Senior Lecturer

Dr.Edna Milgo

Computing and Informatics

Wasifu wa kitaalamu

Dr. Edna Milgo is a Senior Lecturer in the Department of Computing and Informatics at the Open University of Kenya and a researcher whose interdisciplinary work focuses on Artificial Intelligence (AI) and Cybersecurity. Her recent research includes developing a Markov Chain Monte Carlo framework for adaptive sampling and a signature detection framework for metamorphic malware.

She chairs the development of the Open University of Kenya's AI Policy and contributes to various technical committees in AI and cybersecurity. Dr. Milgo holds a PhD from Vrije Universiteit Brussel, Belgium, and a Master's degree from Columbus State University, USA.

Research Interests

Artificial Intelligence and Machine Learning
Cybersecurity
Adaptive Markov Chain Monte Carlo (MCMC) sampling
Metamorphic malware detection

Matriksi ya kitaaluma

Sifa

PhD, Vrije Universiteit Brussel, Belgium
Master's degree, Columbus State University, USA
BSc in Computer Science, Kenyatta University

Utaalamu mkuu

Artificial Intelligence
Cybersecurity
Machine Learning
Adaptive MCMC Sampling
Malware Detection

Machapisho teule yaliyokaguliwa na rika

Mandela, N., Ojino, R., Milgo, E., & Mwangi, W. (2026). Ensemble machine learning framework for missing micronutrient prediction in food composition tables. In 2026 IEEE Conference on Computing. IEEE.
Liu, B., Milgo, E., Ronoh, N., & Bernard, M. (2023). Comparison of MCMC adaptation schemes: A preliminary empirical study. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 631–634). Association for Computing Machinery. https://doi.org/10.1145/3583133.3590729
Milgo, E., & Ronoh, N. (2020). A comparative study of adaptive MCMC schemes with stopped and diminishing adaptation. Journal of Applied Computer Science & Mathematics, 14(1), 15–22.
Milgo, E., Ronoh, N., Waiganjo, P., & Bernard, M. (2017). Adaptiveness of CMA based samplers. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 165–166). Association for Computing Machinery. https://doi.org/10.1145/3071178.3071239
Milgo, E., Ronoh, N., Waiganjo, P., & Bernard, M. (2017). Comparison of adaptive MCMC samplers. In Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 445–450).
Ronoh, N. K., Odoyo, R., Milgo, E., Drugan, M. M., & Bernard, M. (2015). Bernoulli bandits: An empirical comparison. In Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 517–522).
Kimutai, S. K., Milgo, E., & Gichoya, D. (2013). Isolated Swahili words recognition using Sphinx4. International Journal of Computer Applications, 77(16), 1–5. https://doi.org/10.5120/13538-1296
Milgo, E. (2009). A secure unidirectional proxy re-encryption using identity and secret key exchange. In Proceedings of the 47th Annual ACM Southeast Conference. https://doi.org/10.1145/1566445.1566539