Data Science & Artificial Intelligence
Dive into the heart of the digital revolution!
Artificial Intelligence and Data Science at the Service of Society and Humanity
The challenges in Data Science and Artificial Intelligence are part of CentraleSupélec's desire to put science and technology at the service of society and humanity.
Connected objects, social networks, online transactions... Nearly 90% of the data generated worldwide has been generated in the last five years. Across all sectors (health, environment, telecommunications, transportation, construction, banking and insurance, etc.), the massive influx of data raises questions and concerns, but also opens up new possibilities. Because more than ever, the future will belong to those who can extract knowledge from this data and harness it to serve society and humanity.
Understanding and improving our living environment is indeed the major challenge facing the field of data science and artificial intelligence. To achieve this, CentraleSupélec approaches this scientific field not as a new discipline, but as the combination of fundamental and mature disciplines that have long formed the core of its expertise in complex systems science: mathematics, computer science, and physical modeling.
These disciplines, which constitute the three pillars of CentraleSupélec's positioning in data science and artificial intelligence, are transversal to both its training programs (engineering, Bachelor's, Master of Science, doctoral training, etc.) and its Research Center. This transversality has enabled the School to launch a "Data & AI" initiative that mobilizes a significant portion of the teams in its research laboratories and teaching departments.
A Center of Excellence in Artificial Intelligence and Data Science
Created in 2021, the CentraleSupélec AI Hub is the single point of entry for artificial intelligence and data science, bringing together students, faculty members, entrepreneurs, and partners. It coordinates the School's initiatives and facilitates innovative projects in training, research, and entrepreneurship, while also involving businesses.
Thanks to its experience in multidisciplinary research and its links with the socio-economic world, CentraleSupélec approaches data science issues in a concrete manner, with one objective: to put its application culture at the service of societal issues, such as decision-making, the development of personalized medicine, or warning in the event of a natural disaster.
Its research areas, the innovation of its start-ups, the growing interest of students and its partnerships demonstrate its growing leadership in data science and “citizen” AI.
Our ecosystem
The AI Hub[at]CentraleSupélec
Created in 2021 with the support of the school and the CentraleSupélec Foundation, the AI Hub at CentraleSupélec pilots the school's AI policy.
The AI Hub has a dual purpose: to break down the barriers surrounding AI within the school by bringing together the various stakeholders in AI, whether students, doctoral students, or teacher-researchers, and to promote AI made in CentraleSupélec beyond its walls. To achieve this, the AI Hub is embodied at CentraleSupélec by a space where events related to Data and AI are organized. Numerous demonstrations are developed there between the school's laboratories and engineering students.
At the crossroads of teaching, research and innovation, the AI Hub is in permanent interaction with initial and continuing training, the research department and the school's partners, both academic (Graduate School engineering at the University of Paris-Saclay, DATAIA institute) and industrial (groups, SMEs, mid-cap companies and startups).
Concrete examples for data science and artificial intelligence
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The National Center for Precision Medicine in Oncology PRISM is a University Hospital Institute (IHU) funded by the ANR as part of the France2030 plan. It is based on a transformative, long-term vision of cancer management and interception, and is the result of several years of translational research conducted by the Gustave Roussy teams, in partnership with CentraleSupélec, Paris-Saclay University, Inserm and Unicancer.
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The school's laboratories are heavily involved in analyzing patient data. To better treat each patient, it's necessary to characterize their disease in great detail, as each cancer is unique. This involves collecting clinical data, molecular data of various types (the "omics"), now measured at intracellular levels, and imaging data.
Artificial intelligence will enable the integration of this heterogeneous and multi-modal data, the development of predictive and interpretable models of response to treatments, and the identification of at-risk populations.
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Led by Emilie Chouzenoux, Inria research director at the Digital Vision Center (CVN), the ERC MAJORIS aims to provide a breakthrough in Majorization-Minimization (MM) algorithms, so that they remain efficient when processing large data.
Increasingly, in biology, medicine, astronomy, chemistry, and physics, large amounts of data are being collected by ever-improving signal and image acquisition devices, which must be analyzed by sophisticated optimization tools. This project addresses optimization problems with large data sets. This involves minimizing a cost function with a complex structure and many variables.
Several challenging algorithm design issues are addressed. These include acceleration strategies, convergence analysis with complex costs, and inexact schemes. Practical, massively parallel, and distributed architecture implementations will be proposed.
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The International Laboratory on Learning Systems (ILLS) aims to develop mathematical tools to improve machine learning algorithms and make their use safer. These algorithms could be used, for example, for natural language and speech processing or for applications in computer vision and signal processing.
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Based in Montreal and launched in April 2022, it brings together CentraleSupélec, the CNRS, Paris-Saclay University, McGill University, the École de technologie supérieure (ETS) of Montreal and the Quebec Institute of Artificial Intelligence (Mila).
The laboratory focuses its activities around 5 research axes:- The fundamentals of AI to secure its use
- Online learning
- Interactions with dynamic systems
- natural language processing
- Computer vision
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This unique chair is driven by a desire to explore the possibilities of AI applied to digital simulation. Transvalor and CentraleSupélec are working to develop methods and models built from data to accelerate digital simulations and increase the accuracy of results.
The techniques used cover the disciplinary fields of numerical analysis, statistics, and databases, using machine learning methods, finite element methods, as well as so-called “physics-informed” models.
Agile and collaborative innovation is the key to supporting industries in their profound changes and meeting the challenges of the future, namely the optimization of industrial processes, energy efficiency, production integrating the circular economy and the reuse of materials.
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Laboratories involved in data science and artificial intelligence
| Laboratory or team | Director / Manager | Research areas | |
|---|---|---|---|
 | Digital Vision Center CVN | JC Pesquet | Signal, vision |
 | Institute of Electronics and Digital Technologies from Rennes (IETR) AIMAC and SIGNAL teams | R. Séguier G. Andrieux | Automatic |
 | International Laboratory on Learning Systems (IRL ILLS) | P. Piantanida | Artificial intelligence |
 | Signals and Systems Laboratory L2S | P. Bondon | Automatic, signal processing and statistics, telecom |
 | Interdisciplinary Science Laboratory Digital (LISN) | S. Rosset | IT |
 | Lorraine Computer Research Laboratory and its applications (LORIA) | Y. Toussaint | IT |
 | Mathematics and Computer Science for Complexity and Systems (MICS) | C. Hudelot | Applied mathematics IT |
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