Alec F. Diallo

Alec F. Diallo is a Postdoctoral Research Associate at the University of Edinburgh's School of Informatics, affiliated with the Mobile Intelligence Lab and Institute for Computing Systems Architecture. He concurrently serves as a Machine Learning Engineer at Net AI, bridging academic research with industry applications in secure networked systems.

His educational background includes a joint Computer Science and Electrical Engineering degree from École Supérieure d'Ingénieurs en Électronique et Électrotechnique (ESIEE Paris) and Mundiapolis University, followed by a PhD in Computer Science at the University of Edinburgh under Dr. Paul Patras.

Dr. Diallo's research centers on computational intelligence with emphasis on adversarial robustness, explainable AI, and optimization algorithms applied to network security. His work develops novel frameworks like Amoeba for censorship circumvention and Sabre for neural network defense, addressing critical vulnerabilities in edge intelligence and secure communications through advanced machine learning techniques.

His publication trajectory reveals consistent innovation in applying clustering methods and spectral analysis to cybersecurity challenges, with increasing focus on real-world deployment at network edges. Recent work demonstrates exceptional performance in malicious traffic classification (100% accuracy) and robust adversarial defense across diverse domains including image recognition and network intrusion detection.

Notable recognitions include:

• SICSA 2024 Best Dissertation Award for "Active Intelligence for Secure Systems"
• Brendan Murphy Memorial Prize at the 33rd Next Generation Networking workshop (2021)

He actively contributes to the academic community as reviewer for AISTATS and IEEE journals, Program Committee Member for IEEE S&P 2024, and frequent invited speaker at institutions including Cambridge, Imperial College London, and Glasgow. His research is supported by Arm funding and features strong industry collaboration through Net AI.

As core member of Edinburgh's Mobile Intelligence Lab, he advances edge computing security through projects spanning network traffic analysis, censorship resistance, and robust neural architectures, with ongoing work focusing on scalable clustering validation and causal explainability frameworks.