Dr. Abdelhak Bouayad | machine Learning | Young Scientist Award

PhD, UM6P, Morocco

📚 Abdelhak Bouayad is a dedicated researcher in artificial intelligence and privacy from the College of Computing at Mohammed VI Polytechnic University in Ben-Guérir, Morocco. His work explores innovative methods to protect sensitive data in machine learning models, ensuring both privacy and AI effectiveness. With a robust background in machine learning, data security, and federated learning, Abdelhak aims to drive advancements in privacy-preserving AI applications.

Publication Profile

Google Scholar

Education

🎓 Abdelhak Bouayad is currently pursuing a Ph.D. in Computer Science at Mohammed VI Polytechnic University under the guidance of Dr. Ismail Berrada. He holds an M.Sc. in Big Data Analytics and Smart Systems from Sidi Mohamed Ben Abdellah University, where he developed a thesis on lip reading for speech recognition, and a B.A. in Mathematics and Computer Science from the same institution in Fès, Morocco.

Experience

👨‍💻 Abdelhak has served as a Research Assistant at the College of Computing at Mohammed VI Polytechnic University since 2019. His research delves into the intersection of machine learning, privacy, and federated learning, with a focus on protocols to secure data exchanges and safeguard privacy within machine learning systems.

Research Focus

🔍 Abdelhak’s research is centered on artificial intelligence, machine learning, and privacy-preserving mechanisms. His primary focus lies in creating algorithms and protocols that protect sensitive data in machine learning models from potential exploitation. He aims to strengthen federated learning systems to ensure robust data privacy without compromising AI performance.

Awards and Honors

🏆 Abdelhak was awarded the College of Computing Fellowship for a pre-doctoral fellowship at Mohammed VI Polytechnic University from October 2018 to October 2019. This fellowship recognizes his commitment to research excellence and contributions to privacy-preserving AI methods.

Publication Highlights

NF-NIDS: Normalizing Flows for Network Intrusion Detection Systems

On the atout ticket learning problem for neural networks and its application in securing federated learning exchanges

Investigating Domain Adaptation for Network Intrusion Detection