Khadija Bousselmi Ep ARFAOUI

Senior Lecturer, Université Savoie Mont Blanc, IUT d'Annecy

Contact:

E-mail: khadija.arfaoui -@- univ-smb.fr

Telephone: +33(0) 4 50 09 65 59

Office: A 222

Address: LISTIC - Polytech Annecy-Chambéry, BP 80439 - Annecy le Vieux - 74944 ANNECY Cedex, France

Research topics:

My research interests include parallel computing, distributed computing and computer communications. I have mainly worked on optimizing the performance of data-intensive systems, such as the scheduling of scientific workflows in the Cloud and the design and processing of Big Data warehouses. I'm currently working on optimizing the architecture of convolutional neural networks (CNNs) to reduce energy consumption and improve performance.

Framing :

Theses :

2021-2024: Asma Dhaouadi (defended on 11/12/2024)
Theme: Modeling Data Warehousing in the context of Big Data

Subject: CONTRIBUTING TO MASSIVE DATA STORAGE: GENERIC ARCHITECTURE, METHODOLOGY AND IMPLEMENTATION
Summary:

In the era of Big Data, the rapid evolution of technologies makes the choice of tools adapted to experts' needs a complex one. Existing studies on Big Data storage and analysis are often limited and specific. To overcome these limitations, this thesis proposes a generic modeling approach, inspired by model-driven architecture, to guide technology choice at every stage. An interactive framework helps to model a customized pipeline and verify tool interoperability, with connector recommendations. This framework has been validated through case studies on COVID-19 and the 2022 World Cup. User feedback has been satisfactory (85% satisfaction rate).

Internships:

2023: William Paccoud, M2 internship, performance analysis of Big Data warehousing tools

2022: Moenes Ben Soussia, M2 internship, Dynamic load balancing of Cloud servers based on machine learning techniques

2021: EZ-ZAROUALY AZIZA M1 internship, Study and extension of orchestration mechanisms for scientific data stream
for the MUST platform

Key words:

Cloud Computing, Load Balancing, Reinforcement Learning, MUST, Scheduling, Scientific Workflows, Distributed Environments, Data Warehousing, Big Data, Decision Support, Machine Learning, Interoperability, Green Computing, CNN, Neural Networks.

Publications:

Here is the link to my publications on HAL: here

Here is the link to my updated publications from Google Scholar: here

Reading committees:

International magazines:

  • IEEE Transactions on Industrial Informatics
  • Intelligent Decision Technologies
  • International Journal of Computing
  • IEEE Transactions on Neural Networks and Learning Systems
  • Journal of King Saud University - Computer and Information Sciences
  • Jordanian Journal of Computers and Information Technology

International conferences:

  • International Conference on Software Engineering Advances (ICSEA 2024, 2022)
  • International Conference on Control, Decisionand Information Technologies(CoDIT 2024, 2023)

  • International Conference on the Sciences of Electronics, Technologies of Information and Telecommunications
    (SETIT'18)
  • international conference on Sciences and Techniques of Automatic control and computer engineering
    (STA'19)

Teaching:

Module information Description
Name: Optimization methods for decision support

Hourly volume: 15H

Level: BUT 3 - all courses

This module aims to introduce students to the fundamentals of optimization methods used in decision-making and resource management. Courses will cover techniques such as linear programming, nonlinear programming, as well as heuristic and meta-heuristic approaches adapted to complex problems. Applications include logistics, project planning and business scenario analysis.
Name: Optimization methods: introduction to machine learning

Hourly volume: 15H

Level: BUT 2

 

This module provides an introduction to the fundamental concepts and tools of machine learning for second-year BUT students. The main objectives are:

- Understand the basics of machine learning, including classification, regression and clustering.

- Discover the links between optimization and machine learning, such as optimizing cost functions and adjusting hyperparameters.

Communication and low-level operation

Hourly volume: 50H

Level BUT 1

This module introduces students to the basics of machine-to-machine communication. It covers :

  • Fundamental principles of digital communication (binary coding, synchronization, modulation).
  • Low-level transmission protocols (RS232, UART, SPI, I2C).
  • Understanding the physical and link layers of the OSI model.
Name: Network Architecture

Hourly volume: 50 H

Level: BUT 2, semester 3

This module takes an in-depth look at the structuring and organization of computer networks. It includes :

  • IP network basics: addressing, subnetting and routing.
  • Discover the network and transport layers (TCP/IP, UDP).
  • Implementation of communication protocols (DHCP, DNS, HTTP).
Name: Réseaux Avancés

Hours: 18H

Level: BUT 2, semester 4

This module explores advanced technologies and issues in modern networks:

  • Wireless networks (Wi-Fi, Bluetooth, ZigBee) and IoT networks.
  • Management of complex networks, including virtualization (VLAN, VPN) and cloud networking.
Name: Graph theory

Hourly volume: 22H

Level: BUT 1

  • Model complex problems using graphs (oriented/unoriented, weighted graphs).
  • Study the fundamental properties of graphs: connectivity, traversal (DFS/BFS), minimal spanning tree.
  • Applications: networks, planning and optimization.
Name: Automata and languages

Hourly volume: 22H

Level: BUT2, semester 4

  • Understand deterministic finite automata (DFA) and non-deterministic finite automata (NFA).
  • Model computer processes with automata, and link them to regular expressions.
  • Applications in compiler design and pattern recognition.
  • Introduction to formal grammars (type 0 to type 3 according to Chomsky).
  • Study the relationships between languages, automata and Turing machines.
  • Applications to programming languages, syntax analysis and interpreter development.

Career path:

Since 2020: Senior lecturer at Annecy IUT

  • University: Université Savoie Mont Blanc
  • Laboratory: LISTIC, École Polytechnique d'Annecy
  • Research team: Representation, Management and Processing of Data for Humans (ReGaRD)

2019 - 2020 : Post-doctoral internship at LAMSADE

  • Topic: Multi-objective optimization of scheduling intensive scientific workflows in a Cloud Computing environment, guided by data localization.
  • Location: LAMSADE, Université Paris Dauphine, Paris, France

2018 - 2020 : Temporary teaching and research associate (ATER), University of Paris Nanterre, UFR SEGMI.

2013 - 2017 : PhD thesis, RIADI-GDL Laboratory, La Mannouba, Tunisia

  • Subject: Scalable approach to scheduling scientific workflows in a Cloud Computing environment, guided by quality of service (QoS) and energy consumption.
  • Venue: Tunis Faculty of Science, University of Tunis El Manar
  • Research laboratory: RIADI-GDL, Université de la Manouba, Tunisia
  • Supported in 2017

2010 - 2013: C/C++ design and development engineer

  • Company: EUROGICIEL Ingénierie, Tunisia
  • Mission: Design and development of embedded applications in C/C++.
  • Customers: SAGEM Défence, THALES, EURODOC

2009 - 2010: C/C++ design and development engineer

  • Company: SAGEM COMMUNICATION
  • Mission: Development of embedded applications in C/C++.
  • Customers: SAGEM COMMUNICATION, Bouygues Télécom

2011: Research Master's degree in computer science, software engineering and decision support

    • Research laboratory: RIADI-GDL, Université de la Manouba, Tunisia.

2008: Diploma in computer engineering

    • Institution: National School of Computer Science (ENSI), Tunisia.

2003: Baccalauréat, Mathematics option