Lylia Abrouk

Lylia Abrouk

Associate Professor HDR at Université Bourgogne Europe

Member of LIB Lab
Member of CNU section 27

I have been an Associate Professor at the University of Burgundy since 2007, working in the LIB laboratory within the IEM department. My research focuses on modeling customer behavior in the financial sector and collaborating with the SKAIZen Group on fraud detection using machine learning algorithms.

I hold a Master's and DEA in Computer Science from the University of Montpellier 2, and a PhD from the same university, where I specialized in document annotation and ontology enrichment. I previously held an ATER position at the Faculty of Law, University of Montpellier 1.

My post-PhD research includes query enrichment, community detection, and recommendation systems, with applications in video games and customer relationship management. Currently, I focus on data analysis for intelligent decision-making in the automotive sector.

Research Topics

Machine Learning
Deep Learning
Predictive Analysis
Ontology
Artificial Intelligence
Anomaly Detection Data Mining Neural Networks Natural Language Processing Big Data Pattern Recognition Automated Reasoning Statistical Modeling Reinforcement Learning

Experience

 
 
 
 
 
University of Burgundy
Délégation
MISTEA, INRAE
Sep 2023 – August 2025 Montpellier, France
 
 
 
 
 
University of Burgundy
Associate Professor
University of Burgundy
2007 – Present Dijon, France
 
 
 
 
 
University of Montpellier
ATER Position
University of Montpellier
2006 – 2007 Montpellier, France
 
 
 
 
 
Inria
Ph.D. Position
November 2003 – November 2006 Montpellier, France

Projects

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France Relance Project - KYC360

Objective: Construction of a 360° view of clients and counterparties of a financial institution to optimize fraud detection and sanction controls.

(12/2021 - 11/2023)

BQR - Support for Conferences, Congresses, and Study Days

Objective: Organization of the INFORSID 2022 congress in Dijon.

Date: 2022

Support - Participation in the Implementation of an R&D Approach

Objective: Establishment of a scientific state of the art.

(02-09/2020)

Expertise Contract - Scientific Valorization

Objective: Support in the preparation of the CIR file and R&D projects at Skaizengroup. Advice on scientific valorization.

(10/2020 - 06/2021)

BQR - Aid for the Installation of Young Researchers

Objective: Establishment of a knowledge base to model information resulting from image analyses.

Date: 2008

PEPS CNRS STRATES

Objective: Study of data economic models through an interdisciplinary approach. Use of data semantics to optimize the strategy of different actors.

ANR Content and Interaction NEUMA

Objective: Modeling, implementation, and evaluation of an open collaborative platform for the dissemination and sharing of musical content. Development of a musical ontology and creation of musicologists' communities.

Recent & Upcoming Talks

OntoFiC : une ontologie pour la détectionde fraude financière et la modélisation descomportements des clients
INFORSID

Enseignements

Dissimination

Contact