Jonathan Mwaura
(he/him/his)
Associate Teaching Professor
Research interests
- Evolutionary computation
- Multimodal optimization
- Robotics
Education
- PhD in Computer Science, University of Exeter – UK
- BS in Computer Science, Kenyatta University – Kenya
Biography
Jonathan Mwaura is an associate teaching professor at the Khoury College of Computer Sciences at Northeastern University. He teaches introductory mathematics courses, algorithms, artificial intelligence and machine learning.
Mwaura earned his doctorate in computer science from the University of Exeter — UK and his bachelor’s in computer science from Kenyatta University — Kenya. He is part of the Carnegie African Diaspora Fellowship Program and have received NRF (Kenya) Funding for enhancing e-learning using artificial intelligence. Prior to joining Northeastern in 2021, Mwaura was an assistant teaching professor at the University of Massachusetts Lowell. Notable journals he has published in include the Algorithms Journal, ACM and IEEE. Outside of research and teaching, he is passionate about golf and promoting education as a poverty eradication tool.
Recent publications
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Computer Science Higher Education Curriculum and Accreditation Strategies to Enhance Africa’s Computing Skills
Citation: Michael J. Oudshoorn, Karen L. Bradshaw, Oluwatoyin Adelakun-Adeyemo, Engineer Bainomugisha, Leonard Peter Binamungu, Janet Liebenberg, Jonathan Mwaura, Costain Nachuma. (2025). Computer Science Higher Education Curriculum and Accreditation Strategies to Enhance Africa's Computing Skills CompEd (Working Group Reports), 1-32. https://doi.org/10.1145/3815397.3815768 -
The Process of Collaboratively Creating a Global Computing Education Terminology Resource with GenAI-in-the-Loop
Citation: Amruth N. Kumar, Michael J. Oudshoorn, Mohammed Seyam, Mor Friebroon Yesharim, Rukiye Altin, Leonard Peter Binamungu, Karen L. Bradshaw, Carlos Cabrera, Nils Dyck, Malayam Parambath Gilesh, Paul He , Andreea Molnar, Jonathan Mwaura, Liviana Tudor. (2026). The Process of Collaboratively Creating a Global Computing Education Terminology Resource with GenAI-in-the-Loop ITiCSE (2), 737-738. https://doi.org/10.1145/3803401.3812050 -
Future Ready, Globally Relevant: Aligning Africa’s Tertiary Computer Science Education Objectives with Global Standards
Citation: Michael J. Oudshoorn, Karen L. Bradshaw, Joseph Ahor Abandoh-Sam, Oluwatoyin Adelakun-Adeyemo, Engineer Bainomugisha, Leonard Peter Binamungu, Mohamed Essam Khedr, Janet Liebenberg, Jonathan Mwaura, Costain Nachuma. (2025). Future Ready, Globally Relevant: Aligning Africa's Tertiary Computer Science Education Objectives with Global Standards CompEd (2), 373-375. https://doi.org/10.1145/3736251.3749527 -
Connectivity Schemas in NeuroEvolution: What Neural Architectures does GEPNN evolve?
Citation: Jonathan Mwaura, Ryan Heminway. (2023). Connectivity Schemas in NeuroEvolution: What Neural Architectures does GEPNN evolve? SSCI, 1829-1836. https://doi.org/10.1109/SSCI52147.2023.10371957 -
A Conceptual Framework for Automatic Generation of Examinations Using Machine Learning Algorithms in Learning Management Systems
Citation: Emma Cheserem, Elizaphan M. Maina, John Kihoro, Jonathan Mwaura. (2022). A Conceptual Framework for Automatic Generation of Examinations Using Machine Learning Algorithms in Learning Management Systems WCCE, 441-450. https://doi.org/10.1007/978-3-031-43393-1_41 -
Diversity Measures for Niching Algorithms
Citation: Jonathan Mwaura, Andries P Engelbrecht, Filipe V Nepomuceno (2021). Diversity Measures for Niching Algorithms. Journal of Algorithms, Vol 14, Issue 2. -
Optimized K-Means clustering algorithm using an intelligent stable-plastic variational autoencoder with self-intrinsic cluster validation mechanism
Citation: Rufus Gikera, Shadrack Mambo, Jonathan Mwaura (2020). Optimized K-Means clustering algorithm using an intelligent stable-plastic variational autoencoder with self-intrinsic cluster validation mechanism. ICONIC '20: Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications. -
Performance measures for niching algorithms
Citation: J. Mwaura, A. P. Engelbrecht and F. V. Nepocumeno, "Performance measures for niching algorithms," 2016 IEEE Congress on Evolutionary Computation (CEC), 2016, pp. 4775-4784, doi: 10.1109/CEC.2016.7744401.
