Maria Kanyama

Maria Nelago Kanyama is currently pursuing a Doctoral degree in Computer Science, specializing in Artificial Intelligence and Blockchain applications. A lifelong learner and highly accomplished professional, Maria was recognized as a L'OréalUNESCO Sub-Saharan FWIS Young Talent in 2023. She has participated in the Women in Tech and AI program, focusing on Artificial Intelligence, Data Science, Women in Leadership, prospective applications of AI, AI project management, market research, and benchmarking. As part of the African Leadership Academy - L'Oréal Leadership Enrichment Stream, she has received mentorship and developed strong leadership and project management skills. Maria has represented her country and contributed to global initiatives as a delegate at the AFS Youth Assembly in 2024 and the Africa-Berlin Startup Connect Week in 2023. Her diverse experiences and achievements reflect her commitment to personal growth, leadership, and making a positive impact in her field and beyond.

• L'Oréal-UNESCO Sub-Saharan FWIS Young Talent 2023
• African Women in Tech and AI (AWITAI) 2024 - Trainings on Artificial Intelligence and Data science, women's leadership, prospective applied to AI, Benchmarking, Market research, AI Project management, and startup business in AI pitching
• AFS Global Up Competence 2024 - Training aimed in developing intercultural communication, active listening, & conflict resolution skills, self-awareness and emotional intelligence, expanding awareness of other cultures and learning how to effectively bridge across cultural differences.
• Make-IT Women in Leadership 2023
• Lead-F program 2024
• African Leadership Academy - Leadership program 2024 -Training on project model canvas, presentation, Design Thinking and ALA's BUILD Model.
• AFS Youth Assembly delegate 2024
• Africa-Berlin Startup connect week delegate 2023, Berlin - Germany
• AFS Youth Assembly delegate 2024, New-York - USA
• Telecom Namibia Bursary award recipient 2010
• SASSCAL IWRM PhD scholarship award 2022

• Anomaly detection techniques in Smart Water Metering Network, Kanyama, M. N., Nyirenda, C. N. & TemanehNyah, C., The 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2017), 11/02/17, Beijing, P.R. China
• Machine Learning Anomaly Detection Techniques Applicable to Smart Water Metering Networks - A Systematic Review, Kanyama, M. N., Bhunu Shava, F., Gamundani A.M. & Hartmann, A., 23WNSYMP (23rd WaterNet/WARFSA/GWPSA Symposium), 10/19/22, Rustenburg, South Africa at the Sun City Resort
• Blockchain Technology Approach on Securing Smart Water Metering Networks Toward Anomaly Free: An Overview and Future Research Directions, Kanyama, M. N., Bhunu Shava, F., Gamundani, A. M. & Hartmann, A., SAI 2023
• Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review, Kanyama, M. N., Bhunu Shava, F., Gamundani A.M. & Hartmann, A., Physics and Chemistry of the Earth, Parts A/B/C, 2024
• Kanyama, M. N., Bhunu Shava, F., Gamundani A.M. & Hartmann, A ., Anomalies identification in Smart Water Metering Networks: Fostering Improved Water Efficiency,., Physics and Chemistry of the Earth, Parts A/B/C, Physics and Chemistry of the Earth, Parts A/B/C, 2024. http://dx.doi.org/10.1016/j.pce.2024.103592
• Kanyama, M. N., Bhunu Shava, F., Gamundani A.M. & Hartmann, " Enhancing Anomaly Detection in Smart Water Metering Networks with LSTM-Autoencoder and Data Augmentation Techniques," 2024 4th International Multidisciplinary Information Technology and Engineering Conference (IMITEC) (Accepted for publication)
• Nantinda, T., Gamundani A.M, Kanyama, M. N, "Machine-Learning Application for Detection and Prediction of Stroke Risk and Post-Stroke Outcomes: A Comprehensive Review". icABCD 2024 - Proceedings (6th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (Accepted for publication)
• Ndjene, J., Gamundani A.M, Kanyama, M. N, "Leveraging Machine Learning to Detect and Predict Diabetes in Polycystic Ovary Syndrome Patients: A Review", icABCD 2024 - Proceedings (6th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (Accepted for publication)

• Prof Attle M Gamundani, agamundani@nust.na, +264816466549
• Prof Clement Temaneh-Nyah, clementtemaneh@gmail.com, +264816219715
• Prof Fungai Bhunu Shava, fbshava@nust.na, +264813289988