Sofiane ENNADIR

About Me

I am a PhD student in the EECS School at the KTH Royal Institute of Technology in Sweden, working under the supervision of Prof. Michalis Vazirgiannis and Prof. Henrik Boström. I am also fortunate to be part of the WASP AI program and my research is supported by the Wallenberg AI, Autonomous Systems and Software Program funded by the Knut and Alice Wallenberg Foundation.

My research revolves around Graph Neural Networks (GNNs), delving deeply into their robustness and practical applications. Specifically, my PhD focuses on enhancing the robustness of these models against adversarial attacks while analyzing and developing various attack strategies. I am particularly interested in theoretically understanding the robustness behaviour of GNNs and consequently proposing efficient and viable defense methods. Beyond theoretical exploration, I am also passionate about leveraging GNNs within the biomedical domain to unlock innovative applications.

I'm eager to connect with fellow academics in my field! Whether our work aligns closely or intersects in intriguing ways, I'd be delighted to engage in a conversation—feel free to reach out via email or LinkedIn :) Additionally, I'm actively seeking internship opportunities for the summer of 2024.

Prior to joining KTH, I completed a MSc Degree in "Applied Mathematics - Data Sciences" from l’Ecole Polytechnique in Paris, France. I also hold a Master of Engineering from EMINES, School of Industrial Management at the Mohammed VI Polytechnique University (UM6P) in Morocco.

My academic CV can be accessed here.

News

• [Jan. 2024] Our paper "Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks" is accepted to ICLR 2024!
• [Dec. 2023] Our paper "Simple and Yet Fairly Effective Defense for Graph Neural Networks" is accepted to AAAI 2024!
• [Nov. 2023] Presented our paper "UnboundAttack : Generating Unbounded Adversarial Attacks to Graph Neural Networks" at 12th Internation Conference on Complex Networks. - [Slides]
• [Sep. 2023] Presented our paper "Conformalized Adversarial Attack Detection for Graph Neural Networks" at 12th Symposium on Conformal and Probabilistic Prediction with Applications. - [Slides]
• [Jun. 2023] Our paper "Simple and Yet Fairly Effective Defense for Graph Neural Networks" is accepted to the AdvML workshop at ICML 2023!
• [Apr. 2023] New Preprint of our work on detecting ARGs using both Sequence and Structures. This work has also been presented at the AI forScience at Neurips 2022.

Publications