Yann-Aël Le Borgne

I am postdoc researcher, working jointly for CoMo and ETRO/IRIS since October 2009 for an FWO project focused on QoS support in heterogeneous wireless sensor networks using distributed Reinforcement Learning techniques, in collaboration with the INTEC Broadband Communication Networks at the University of Gent.

I am interested in machine learning algorithms, with a focus on their applications to the technologies related to the Internet of Things. Domains of particular interests include reinforcement learning, model identification and model selection, feature extraction, compressed sensing, and sensor networks.

Current work involves the design of adaptive scheduling, routing and data processing techniques for extracting data from heterogeneous sensor networks. I work with motes such as TelosB and MICAz, using Contiki and TinyOS operating Systems, for projects related to climatic and sound monitoring.

• Mihaylov M., Le Borgne Y., Nowe A., and Tuyls K., “Self-Organizing Synchronicity and Desynchronicity using Reinforcement Learning”, accepted to the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011).
• Mihaylov M., Le Borgne Y., Nowe A., and Tuyls K., “Decentralized Reinforcement Learning for Wake-up Scheduling”, 7th European conference on wireless sensor networks (EWSN 2010), pp.49 - 51, 2010.
• Le Borgne Y., Nowe A., Steenhaut K., and Bontempi G., “Demo Abstract: Demonstrating Principal Component Aggregation for Distributed Spatial Pattern Recognition”, 9th International Conference on Information Processing in Sensor Networks, issue April 12, 2010 Stockholm, Sweden, pp.430 - 431, eds. Tarek Abdelzaher, Thiemo Voigt, Adam Wolisz, published by ACM, 2010.
• Abughalieh N., Le Borgne Y., Steenhaut K., and Nowé A., “Lifetime Optimization for Wireless Sensor Networks with Correlated Data Gathering”, 8th Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp.252 - 258, eds. Eitan Altman, Tamer Basar, Imrich Chlamtac, 2010.
• Le Borgne Y., Nowe A., Abughalieh N., and Steenhaut K., “Distributed regression for high-level feature extraction in wireless sensor networks”, 7th International Conference on Networked Sensing Systems, pp.249 - 252, eds. Hartmut Hillmer, Masateru Minami, published by IEEE, 2010.
• Le Borgne Y., Learning in Wireless Sensor Networks for Energy-Efficient Environmental Monitoring. PhD Thesis, Université Libre de Bruxelles, 2009. ThesisSlides
• Le Borgne Y., Dricot J.M., Bontempi G., Principal Component Aggregation for Energy-efficient Information Extraction in Wireless Sensor Networks. Chapter 5 of Knowledge Discovery from Sensor Data, pp. 55-80. Taylor and Francis/CRC Press, 2008.
• Le Borgne Y., Raybaud S., and Bontempi G., Distributed Principal Component Analysis for Wireless Sensor Networks. Sensors Journal, MDPI, Volume 8, Issue 8, August 2008, Pages 4821-4850.
• Miranda A. A., Le Borgne Y., and Bontempi G., New Routes from Minimal Approximation Error to Principal Components. Neural Processing Letters, Springer, Volume 27, Issue 3, June 2008, Pages 197-207.
• Le Borgne Y., Santini S. and Bontempi G., Adaptive Model Selection for Time Series Prediction in Wireless Sensor Networks. Journal of Signal Processing, Elsevier, Volume 87, Issue 12, December 2007, Pages 3010-3020.

• See here for previous work.