Bruno Fonkeng is a Computer Science Ph.D. student at the University of North Dakota, advised by Dr. Jielun Zhang. His work sits at the intersection of probabilistic machine learning, cybersecurity, and high-performance computing, with a focus on generative models and adaptive defense for modern cyber threats. He is particularly interested in using GANs, VAEs, diffusion models, and related architectures to simulate realistic APT behavior on networks, malware traffic, and cloud infrastructures, and then coupling these simulations with reinforcement-learning–based defenders that can learn to respond in real time. Through teaching and research assistantships, he contributes to UND’s initiatives in advanced computing, applied AI, and secure systems engineering.
Before beginning his doctoral studies, Bruno earned a B.Sc. in Physics from the University of Bamenda, Cameroon, where he developed strong analytical and quantitative skills that sparked his interest in scientific computing and AI. The rigorous mathematical training from physics now underpins his research in probabilistic modeling, statistical reasoning, and algorithmic optimization.
At UND, he has completed graduate coursework spanning machine learning, applied machine learning, artificial intelligence, data visualization, predictive modeling, application layer security, ethical hacking, security for cloud computing, high-performance computing & paradigms, and computer forensics, along with key mathematics courses in linear algebra and statistical theory. He is actively deepening his expertise in HPC, probabilistic modeling, and formal methods to support his dissertation work.
Bruno’s current research explores probabilistic generative models for cybersecurity, including synthetic cyber-traffic and malware image generation, data augmentation for intrusion detection, and robust anomaly detection. His long-term goal is to develop a probabilistic simulation-to-defense framework that can generate realistic, large-scale cyber data and train self-improving defensive agents for cloud environments. By combining rigorous statistical modeling, scalable experimentation on HPC systems, and practical systems thinking, he aims to help close the gap between academic ML models and deployable, trustworthy security tools that protect real organizations.
