Chair Holder

Florence d'Alché-Buc

Florence d'Alché-Buc

Professor at Télécom Paris

Florence d'Alché-Buc, the holder of the Data Science & Artificial Intelligence for Digitalized Industry & Services Chair, 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.

As part of her teaching work, she is co-head of the Data Science Master 2 at Université Paris-Saclay jointly awarded by École Polytechnique, ENSAE Paris and Université Paris Sud. She is co-head of new continuous education programs in artificial intelligence: a Post-Master Degree and a specialist certificate. She is also responsible for the Bearing Point Data Science Education Chair.

Keywords: machine learning, kernel methods, structured prediction, network inference, dynamical systems.

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Referents for the axes

Chloé Clavel

Chloé Clavel

Professor at Télécom Paris

Chloé Clavel is a lecturer and professor at Télécom Paris, where she facilitates the Social Computing group (https://www.tsi.telecom-paristech.fr/en/research/1885-2/social-computing-topic/). She is currently working on the interaction between humans and machines, including the analysis of users’ socio-emotional behavior and strategies for socio-emotional interaction. She has contributed to several European and national collaborative projects relating to Social Computing (e.g. H2020 ITN ANIMATAS, aria-valuspa UE-TIC, Labex smart).

Keywords: opinion analysis, natural language processing, learning, speech processing, emotion recognition, human-machine interaction.

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Stephan Clémençon

Stephan Clémençon

Professor at Télécom Paris

Stephan Clémençon is a full professor at Télécom Paris, Institut Mines-Télécom, and head of the S2A Research Team (Statistics and Applications). 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.

Keywords: ranking, clustering, anomaly detection, graph-mining, recommendation systems

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Pavlo Mozharovskyi

Pavlo Mozharovskyi

Associate 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.

Keywords: data depth, computational statistics, robust statistics, missing values, data envelopment analysis.

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François Portier

François Portier

Lecturer at Télécom Paris

François Portier is a lecturer in the LTCI S2A team at Télécom Paris. His PhD at Université de Rennes 1 was on parsimonious predictive models. He was then an FNRS postdoc at Université catholique de Louvain, where he studied certain survival analysis models. His current research work is on sequential Monte-Carlo methods, machine learning for censored and dependent data and parsimonious predictive models.

Keywords: adaptive sampling, MCMC, bootstrap, Markov chain, dimensionality reduction, survival analysis.

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François Roueff

François Roueff

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.

Keywords: long dependency, wave analysis, Hawkes process, locally stationary processes.

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Anne Sabourin

Anne Sabourin

Lecturer at Télécom Paris

Anne Sabourin is assistant professor in the S2A research team at Télécom Paris since 2013. She received her PhD degree in 2013 on multivariate extreme values and Bayesian inference at Lyon 1 University, under the supervision of Anne-Laure Fougères and Philippe Naveau. Her research interests concern multivariate extreme value theory, dependence between rare events, dimension reduction in extreme regions, with various applications ranging from environmental risk to machine learning applications such as anomaly detection.

Keywords: multivariate extreme value theory, rare events, dimension reduction, anomaly detection.

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Giovanna Varni

Giovanna Varni

Lecturer at Télécom Paris

Giovanna Varni joined Télécom Paris as an Assistant Professor in 2017. Her research activities focus on the field of socio-affective computing. Previously, Giovanna Varni was a postdoctoral researcher at the University of Genoa (Italy) on the InfoMus Lab team and then in the INTERACTION team (IMI2S group) at the Pierre et Marie Curie University in Paris 6. Her work focuses on the analysis of non-verbal multimodal signals in human-human interaction and human-machine interaction. Since 2006, she has been involved in several European projects (FP7, EU-ICT, STREP and FET).

Keywords: human-human and man-machine interaction, socio-affective signal processing, interpersonal synchrony, expressive gesture.

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Permanent members

Roland Badeau

Roland Badeau

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.

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Pascal Bianchi

Pascal Bianchi

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 Statistics and Applications (S2A) team at the Télécom Paris LTCI lab. His current research interests are stochastic optimization, probability and signal processing.

Keywords: statistical signal processing, convex optimization, distributed optimization, network sensors.

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Albert Bifet

Albert Bifet

Professor at Télécom Paris

Albert Bifet is Full Professor at Télécom Paris. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He is co-author of a book on Machine Learning from Data Streams at MIT Press. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2018-2012), and ACM SAC Data Streams Track (2019-2012).

Keywords: Artificial Intelligence, Data Streams, IoT, Real-Time Analytics, Machine Learning, Graph Mining.

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Thomas Bonald

Thomas Bonald

Professor at Télécom Paris

Thomas Bonald is a professor at Télécom Paris and heads the DIG Team (Data, Intelligence and Graphs). His research interests are in the analysis of network-structured data. He is one of the main contributors of scikit-network, a Python package for the analysis of massive graphs. He is also interested in multivariate time-series and NLP.

Keywords: Large graphs, spectral methods, clustering, recommender systems.

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Slim Essid

Slim Essid

Professor at Télécom Paris

Slim Essid is Full Professor at Télécom Paris and the coordinator of the Audio Data Analysis and Signal Processing theme (ADASP). His research is on machine learning and artificial intelligence for temporal data analysis, especially multiview learning, structured prediction, representation learning and data decomposition methods. The target applications include machine listening and music content analysis (MIR); multimodal perception: human behavior analysis, affective computing, and EEG data analysis; multimedia content analysis, especially joint audiovisual data analysis.

Keywords: Multiview learning, structured prediction, representation learning, audio and multimodal data.

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Olivier Fercoq

Olivier Fercoq

Lecturer at Télécom Paris

Olivier Fercoq is a lecturer 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.

Keywords: optimization, stochastic algorithms, rate of convergence, high dimensional, parallel computation.

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Robert M. Gower

Robert M. Gower

Lecturer at Télécom Paris

Robert M. Gower joined Telecom Paris as an Assistant Professor in 2017. He is interested in designing and analyzing new stochastic algorithms for solving big data problems in Machine Learning and scientific computing. A mathematician by training, his academic studies started with a Bachelors and a Master’s degree in applied mathematics at the state University of Campinas (Brazil), where he designed the current state-of-art algorithms for automatically calculating high order derivatives using back-propagation. His PhD in stochastic numeric methods at the University of Edinburgh earned him the 2nd place of the 2017 Leslie Fox prize in numerical analysis. In 2016 he was granted the Fondation Sciences Mathématiques de Paris postdoctoral Laureate fund to continue his work as a postdoc in ENS.

Keywords: Stochastic optimization, randomized numerical linear algebra, convex optimization, machine learning, automatic differentiation.

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Ons Jelassi

Ons Jelassi

Teacher and Training manager

Ons Jelassi is a continuing education teacher at Télécom Paris and Télécom Evolution. She supervises Post Master’s Degree and Specialist Certificate programs in data science and artificial intelligence. She has worked in the fields of metrology and network performance as part of her doctoral research and as an audit and expertise consultant for large companies. Her work in the LTCI S2A research team is on the scalability of machine learning algorithms.

Keywords: machine learning, performance, scalability, distributed algorithms.

Laurence Likforman

Laurence Likforman

Associate Professor at Télécom Paris

Laurence Likforman has been an associate professor (certified for doctoral supervision) at Télécom Paris since 1991. She heads the Pattern Recognition course and also teaches Signal Processing, Document Analysis and Statistics. Her research includes the use of recurrent and convolutional neural networks in the recognition and analysis of handwriting.

Keywords: handwriting recognition, Markov models, Bayesian networks, recurrent neural networks.

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Geoffroy Peeters

Geoffroy Peeters

Professor at Télécom Paris

Geoffroy Peeters is full-professor in the LTCI S2A team at Télécom ParisTech. 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 ParisTech, 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.

Keywords: audio signal processing, machine learning and deep learning.

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Gaël Richard

Gaël Richard

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.

Keywords: Machine listening, Matrix Factorization, Representation and subspace learning, Music Information Retrieval (MIR), Sound recognition, Audio source separation.

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Umut Şimşekli

Umut Şimşekli

Associate Professor at Télécom Paris

Umut Şimşekli is an assistant professor at Télécom Paris. He received his PhD degree in 2015 on inference methods for large-scale matrix and tensor factorization models in the Department of Computer Engineering at Bogaziçi University, İstanbul, Turkey. His research interests are in scalable Bayesian machine learning, audio and music processing, and recommendation systems.

Keywords: matrix and tensor factorizations, Markov Chain Monte Carlo, audio and music processing.

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Fabian M. Suchanek

Fabian M. Suchanek

Professor at Télécom Paris

Fabian M. Suchanek is a full professor at Télécom Paris. He developed inter alia the YAGO knowledge base, one of the largest public general-purpose knowledge bases, which earned him a honorable mention of the SIGMOD dissertation award. His interests include information extraction, automated reasoning, and knowledge bases. Fabian has published around 70 scientific articles, among others at ISWC, VLDB, SIGMOD, WWW, CIKM, ICDE, and SIGIR, and his work has been cited more than 7000 times.

Keywords: semantic web, knowledge bases, information extraction, natural language processing, automatic reasoning.

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PhD Students funded by the Chair

Dimitri Bouche

Dimitri Bouche

Intern

Dimitri Bouche is working on a topic that is little explored in machine learning: space-time processes. He will be analyzing functional and operator-valued kernel methods, with a view to meeting several of the challenges inherent to this type of data. Dimitri Bouche is working with Florence d’Alché-Buc, the head of the DSAIDIS Chair and a professor at Télécom Paris.

Keywords: machine learning: space-time processes, analyzing functional, operator-valued kernel methods
Jayneel Parekh

Jayneel Parekh

PhD student

Jayneel Parekh is a PhD student at Telecom Paris since September 2019. He is working under the guidance of Florence d'Alché-Buc and Pavlo Mozharovskyi. His thesis focuses on making machine learning models interpretable by design. He completed his bachelors and masters from IIT Bombay in electrical engineering and is primarily interested in machine learning and its applications to audio/image domains.

Keywords: Interpretable machine learning, deep learning, audio/image analysis

PhD Students

Pierre  Laforgue

Pierre Laforgue

PhD student

Pierre Laforgue is a PhD student in the S²A team of Télécom Paris since October 10th, 2016. A graduate of ENSAE Paris, he also holds the Master 2 "Mathematics, Learning and Human Sciences" from Paris Dauphine University. His thesis, supervised by Florence d'Alché-Buc and Stephan Clémençon, focuses on the unsupervised learning of representations, with the aim of applying it to time series.

Mots-clés : unsupervised learning, representation learning, time series.

Robin Vogel

Robin Vogel

PhD student

Robin Vogel is a PhD student at Télécom Paris since 2017, under the supervision of Stephan Clémençon, Aurélien Bellet (Inria) and Anne Sabourin. He is employed by the leading biometrics and security solutions company IDEMIA, through a CIFRE agreement. He is interested in statistical learning theory and machine learning for identification and access control. He completed his engineering degree from ENSAE Paris as well as the M2 Data Science from Université Paris-Saclay in 2016.

Keywords: similarity learning, ranking, biometrics, statistical learning theory, deep learning.

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Alumni

Jean-Rémy Conti

Jean-Rémy Conti

Intern (2019)

Jean-Rémy Conti will be focusing on machine learning for spatial data. He will be setting a mathematical framework, in order to establish to what extent the rules obtained by empirical risk minimization can be generalized. It will also serve to study other extensions to the spatial context of popular algorithms, both theoretically and experientially. He is being supervised by Stéphan Clémençon, professor at Télécom Paris and Emilie Chautru, co-director of the geostatistics program and a researcher at the Ecole des Mines de Paris Geostatistics Center.

Keywords: machine learning for spatial data, mathematical, spatial context of popular algorithms, Geostatistics

Rémi Leluc

Rémi Leluc

Intern (2019)

Rémy Leluc’s internship will be on adaptive Monte Carlo methods, in the context of reinforcement learning, examined from a theoretical point of view (convergence and inequalities, theoretical bounds) and from a practical point of view (implementation of new methods and comparisons with state-of-the art methods). He is being supervised by François Portier, lecturer at Télécom Paris and Pascal Bianchi professor at Télécom Paris.

Keywords: reinforcement learning, examined from a theoretical point of view, convergence and inequalities, theoretical bounds, state-of-the art methods
Brice Parilusyan

Brice Parilusyan

Intern (2019)

Brice Parilusyan is passionate about robotics. He joined his school’s robotics association, twice taking part in the French robotics championship and built InMoov, a life-size humanoid robot. His internship will be on the study of social signals in a group. He is studying the “emerging state” in a group, such as cohesiveness and leadership and how they can be quantified by a virtual agent. He is being supervised by Giovanna Varni, lecturer at Télécom Paris.

Keywords: robotics, social signals, virtual agent
Othmane Sebbouh

Othmane Sebbouh

Intern (2019)

Othmane Sebbouh is a Data Science Master's student at ENSAE & Ecole polytechnique. The topic of his internship at the DSAIDIS chair is "Towards closing the gap between the theory and practice of stochastic variance reduced methods". SVRG is an inner-outer loop based method, where in the outer loop a reference full gradient is evaluated, after which m steps of an inner loop are executed where the reference gradient is used to build a variance reduced estimate of the current gradient. The simplicity of the SVRG method and its analysis has lead to multiple extensions and variants for even non-convex optimization. Yet there is a significant gap between what parameter settings the analysis suggests and what is known to work well in practice, which is why Othman's work will be to take several steps towards closing this gap.