Artificial Intelligence for Industry
Academic team
Faculty members of Télécom Paris involved in the chair. Click on a person to read their profile.
Chair holders
Members
PhD students & postdocs
Louise Davy PhD student · since Nov. 2024
Bias, frugality and fairness in AI — survey sampling for pairwise learning
Sarra Khairi PhD student · since Nov. 2024
Generative AI for image and video editing
Sibsankar Singha Postdoc · since Mar. 2025
Unsupervised anomaly detection and explainable predictive maintenance
Antoine Domingues PhD student · since Dec. 2025
Foundation models and continual adaptation for remote sensing image time series
Kian Bakhtari PhD student · since Mar. 2026
Multimodal LLM efficiency for autonomous vehicles
Stephan Clémençon
AI4I Chair Responsible, Full Professor at Télécom Paris
Stephan Clémençon is a full professor at Télécom Paris, Institut Mines-Télécom, and head of the S2A Research Team (Signal, Statistics and Learning). He carries out his research activity in applied math in the Télécom Paris LTCI Lab. His research topics are mainly related to machine learning, probability and statistics. He is in charge of the Big Data Post-Master Degree at Télécom Paris and held the Machine Learning for Big Data Chair from 2013 to 2018.
rankingclusteringanomaly detectiongraph-miningrecommendation systems
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Florence d'Alché-Buc
AI4I Chair Co-Responsible, Full Professor at Télécom Paris
Florence d’Alché-Buc has been since 2014 a full professor at Télécom Paris, an IMT grande école. Previously, she was a professor at Université d’Evry, an ATIGE (Genopole Thematic Incentive Actions) researcher and joint head of the IBISC lab. She launched and managed the Challenges program as part of the PASCAL European network (2004-08) and since 2017, has become the scientific director of the Digiscome Labex. Her research is on machine learning, network inference, structured prediction and dynamical system modeling. She has authored more than 80 articles in international journals and conference proceedings.
machine learningkernel methodsstructured predictionnetwork inferencedynamical systems
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Pavlo Mozharovskyi
AI4I Chair Co-Responsible, Full Professor at Télécom Paris
Pavlo Mozharovskyi joined Télécom Paris as an Assistant Professor in 2018. After having finished his studies at Kyiv Polytechnic Institute in automation control and informatics, he obtained a PhD degree at the University of Cologne in 2014, where he conducted research in nonparametric and computational statistics and classification. He has been a postdoc at Agrocampus Ouest in Rennes with the Centre Henri Lebesgue for a year working on imputation of missing values, and then joined the CREST laboratory at the National School of Statistics and Information Analysis. His main research interests lie in the areas of statistical data depth function, classification, computational statistics, robust statistics, missing values, and data envelopment analysis.
Permanent members
data depthanomaly detectionexplainable AIcomputational statisticsrobust statisticsmissing values
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Roland Badeau
Full Professor at Télécom Paris
Roland Badeau is Full Professor in the Signal, Statistics and Machine learning (S2A) team of the Image, Data, Signal (IDS) Department at Télécom Paris. His research interests focus on statistical modeling of non-stationary signals (including adaptive high-resolution spectral analysis and Bayesian extensions to NMF), with applications to audio and music (source separation, denoising, dereverberation, multipitch estimation, automatic music transcription, audio coding, audio inpainting). He is a co-author of 30 journal papers, over 100 international conference papers, and 4 patents. He is also an Associate Editor of the EURASIP Journal on Audio, Speech, and Music Processing and the IEEE/ACM Transactions on Audio, Speech, and Language Processing.
machine learningdata decompositionmachine listeningMIR
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Pascal Bianchi
Full Professor at Télécom Paris
Pascal Bianchi was born in 1977. He was awarded an MSc from Université Paris 11 and Supélec in 2000 and a doctorate from Université de Marne-la-Vallée in 2003. He was an assistant professor at the Supélec Telecommunication Department from 2003 to 2009. He then joined the Signal, Statistics and Learning (S2A) team at the Télécom Paris LTCI lab. His current research interests are stochastic optimization, probability and signal processing.
statistical signal processingconvex optimizationdistributed optimizationnetwork sensors
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Philippe Ciblat
Full Professor at Télécom Paris
Philippe Ciblat was born in Paris, France, in 1973. He received the Engineering degree from Ecole Nationale Supérieure des Télécommunications (ENST, now called Telecom Paris) and the M.Sc. degree (DEA, in french) in automatic control and signal processing from Université Paris-Saclay, Orsay, France, both in 1996, and the Ph.D. degree from Université Gustave Eiffel, Marne-la-Vallée, France, in 2000. He eventually received the HDR degree from Université Gustave Eiffel, Marne-la-Vallée, France, in 2007.
In 2001, Philippe Ciblat was a Postdoctoral Researcher with the Université de Louvain, Belgium. At the end of 2001, he joined Telecom Paris, as an Associate Professor. Since 2011, he has been (full) Professor in the same institute. From 2009 to 2020, he was the head of Digital Communications Team.
Philippe Ciblat served as Associate Editor for the IEEE Communications Letters from 2004 to 2007. From 2008 to 2012, he served as Associate Editor and then Senior Area Editor for the IEEE Transactions on Signal Processing. From 2014 to 2019, he was member of IEEE Technical Committee “Signal Processing for Communications and Networking”. From 2018 to 2021, he served as Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks.
statistical signal processingsignal processing for digital communicationsresource allocationdistributed optimizationsignal over graphsand machine learning
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Olivier Fercoq
Full Professor at Télécom Paris
Olivier Fercoq is a Full Professor at Télécom Paris. He obtained a Master’s diploma from Université Paris 6 and ENSTA Paris. He studied optimization problems linked to web page references and applications in biology during his PhD at École polytechnique (2009-2012). He spent two years at the University of Edinburgh where he worked on coordinate descent methods. He joined Télécom ParisTech in 2014. His current research focuses on developing and studying optimization algorithms for high-dimensional problems.
optimizationstochastic algorithmsrate of convergencehigh dimensionalparallel computation
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Geoffroy Peeters
Full Professor at Télécom Paris
Geoffroy Peeters is Full Professor in the LTCI S2A team at Télécom Paris. He received his PHDs degree in 2001 and Habilitation in 2013 from University Paris-VI on audio signal processing, data analysis and machine learning. Before joining Télécom Paris, he lead research related to Music Information Retrieval at IRCAM. His current research work is on signal processing, machine learning and deep learning applied to audio and music data analysis.
audio signal processingmachine learning and deep learning
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Gaël Richard
Full Professor at Télécom Paris
Gaël Richard is Professor at Télécom Paris, Institut Mines-Télécom and head of the Image, Data, Signal (IDS) department. His research work lies at the core of digitization and is dedicated to the analysis, transformation, understanding and automatic indexing of acoustic signals (including speech, music, surrounding sounds) and to a lesser extent of heterogeneous and multimodal signals. In particular, he developed several source separation methods for audio and musical signals based on machine learning approaches.
machine listeningmatrix factorizationrepresentation and subspace learningmusic information retrieval (MIR)sound recognitionaudio source separation
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François Roueff
Full Professor at Télécom Paris
François Roueff is a Professor at Télécom Paris, IMT, working in the S2A research team, and the deputy director of the Hadamard mathematical doctoral school. His research areas lie mainly in the fields of statistical signal processing and the analysis and the random modeling of time series and statistics for stochastic processes.
long dependencywave analysisHawkes processlocally stationary processes
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