Hello, since Nov. 2018, I am on an “advanced research position” at Inria, in the WIDE team. My current and main topics of research are

• the study of black-box algorithms in the context of recommender systems, and the related scalable computation
• algorithms for the distribution of machine learning (deep networks, GANs)

I was a senior research scientist at Technicolor R&I (2009-2018), where I worked on scalable storage, processing and machine learning for data analytics and monitoring of home devices. I own a PhD on distributed systems from University of Rennes 1, and a background on peer-to-peer systems, and graph mining/algorithms. My Ph.D. thesis was financed by Orange Labs (where I stayed btw 2004-2007). I obtained my habilitation (HDR) on November 2016 from University of Rennes 1. I am the president and sysadmin of the gozdata association, that provides the gozmail service, built from free software.

News

• Article sur les enjeux de l’explicabilité, mentionant nos travaux, sur internetactu.net.
• Talk au THCON20 @ Toulouse avec G. Trédan.
• Joined the board of the Société Informatique de France.
• Article from Algorithm Watch (Berlin) on our “bouncer problem” “Explainable AI” doesn’t work for online services – now there’s proof
• Atelier “algorithmes en boite-noire”, le 10 octobre 2019 à Lyon.
• Article Emergences/Inria “Plus de transparence pour les algorithmes de recommandation” par J-M Prima.
• Talk video about “Decision boundaries & security related questions” @ the Deep Learning : from theory to applications workshop in Sept. 2018 in Rennes: VIDEO HERE.
• Second order centrality code now part of the Networkx library v2.2.

Recent publications

• The Bouncer Problem: Challenges to Remote Explainability, Erwan Le Merrer, Gilles Trédan. [ArXiv preprint 2019].

• Adversarial Frontier Stitching for Remote Neural Network Watermarking, Erwan Le Merrer, Patrick Perez, Gilles Trédan. In the journal of Neural Computing and Applications, 2019. [editor]

• TamperNN: Efficient Tampering Detection of Deployed Neural Nets, Erwan Le Merrer, Gilles Trédan. In ISSRE, 2019. [preprint]

• Unified and Scalable Incremental Recommenders with Consumed Item Packs, Rachid Guerraoui, Erwan Le Merrer, Rhicheek Patra, Jean-Ronan Vigouroux. In Euro-par 2019. [preprint].

• Application-aware adaptive partitioning for graph processing systems, Erwan Le Merrer, Gilles Trédan. (6p short) In MASCOTS 2019. [preprint]

• MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets, Corentin Hardy, Erwan Le Merrer, Bruno Sericola. In IPDPS, 2019. [preprint].

• Gossiping GANs, Corentin Hardy, Erwan Le Merrer, Bruno Sericola. In Middleware’s Workshop on Distributed Infrastructures for Deep Learning, 2018. [preprint] [ACM ref]

• zoNNscan : a boundary-entropy index for zone inspection of neural models, Adel Jaouen, Erwan Le Merrer. [ArXiv preprint 2018].

• Distributed deep learning on edge-devices: feasibility via adaptive compression, Corentin Hardy, Erwan Le Merrer, Bruno Sericola. In IEEE NCA, 2017. Best paper award [preprint].

• The topological face of recommendation, Erwan Le Merrer, Gilles Trédan. In Complex Networks 2017. [preprint].

• Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services, Erwan Le Merrer, Gilles Trédan. In ACM CSCW, 2017. [preprint].

Selected publications

• Adversarial Frontier Stitching for Remote Neural Network Watermarking, Erwan Le Merrer, Patrick Perez, Gilles Trédan. Neural Computing and Applications, 2019.

• Frugal topology construction for stream aggregation in the cloud, Rachid Guerraoui, Erwan Le Merrer, Rhicheek Patra, Bao-Duy Tran. Infocom, 2016.

• Archiving cold data in warehouses with clustered network coding, Fabien André, Anne-Marie Kermarrec, Erwan Le Merrer, Nicolas Le Scouarnec, Gilles Straub, Alexandre van Kempen. Eurosys, 2014.

• Second order centrality: Distributed assessment of nodes criticity in complex networks, Anne-Marie Kermarrec, Erwan Le Merrer, Bruno Sericola, Gilles Trédan. Computer Communications, 2011.

• Peer counting and sampling in overlay networks based on random walks, Ayalvadi J. Ganesh, Anne-Marie Kermarrec, Erwan Le Merrer, Laurent Massoulié. Distributed computing, 2007.

Code

• Pushed non-randomness computation for graphs, by Ying & Wu, in NetworkX 2.4.
• zoNNscan code, for analysis of decision boundaries, available here.
• Second order centrality code now part of the Networkx library, from ComCom paper.
• Corentin’s code for distributed deep learning (NCA best paper) here and for random neural layers here.

Awards

• IEEE NCA 2017 best paper award, for paper “Distributed deep learning on edge-devices: feasibility via adaptive compression”, with Corentin Hardy and Bruno Sericola.
• Paper co-authors are awarded prize “La recherche 2015”, for work on cold storage, from Eurosys 2014.
• Prix “Bretagne jeune chercheur” 2011, mention spéciale.

Service

As my main participation to scientific events, I was co-chair of Middleware 2018 industry track, in Rennes, and AlgoTel 2019 program co-chair. Co-organizing WOS with INRIA and Interdigital (ex Technicolor), a workshop on storage, cloud processing and data analytics, in every year Rennes: WOS archive.

I have co-authored 8 granted patents, and around 39 pending demands.

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