML] [R function] [TP3] Lasso [HTML] - data: [Zip] - lire les donnees: [R] [TP4] Regression Logistique [HTML] - data: [Zip] Convex and Distributed Optimization at Universite Grenoble Alpes -- Master 2 MSIAM/MOSIG(EN) Incremental and Stochastic Optimization for Learning, Spark, Distributed Optimization See the course website Introduction to Python for Data Sciences at Universite Grenoble Alpes -- Master 2 SSD (FR/EN) Python, NumPy, Scikit-learn, etc. Notebooks: on Github Correction to 3-2 bots and planets exercises [Notebook] Projects: [List] Refresher in Matrix Analysis and Numerical Optimization at Universite Grenoble Alpes -- Master 2 MSIAM/MOSIG/etc. (EN) Reminder in Matrix analysis and optimization. Exercices: [PDF] Notebooks: on GitHub Chap 1: Python and NumPy Basics (look at that part before the practical sessions) Chap 2.1: Matrix Part (Wed. AM) Chap 2.2: Optimization Part (Fri. AM) 2016/2017 Optimisation Numérique at Universite Grenoble Alpes -- ENSIMAG 2A (FR) Numerical Optimization Course by J. Malick Material and Informations: TP1: Algorithmes de descente de gradient et (quasi-) Newton [TP1 Notebooks] TP2: Programmation Linéaire et Quadratique [TP2 Notebooks] TP3: Algorithmes Proximaux [TP3 Notebooks] Numerical Optimization at Universite Grenoble Alpes -- Master 1 MSIAM (EN) Optimization 101 Course by L. Desbat Tutorials: Tuto 1: Differentiation, Convexity, Gradient algorithm with rates [Tuto1 PDF] [Tuto1+ PDF] Tuto 4: Proximal operations, proximal gradient algorithm [Tuto4 PDF] Tuto 5: Linear and Quadratic Problems and Reformulation [Tuto5 PDF] Labs: Lab 1: Structure of an Optimization program, Gradient algorithms [Lab1 Notebooks]v2.1 [Common coding mistakes] Lab 2: Descent algorithms [Lab2 Notebooks]v1.0 Lab 3: Performance of Optimization algorithms on learning problem [Lab3 Notebooks]v1.0 Lab 6: The Proximal Gradient algorithm and sparsity [Lab6 Notebooks]v1.0 Lab 7: Linear and Quadratic Programs [Lab7 Notebooks]v1.0 Lab 8: ADMM [Lab8 Notebooks]v1.0 Introduction à la Recherche Operationelle at Universite Grenoble Alpes -- Master 1 SSD (FR) Operation Research Course by A. Juditsky Material and Informations: Intro: Diet planning -- Menu with cal. infos vit. infos, ideal diet file Try to minimize the number of calories while staying equal (or close to) the ideal diet - see Jupyter notebook and HTML version. TP1: PDF Example du papier Candes-Tao TP2: PDF Convex and Distributed Optimization at Universite Grenoble Alpes -- Master 2 MSIAM/MOSIG (EN) Optimization + Spark for Data Science See the course webpage Refresher course on Numerical Linear Algebra and Optimization at Universite Grenoble Alpes -- Master 2 MSIAM/MOSIG/ORCO/MSCIT (EN) Introductory presentation ---- Tutorial and Labs: Numerical Matrix Analysis - Numerical Optimization Time table: Wed. 21st: 9:45-12:45 "Amphi H" 14:00-17:00 "Amphi H" Thu. 22nd: 9:45-12:45 "E301" 14:00-17:00 "Amphi D" Fri. 23rd: 9:45-12:45 "Amphi H" 14:00-17:00 "E103" MAT306/MAT405 -- Mathematiques Apronfondies pour l'ingenieur at Universite Grenoble Alpes -- L2 GC (FR) Complements of Maths for Engineering Arnaud Chauviere 2015/2016 MAP35G -- Mathematiques Appliquees at Universite Joseph Fourier. Applied Maths Travaux diriges : TD0 : Systemes Lineaires TD1 : Algebre Lineaire TD2 : Analyse TD3 : Equations Differentielles Travaux pratiques : TP1 : Systemes lineaires et regression polynomiale -- datasets : data_16 - data_17_1 - data_17_2 TP2 : Regression, Optimisation, et Regularisation -- datasets : data_1 TP3 : Resolution numerique d'equations differentielles MAT236 -- Mathematiques Apronfondies pour l'ingenieur at Universite Joseph Fourier. Complements of Maths for Engineering Material and Informations: See the page of Arnaud Chauviere Optimisation Numerique at ENSIMAG. Numerical Optimization Material and Informations: See the page of Jerome Malick MAT246 -- Mathematiques Apronfondies pour l'ingenieur at Universite Joseph Fourier. Complements of Maths for Engineering Material and Informations: See the page of Arnaud Chauviere MAT121 -- Bases de l'analyse et lien avec l'algèbre at Universite Joseph Fourier. Basics of Calculus and links with Algebra Material and Informations: 2014/2015 Linear Automatic Control at Universite Catholique de Louvain. 2013/2014 Information Theory at ESIPE -- Université Paris-Est Lab of Advanced Optimization for Machine Learning at Telecom ParisTech 2012/2013 Analysis at Université Paris-Est 2011/2012 Advanced Digital Communications at Telecom ParisTech. 2010/2011 Advanced Digital Communications at Telecom ParisTech. Short Vita since 2015 Asst. Prof. (Maitre de conférences) at Laboratoire Jean Kuntzmann, Université Grenoble Alpes 2015 Post-doc at Université Catholique de Louvain 2014 Post-doc at Supelec 2010-2013 Ph.D. student at Telecom ParisTech 2007-2010 Engineering Student at Telecom ParisTech Full Vita Full CV : Français -- Google Scholar Profile Contact Fastest (and most reliable) way to reach me is by email at firstname.lastname |a|_ imag.fr My office address is: Franck IUTZELER Laboratoire Jean Kuntzmann Batiment IMAG - Bureau 153 - Domaine Universitaire 38400 Saint Martin d'Hères FRANCE