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Thales Artificial Intelligence Fellow, VP ; Algo/AI for Systems & Algo/AI Engineering Segment Leader ; Présidente du Hub DSAI (Pôle Systematic)
Juliette Mattioli began her career as a high school teacher in Mathematics in 1982. Then, she joined Thomson-CSF research team, as a PhD intern on shape recognition systems. In 1993, she received a Ph.D. in Applied Mathematics for Artificial Intelligence from Paris IX Dauphine Univ. on "Inverse Problems and Differential Relationships in Mathematical Morphology", viability theory and neural networks. In 1993, she became a Research Engineer on Image Processing to design target detection algorithms without prior knowledge of the nature of the threats. In 1996, she addressed combinatorial problem solving and optimization as well as the field of the automatic parallelization. In 2001, she became the head of a research lab. dedicated problem solving focusing on combinatorial optimization, AI planning, anytime constraint programming and multi-criteria decision making. In 2005, She set-up a new research axis on soft information fusion combining semantics and knowledge-based reasoning. From 2005 to 2016, She lead few research labs at Thales Research & Technology on information fusion, combinatorial problem solving and optimization, multi-criteria decision and big data. She was appointed Strategy & Innovation Manager at Thales Technical Directorate in 2010. In 2012, she became senior expert and in 2017 she was appointed AI Senior Expert. In 2017, She was one of the 5 representatives of France at the conference of G7 innovators contributing to the question on AI, member of the #FranceIA mission. She is also co-author of a book with Michel Schmitt on mathematical morphology, published numerous scientific articles and filed 8 patents. She has also led numerous R&D projects for Thales programs and European projects (FP6, FP7, H2020). I'm In @Thalesgroup #SpeakersBureau ✒️
Université Paris Dauphine - PSL
Thèse en mathématiques appliquées à l'intelligence Artificielle (1993), Ecole Doctorale de Mathématiques de la Décision
January 1, 1989 – January 1, 1993
Université Paris Cité
Maitrise de mathématiques pures, Mathématiques
January 1, 1982 – January 1, 1988
Cours Richelieu Paris
Bac Série C
January 1, 1978 – January 1, 1981
Université Paris Dauphine - PSL
Doctor of Philosophy - PhD, Mathématiques et informatique
N/A – Present
Thales
Thales Artificial Intelligence Fellow, VP. Algo/AI for Systems & Algo/AI Engineering Segment Leader
March 1, 2025 – Present
Nowadays Agency
Présidente
August 1, 2023 – Present
Nowadays Agency
Co-fondateur
September 1, 2021 – July 1, 2023
Ramp Up ICO
Consultant senior
September 1, 2018 – July 1, 2019
Greater Paris Metropolitan Region
Thales
Senior Expert in AI & Segment leader on Hybrid AI and on AI Engineering
January 1, 2017 – March 1, 2025
OKAHINA WAVE
Associé
March 1, 2016 – June 1, 2025
Greater Bordeaux Metropolitan Area
O'Livia
Co-Fondateur & Directeur Général
December 1, 2015 – June 1, 2017
Samazan, Aquitaine, France
Thales
Directeur du laboratoire "Analyse et Raisonnement dans les Systèmes Complexes"
December 1, 2012 – December 1, 2016
Thales
Research, Technology & Innovation KTD PCC Leader "Decision Aid and Optimisation"
January 1, 2010 – January 1, 2018
Thales
Resp. du laboratoire "Mathématiques et Techniques de la Décision"
February 1, 2007 – November 1, 2012
Thales
resp. du laboratoire PLATON « Planification et Décision »
July 1, 2001 – January 1, 2007
Thales
Ingénieur de Recherches au Laboratoire « Infrastructure et Architecture Systèmes »
January 1, 1998 – January 1, 2001
Palaiseau, France
Thales
Ingénieur de Recherches au Laboratoire « Architectures et Technologies de Systèmes»
January 1, 1996 – January 1, 1998
Palaiseau, France
Thomson CSF
Ingénieur de Recherches au Laboratoire « Perception et Cognition »
January 1, 1993 – January 1, 1996
Thomson CSF
Doctorant au Laboratoire « Analyse des signaux et Reconnaissance des Formes »
April 1, 1990 – September 1, 1993
Ecole Exelmans
Professeur de Mathématiques
September 1, 1987 – June 1, 1990
Paris, France
Cours Privé Richelieu
Professeur de Mathématiques
January 1, 1982 – June 1, 1989
Paris, France
Confiance.ai
September 1, 2019 – September 1, 2024
Confiance.ai a pour mission de permettre aux industriels d’intégrer de l’IA de confiance dans leurs systèmes critiques grâce à un environnement composé de méthodes et d’outils intégrables dans tout atelier d’ingénierie. Pour ce faire, Confiance.ai lève les verrous associés à l’industrialisation de l’IA comme la construction de composants IA à confiance maitrisée, la construction de données et/ou de connaissances pour augmenter la confiance dans l’apprentissage ou encore l’interaction générant de la confiance entre l’utilisateur et le système fondé sur l’IA.
OSEMINTI
July 1, 2007 – July 1, 2009
OSEMINITI is an EDA (European Defence Agency) project which aims to demonstrate and assess the benefits of a semantic network approach for intelligence information systems enhanced by of a new generation Semantic Machine (OSEMINTI Platform). The Semantic Machine is an unified representation and processing device that - Allows importation of external pieces of information - Integrates text and audio mining tools - Integrates decision support through examples - Integrates embedded geographical semantics
SERKET
December 1, 2005 – November 1, 2007
SERKET is an ITEA project which aims to develop an open platform as a global approach to security provision of places, locations and events SERKET prototyped technologies for preventive security of public places and large events such as mass transport, sporting and cultural events, and demonstrations. Through the analysis of information supplied by sensors, SERKET automatically detects risk-prone situations. In future, security supervising operators can focus on their mission since the system reports alarms in case of latent threats. The interest of this approach consists in improving the operators’ knowledge of potentially complex field situations by avoiding the tedious and unfeasible analysis of all images supplied to the control room.
SMMART
November 1, 2005 – October 1, 2008
The SMMART ran for 3 years with an overall budget of around 25 millions €, cofunded by the European Commission. Coordinated by Turbomeca, the project involves 24 companies and institutions from across Europe. Role: Technical Programme Director, Member of the Steering Committee and Work Package Leader for Organisation, Integration & Security. SMMART OVERALL OBJECTIVES The SMMART project aimed at defining a new integrated concept to answer the maintenance challenges of the transport industry – aeronautics, road transport, marine transport: - To reduce the time and cost for scheduled and unscheduled maintenance inspections of increasingly sophisticated and complex products. - To remotely provide the adequate up-to-date information to assist the mobile workers in all their tasks wherever they operate. - To minimise the cost penalties of unscheduled downtime on large transport fleets. SMMART PROGRAMME KEY CHALLENGES - To monitor in real-time the usage and maintenance data throughout the lifecycle of critical sub-assemblies of a vehicle. - To optimise maintenance management through a worldwide network. - To provide new services: advanced troubleshooting tool, global configuration control, resource planning tool. - To remotely exchange information between all life-cycle stakeholders in a timely, secure and trusted environment. - To provide end-to-end visibility of the logistic supply chain. - To improve industrial and logistic traceability. - To optimise maintenance and logistic planning. - To further improve transportation safety. SMMART TECHNICAL APPROACH The technical approach of SMMART is based on the combination of: - Smart items capable of operating and communicating wirelessly in the harsh environment of a vehicle’s propulsion unit. - Re-engineered business processes addressing technological, organisational and social aspects to support the SMMART concept implementation within the end-user community.
CITRINE
January 1, 2005 – January 1, 2006
CITRINE (Common Intelligence and Traceability for Rescues and IdentificatioN opErations) aims to develop a first version of an integrated set of shared information management tools and models to facilitate the efficient integration of diverse emergency and management services for humanitarian operations and rescue tasks in support of the external policies of the EU with an emphasis on security aspects. CITRINE will support the crisis management process in mitigation, damage assessment and preliminary recovery phase, focusing on humanitarian activities provided by NGOs and Health Services.
MARIUS
January 1, 2004 – January 1, 2005
The main benefit of the proposed MARIUS project lays in the development of a pre-operational autonomous command post, equipped with its own sensors, information and communication systems, which can be deployed quickly to monitor crisis management operation. Its importance lies in the implementation of a demonstrator which could easily be deployed for inter-agency cooperation, situation assessment and decision making. The Demonstrator will be deployable by helicopter and will incorporate open scalable IT infrastructure, generic gateways, decision support and crisis communication support. MARIUS focuses on improving Crisis Management efficiency: deployment rapidity, inter-agency co-operation, situation assessment and decision-making. It addresses other priority missions through the operational scenarios and (pre-)normative aspects.
LASCOT
April 1, 2003 – June 1, 2005
LArge Scale COllaborative decision support Technology Developing and demonstrating a set of concepts and technologies for collaborative decision support in a network of large organisations. More and more enterprises and administrations have to collaborate in order to make appropriate business decisions or avert crises. The collapse of the ‘new economy’ has led to a renewed interest in software that can help companies to recover their business revenue streams and rapidly seek new opportunities. The European economy can benefit from decision-support facilities based on the LASCOT technologies. THALES developed advanced software tools for collaborative decision-making and for the adaptation of various supplied information.
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edX
June 24, 2026 – Present
L'avenir de la décision : connaître et agir en complexité
Coursera Course Certificates
June 24, 2026 – Present
Intégrité scientifique dans les métiers de la recherche
France Université Numérique : le groupement d'intérêt public
June 24, 2026 – Present
MOOC FUN: Innover et entreprendre dans un monde numérique
France Université Numérique : le groupement d'intérêt public
June 24, 2026 – Present
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June 24, 2026 – Present
edX Certificat Vérifié pour Les fondements de la stratégie d’entreprise
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June 24, 2026 – Present
Cultural Fit Analysis
The candidate's extensive experience in large-scale, collaborative research and development projects (e.g., SMMART, SERKET, MARIUS, OSEMINTI, CITRINE, LASCOT, Confiance.ai) indicates a strong cultural fit for environments that value innovation, inter-agency cooperation, and complex problem-solving. Their long tenure at Thales, a major defense and aerospace company, suggests an ability to thrive in structured, high-stakes environments. The entrepreneurial ventures (Nowadays Agency, O'Livia, OKAHINA WAVE) also show initiative and a broader business perspective, which can be valuable in understanding data's impact on business outcomes. However, the target role of 'Data Analyst' might be a step down from their senior leadership and strategic roles, potentially leading to a mismatch in expectations regarding day-to-day responsibilities.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, strategic planning, and project coordination skills through their various roles at Thales and involvement in large European projects. Their experience in managing research laboratories and leading key technology domains indicates an ability to drive innovation and manage complex technical teams. The co-founding roles in communication agencies suggest entrepreneurial spirit and adaptability, though these are less directly relevant to a pure Data Analyst operational fit.