CV
You can find the complete CV as a PDF file.
Education
- Ph.D. Robust & Secure Deep Learning SnT - University of Luxembourg (2019-2023)
Selected papers:
Ghamizi, Salah & Zhang, Jingfeng & Cordy, Maxime & Papadakis, Mike & Sugiyama, Masashi & Le Traon, Yves. (2023). Gat: guided adversarial training with pareto-optimal auxiliary tasks; ICML23.
Ghamizi, Salah & Cordy, Maxime & Papadakis, Mike & Le Traon, Yves. (2021). Evasion Attack STeganography: Turning Vulnerability Of Machine Learning To Adversarial Attacks Into A Real-world Application; ICCV21.
Ghamizi, Salah & Renaud Rwemalika & al. (2020). Data-driven Simulation and Optimization for Covid-19 Exit Strategies. KDD 2020; Best Paper Award; DOI 10.1145/3394486.3412863.
Ghamizi, Salah & Cordy, Maxime & Papadakis, Mike & Le Traon, Yves. (2020). Search-based adversarial testing and improvement of constrained credit scoring systems. ESEC/FSE 2020; DOI 10.1145/3368089.3409739.
Ghamizi, Salah & Cordy, Maxime & Papadakis, Mike & Le Traon, Yves. (2020). FeatureNET: Diversity-Driven Generation of Deep Learning Models. ICSE Companion 2020; DOI 10.1145/3377812.3382153.
Ghamizi, Salah & Cordy, Maxime & Papadakis, Mike & Le Traon, Yves. (2019). Automated search for configurations of convolutional neural network architectures. SPLC 2019; DOI 10.1145/3336294.3336306.
- Bachelor then Master in Artificial Intelligence and Robotics MinesNancy, School of Computer Science (2012-2016)
Work Experience
- R&T Association @ LIST, 2023-
Member of the LEAP project: Learning Enabled Autonomous Real-time Operation For Distribution Grids (LEAP)
Abstract: As a result, the taxonomy of distribution grid is rapidly changing at a global scale. It is envisioned that future distribution grids will become highly granular with control and operation procedures that will be profoundly different than those of today. It is envisioned that the time windows to solve operational problems will be narrowed in future distribution grids. In order to maintain the safety and stability of DERs-driven increasingly variable operation of power systems, operational planning decisions of agents will no longer be sufficient merely on the basis of a day or a few hours in advance: most decisions must be made in situ and moving to real-time (RT) according to the instantaneous operating conditions of the distributed grids. Operational planning will evolve towards optimal control of complex systems in RT, which cannot be addressed by existing approaches.
In order to address these questions, LEAP proposes ground-breaking solutions based on computational intelligence techniques, specifically based on cutting-edge technologies such as artificial intelligence (AI) and machine learning (ML), which are emerging as major enablers to lead the energy transition. Such an intelligent digital framework will be integrated into a wider digital initiative, i.e., into a Nationwide Digital Twin (NWDT) project.
- R&D Engineer @ LumApps SAS, 2018-2019
In charge of the improvements and deployment process of the platform of some of the company’s major clients (Decathlon, Veolia,…) and implemented third party API integration Set up the open-source SDK of the company, and supervised the continuous delivery & testing architecture of the SDK.
- Co-founder and CTO @ WAZA Education, 2016-2018
Fullstack lead engineer of a team of 3 people
Awards & Volunteering
COVID-19 Task Force (2020) - 120k€ Grant from Luxembourg National Research Fund and AUF for 1-year research on Covid19 simulation and forecasting.
i-LAB, Pépite (2017) - Awarded 6k€ from the French Ministry of Research & Innovation for my NLP work on WAZA (400 candidates).
EcoRevolutions (2016) - Awarded 12k€ from the “GrandEst” State for Edstore/WAZA project.
Imagine Cup (2016) – Finalist of the competition held by Microsoft France for project Edstore.
Awards & Volunteering
Junior Chamber International (project leader)
TEDx MinesNancy (Founder).