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The MScAI project

An opportunity to consolidate and apply your technical knowledge, adapting it to the various challenges of tomorrow for an increasingly demanding society.

In numbers

The MSc AI team at CentraleSupélec is there to supervise and monitor students and ensure we meet your expectations. Delivering a program of excellence with enthusiasm, the team will support you as you develop AI skills that are adapted to the realities of society without ever losing sight of core concerns.

5
Tracks
53
Teaching professors
75
faculty members
and 150 doctoral students engaged in this scientific field at CentraleSupélec
4
Research and teaching chairs
in Data Science and Artificial Intelligence at CentraleSupélec

Supervising team

The MSc AI team at CentraleSupélec is there to supervise and monitor students and ensure we meet your expectations. Delivering a program of excellence with enthusiasm, the team will support you as you develop AI skills that are adapted to the realities of society without ever losing sight of core concerns.

Céline Hudelot

Academic Co-Director
Renewable energy plays a huge part in the building of a better future, and Artificial Intelligence is a fundamental building block of the clean energy transition: contributing to the improvement and acceleration of the energy industry; improving our ability to understand and forecast energy demand; managing network infrastructures; sustainably managing natural resources in a way that is balanced.

Myriam Tami

Academic Co-Responsible
Myriam TAMI is an associate professor at Université Paris-Saclay, CentraleSupélec, MICS lab. Her research focuses on AI, machine learning, representation learning, causality and models in the context of complex or heterogeneous data.

Dave Jacob

Academic Co-Director
Dave Jacob brings his expertise in communication and development to the program. He has extensive international experience which is an asset when it comes to managing projects. He is a perfect fit with the objectives of the MSc AI, given the high stakes and the importance of ethics.

Azaliya Batalova

Program Assistant/Student Support
Céline Hudelot is Professor in Computer Science at CentraleSupélec. She heads the MICS laboratory (Mathematics and IT for Complex Systems). She is interested in both data-driven and knowledge-driven Artificial Intelligence for the semantic interpretation of non-structured data, with a particular interest in explainable AI (XAI).

Teaching team

Vincent MOUSSEAU photo
Vincent MOUSSEAU
Full Professor
Decision Modeling
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Gianluca QUERCINI photo
Gianluca QUERCINI
Associate Professor
Big data for AI
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Charlotte LACLAU
Associate Professor
Ethical and organisational aspects
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Yiping FANG photo
Yiping FANG
Associate Professor
Stochastic optimization
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Stergios CHRISTODOULIDIS photo
Stergios CHRISTODOULIDIS
Associate Professor
Deep learning
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Thomas CORDIER photo
Thomas CORDIER
Teaching assistant
Deep learning
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Zhiguo ZHENG photo
Zhiguo ZHENG
Associate Professor
Predictive maintenance
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Arthur LEDAGUENEL photo
Arthur LEDAGUENEL
Teaching assistant
Deep learning
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Wassila OUERDANE photo
Wassila OUERDANE
Associate professor
Multi agent systems
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Fragkiskos MALLIAROS photo
Fragkiskos MALLIAROS
Associate professor
Network science analytics
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Myriam TAMI photo
Myriam TAMI
Associate professor
Ensemble learning
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Yannick LE CACHEUX
External collaborator
Machine learning
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Hugues TALBOT
Full professor
Optimization for machine learning
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Celine Hudelot, co-director
Céline HUDELOT
Full professor
Explainability of AI
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Jean-Philippe POLI
External collaborator
Foundations of AI
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Frédéric PASCAL
Full professor
Advanced statistics
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Damien CHALLET
Full professor
AI for finance
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Laurence CARASSUS
Full professor
AI for finance
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Ioane MUNI TOKE
Full professor
AI for finance
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Hedi Hadiji
Associate professor
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Perspectives

Artificial intelligence has become increasingly prevalent in our lives and is a definite game changer for society. This program aims to provide students with the foundations and most advanced techniques in the field, enabling them to become technical leaders of this transformation. Our program offers a unique curriculum, tackling the field with model / symbolic-driven and data-driven artificial intelligence methods, assessing their applications to key societal domains such as sustainable development, new mobilities, networks, Industry 4.0, finance and health / wellbeing.

This unique program, offering an end-to-end approach from theory to practice, is delivered entirely in English by outstanding academics and professionals, and offers an excellent curriculum to those preparing for a future as Artificial Intelligence architects seeking exceptional career perspectives in the hottest discipline of the 21st century.

As a French person, I was well aware of the reputation of CentraleSupélec. It is one of the best schools in France. Furthermore, the description of the course interested me a lot: Most schools that have AI programs are focused on Machine and Deep Learning but AI is not only about that. There are plenty of issues that exist in AI that Deep Learning would not solve. I would describe the MSc AI of CentraleSupélec as:

  • Demanding: the program is short and intense
  • Gobal: we study a lot of different fields in depth
  • Balanced: we did both research opportunities and application.
Louis de Vitry
Co-founder of Kanop
Vincent MOUSSEAU photo

Vincent MOUSSEAU

DECISION MODELING

Vincent Mousseau is Full Professor at CentraleSupélec and Researcher at the Mathematics and IT Laboratory for Complex Systems (MICS). He studies preference modelling and decision systems.

He is academic co-director of the MSc AI, and also heads the Complex Systems Engineering cluster at the Paris-Saclay University INTERFACES doctoral school.

He was awarded his PhD in Computer Science/Operational Research by Paris-Dauphine University in 1993.

He was Assistant Professor at Paris-Dauphine from 1994 to 2008, and has been Full professor at CentraleSupélec since 2008.

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Gianluca QUERCINI photo

Gianluca QUERCINI

BIG DATA FOR AI

Gianluca Quercini has been an associate professor at the Computer Science Department of CentraleSupélec since 2012. He is also a member of LISN (Interdisciplinary Laboratory of Digital Sciences).

He was awarded a Ph.D. in computer science from the University of Genoa (Italy) in 2009 with a thesis titled “Optimizing and Visualizing Planar Graphs via Rectangular Dualization”.

Current research interests: information discovery, information extraction, semantic web.

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Charlotte LACLAU

ETHICAL AND ORGANISATIONAL ASPECTS

Since September 2022, Charlotte has been an Associate Professor at Télécom Paris, Institut Polytechnique de Paris. She is a member of the LTCI lab and part of the S2A team. Prior to this position, she held the same position at the Hubert Curien Laboratory in the Data Intelligence team.

Complementary information can be found in Charlotte’s CV (last update August 2025) or on her LinkedIn profile.

Charlotte’s main line of research is in machine learning, and her current research focus includes:

  • representation learning for complex data
  • fairness in machine learning for relational data
  • unsupervised learning for high-dimensional data

Publications

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Yiping FANG photo

Yiping FANG

OPTIMIZATION STOCHASTIC

Yiping Fang (Ph.D. 2015) is a lecturer at CentraleSupélec.

His research mainly covers stochastic models, stochastic and robust optimization, and reinforcement learning, with applications to risk, resilience, and maintenance of cyber-physical systems (notably, smart grids, electrified and intelligent transport).

Publications

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Stergios CHRISTODOULIDIS photo

Stergios CHRISTODOULIDIS

DEEP LEARNING

Stergios Christodoulidis is assistant professor at the mathematics department of CentraleSupélec and a permanent member of the MICS Laboratory βiomathematics team.

He has a diploma in Electrical and Computer Engineering (AUTH, GR) and a PhD in Biomedical Engineering (Unibe, CH).

His research interests lie at the cross-section between mathematical modeling and precision medicine for improving diagnosis, prognosis and treatment decisions.

Publications

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Thomas CORDIER photo

Thomas CORDIER

DEEP LEARNING

Thomas Cordier has an engineering degree from ENSIL-ENSCI and is a PhD student at CentraleSupélec’s MICS Lab.

He researches the control of neural networks by disentangling their representations. The internal representations of a network should be aligned with the factors of variations of the input data.

At the crossroads of predictive and generative models, he tries to incorporate inductive bias in the model to encode semantic contents to enforce the interpretability and performance in deep learning.

Publications

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Zhiguo ZHENG photo

Zhiguo ZHENG

PREDICTIVE MAINTENANCE

Zhiguo ZENG received his PhD in reliability engineering from Beihang university in 2016.

He is currently assistant professor (with HDR / authorization to direct research) at CentraleSupélec, Université Paris-Saclay, France.

His research focuses on the characterization and modeling of the failure/repair/maintenance behavior of components, complex systems and their reliability, maintainability, prognostics, safety, vulnerability and security.

Publications

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Arthur LEDAGUENEL photo

Arthur LEDAGUENEL

DEEP LEARNING

Arthur joined CentraleSupélec in 2017 and quickly became interested in Applied Mathematics and Machine Learning. In his final year he specialized in Artificial Intelligence.

As a student, he was involved in the association “Oser” which offers tutoring to high school students. After a research internship with CNAM (Conservatoire National des Arts et Métiers), he started his PhD focusing on neuro-symbolic AI at IRT SystemX and MICS in December 2021.

Publications

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Wassila OUERDANE photo

Wassila OUERDANE

MULTI AGENT SYSTEMS

Wassila is an Assistant Professor in Computer Science at CentraleSupélec and a member of the MICS Lab.

She holds a PhD in Computer Science from the University of Paris Dauphine in 2009.

She is interested in questions related to Knowledge Representation and Reasoning within the context of Explainable AI, with the aim of using formal tools from AI and decision theory to specify agent reasoning (resolving conflicts, explaining decisions, etc.) and facilitate agent communication (dialogue protocol).

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Fragkiskos MALLIAROS photo

Fragkiskos MALLIAROS

NETWORK SCIENCE ANALYTICS

Fragkiskos Malliaros is an Assistant Professor at CentraleSupélec and an associate researcher at Inria Saclay (OPIS team).

He was a postdoctoral researcher at UC San Diego (2016-17) and École Polytechnique (2015-16). He received his PhD in Computer Science from École Polytechnique (2015).

His research interests span the broad field of data science, focusing on graph machine learning and graph-based information extraction.

Publications

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Myriam TAMI photo

Myriam TAMI

ENSEMBLE LEARNING

Myriam TAMI (PhD 2016, University of Montpellier, Institut Montpelliérain Alexander Grothendieck, France) is an Associate Professor at University Paris-Saclay, CentraleSupélec, MICS lab.

Her research covers AI, Machine Learning, representation learning, causality, and models in the context of complex or heterogeneous data, e.g., multimodal, structured, and unstructured with sometimes latent variables, with uncertainty or weakly labeled.

Her publications and research profile can be consulted on her web page or Google Scholar via the following links.

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Yannick LE CACHEUX

MACHINE LEARNING

Yannick Le Cacheux (PhD 2020, Hautes Etudes Sorbonne Arts et Métiers) is a lecturer at CentraleSupélec and the founder of an AI consulting company, Manifold Technology.

He has conducted numerous AI projects for industry leaders such as Axa, L’Oréal, Saint Gobain, Pernod-Ricard, Orano, GLS…

His research works include multi-modal models at the intersection of computer vision and natural language processing.

Publications

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Hugues TALBOT

OPTIMIZATION FOR MACHINE LEARNING

Hugues Talbot is distinguished professor in the mathematics department at CentraleSupélec and a member of the Inria OPIS team. He received his HDR (authorization to direct research) from Université Paris-Est in 2013, his PhD from Ecole des Mines in 1993 and his Engineering Degree from Ecole Centrale de Paris in 1989.

He is the co-author or co-editor of 8 books and over 300 articles in the field of mathematical morphology, discrete geometry, combinatorial and continuous optimization.

Publications

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Céline HUDELOT

EXPLAINABILITY OF AI

Celine Hudelot is a Full Professor at CentraleSupélec in the MICS laboratory which she directs. Her research work is at the frontier of knowledge engineering, image processing, and representation learning.

Her current work focuses on the interpretability and explainability of artificial intelligence algorithms and associated learning paradigms such as domain adaptation, few-shot, active and incremental learning.

Publications

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Jean-Philippe POLI

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

Jean-Philippe is a senior expert in Artificial Intelligence at CEA-LIST. He received his PhD from Aix-Marseille Université and the Institut National de l’Audiovisuel in 2007, on the subject of automatic structuring of television streams. He joined CEA-LIST in 2007 as postdoctoral then permanent researcher. His research concerns symbolic Artificial Intelligence, in particular symbolic machine learning and eXplainable Artificial Intelligence (XAI).

He has been teaching at CentraleSupélec since 2004.

Publications

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Frédéric PASCAL

ADVANCED STATISTICS

Frédéric Pascal (MSc 2003, PhD 2006, HDR 2012) is Full Professor in AI at L2S lab, CentraleSupélec, University Paris-Saclay.  Between March 2008 and December 2011 (resp. Jan. 2012 – Dec. 2013), he was an Assistant Professor (resp. Associate Professor) in SONDRA (joint French-Singaporean lab.) at CentraleSupélec.  From August 2013 to August 2014, he was Visiting Associate Professor in the ECE department at the National University of Singapore.

Between January 2017 and December 2018, he was head of the “Signals and Statistics” group of L2S. Since December 2019, he has been director of the AI@CS Hub, coordinating the activities in artificial intelligence at CentraleSupélec, while also being the Givaudan chair on data sciences. Since September 2017, he has been part of the executive committee of the DATAIA institute as the Program Coordinator, and he was appointed as Co-Director of the institute in April 2021.

From 2015 to 2017, Frédéric Pascal was the Chair of the EURASIP SAT in Theoretical and Methodological Trends in Signal Processing (TMTSP). He is a member of the IEEE Signal Processing Society SAM technical committee (January 2015-present). Between 2019 and 2021, he was the Vice Chair of the Data Science Initiative of the IEEE Signal Processing Society.

Frédéric Pascal served as Associate Editor for the IEEE Transactions on Signal Processing (2015-2018), the EURASIP Journal on Advances in Signal Processing (2015-2021), and Elsevier Signal Processing (2018-2021). His research interests include estimation, detection, and classification for statistical signal processing and radar and image processing applications. He is the author/co-author of more than 200 papers in the top journals and conferences in Signal Processing, Image Processing, and Statistics.

Publications

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Damien CHALLET

AI for finance

Damien Challet obtained his PhD in Theoretical Physics from Université de Fribourg, Switzerland in 2000. He joined the MICS lab at CentraleSupélec in 2011 where he is full professor.

 

His research interests include agent-based modelling, statistical learning, cognitive biases, market microstructure, large-scale human-made systems

Publications

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Laurence CARASSUS

AI for finance

Graduated from Paris-Dauphine University with a Master’s degree in Applied Mathematics and a Master’s Degree in Audit, Laurence also holds a PhD in Applied Mathematics from Paris 1 Panthéon-Sorbonne University, prepared at the CREST, and an HDR (Habilitation à Diriger les Recherches) in Applied Mathematics prepared in Paris 7 University. Laurence was an associate professor at Paris 7 University, where she took part in the Master 2 Modélisation Aléatoire (Laure Elie), and she created the Master Ingénierie Statistique et Informatique, de la Finance, de l’Assurance et du Risque (ISIFAR). She then joined Deloitte Risk Services as a Manager and was involved in audit and advisory missions in the areas of banking and energy. Back in Paris 7 University, she obtained her HDR and was promoted to professor at the University of Reims, where she managed the Master Statistique pour l’Evaluation et la Prospective (SEP). During 4 years, she was Dean of Research at the Ecole Supérieure d’Ingénieurs Léonard de Vinci (ESILV) and co-head of the multidisciplinary lab De Vinci Research Center (DVRC). From the research point of view, Laurence works in probability applied to finance and mathematical economics, mainly on incomplete markets. She regularly publishes for leading journals such as Mathematics of Operations Research, Annals of Applied Probability or Mathematical Finance. Laurence participates in numerous conferences, is invited to foreign universities and has expertise activities (thesis or HDR juries, selection or HCERES committees, referee for international reviews, etc.).

Publications

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Ioane MUNI TOKE

AI for finance

Ioane Muni Toke is an Associate Professor at CentraleSupelec, Université Paris-Saclay. His prior positions include Research Associate at the University of Texas at Dallas and Associate Professor at the University of New Caledonia.

 

His research deals with probability and statistics applied to quantitative finance, with a focus on financial market microstructure and limit order books.

Publications

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Guillaume LAME photo

Guillaume LAME

ETHICAL AND ORGANISATIONAL ASPECTS

Guillaume Lamé received his PhD from Paris Saclay University in 2017 before working for two years as a postdoc in the Department of Public Health and Primary Care at the University of Cambridge.

He became assistant professor at CentraleSupélec in 2019.

He teaches operations management and industrial engineering, and does research on the organization of healthcare delivery, e.g. analyzing electronic health records to evaluate the quality of cancer care.

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