About me
I am currently a Claire DesChênes Postdoctoral Fellow at Mila- Quebec AI Institute, working on the intersection of fairness and privacy in machine learning with Dr. Golnoosh Farnadi. I have obtained a PhD in Electrical engineering in June 2022 from Université de Sherbrooke, where I worked under the supervision of Pr. Soumaya Cherkaoui on problems related to distributed machine learning algorithms in wireless networks. Before that, I have obtained a DESS from Université de Sherbrooke (2018), and a software engineering degree from ENSIAS (2018).
During my PhD, I have received the Leonard De Vinci medal from the engineering faculty at Université de Sherbrooke, and the best paper award at IEEE LCN 2021. In summer 2019, I was a MITACS Accelerate intern with the city of Sherbrooke. Later, I was working with Trilliant, Inc. on a mesh network data analysis for anomaly detection and prediction. I also helped supervising several students and interns in our lab.
I like the outdoors, museums, and web comics.
Research interests
- Federated Learning
- Reinforcement learning
- Robustness in Machine learning
- Wireless Communication
- Edge computing
Publications
- N. Neophytou, A. Taïk, G.Farnadi, "Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements", Proceedings of the AAAI Conference on Artificial Intelligence ,EAAMO '23: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization 2023.
- M Molamohammadi, A. Taïk, N.LeRoux, G.Farnadi, "Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread", Proceedings of the AAAI Conference on Artificial Intelligence , 2024.
- A. Taïk and S. Cherkaoui, "Electrical Load Forecasting Using Edge Computing and Federated Learning", IEEE International Conference on Communications (ICC), 2020.
- A. Taïk, H. Moudoud and S. Cherkaoui, "Data-Quality Based Scheduling for Federated Edge Learning", IEEE 46th Conference on Local Computer Networks (LCN), 2021, Best Paper Award
- A. Taïk, Z. Mlika and S. Cherkaoui, "Data-Aware Device Scheduling for Federated Edge Learning," in IEEE Transactions on Cognitive Communications and Networking.
- A. Taïk, Z. Mlika and S. Cherkaoui, "Clustered Vehicular Federated Learning: Process and Optimization," in IEEE Transactions on Intelligent Transportation Systems.
- A. Taïk, and S. Cherkaoui, "Federated Edge Learning: Design Issues and Challenges," in IEEE Network.
- A. Taïk, , B. Nour and S. Cherkaoui, "Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning," in IEEE Wireless Communications.
Additional activities
- Teaching assistant for Introduction to Machine learning (HEC, Winter 2023) and Responsible AI (McGill, Winter 2024)
- Reviewer for multiple IEEE, Elsevier and Springer journals
Contact
Email : afaf.taik@usherbrooke.ca
