Designation
Chair of Department - Data Science & Artificial Intelligence/ Lecturer in Data Science & Artificial Intelligence
Staff E-mail Address
rgikera@kcau.ac.ke
Previous Titles
- Examinations Coordinator, NAC
Academic Qualification
- PhD Computer Science – Kenyatta University
- Msc. AI in Medicine (Ongoing) –University of Alabama Birmingham
- Msc. Computing – Strathmore University
- BEd (Mathematics & Computer Science) – Kenyatta University
- Higher Diploma IT (IMIS) – KCA University
- Diploma IT (IMIS) – Strathmore University
- Certificate in Bioethics – CBEC Pakistan & KEMRI
- Certificate in Neuroimaging – University of Oxford
Profile
Dr. Rufus Gikera is an interdisciplinary scholar, researcher, and educator in Data Science and Artificial Intelligence, with a specialized focus on AI in Medicine. Currently serving as a Lecturer in Data Science and Artificial Intelligence at both undergraduate and postgraduate levels, Dr. Gikera’s teaching philosophy and mentorship for the next generation of computer scientists centers on inquiry-driven learning, interdisciplinary thinking, and research excellence. He is also the founder and director of AIMedix Lab of Africa, a pioneering research and innovation hub advancing AI in medicine and drug discovery to support clinical intelligence and health equity across Africa. His research portfolio spans five core pillars of Computational Medicine:
- Computational Neuroimaging: Applying deep learning and statistical models to CT, MRI, PET, and DTI data to study neurodegeneration, brain connectivity, and cognitive disorders.
- Computational Molecular Medicine: Decoding genomic, proteomic, and multi-omic data for predictive modeling and drug target discovery.
- Computational Anatomy: Leveraging AI-based imaging analytics to detect structural and morphological biomarkers in disease.
- Computational Physiology: Simulating multi-scale biological systems to analyze disease dynamics and therapeutic responses.
- Computational Healthcare Systems: Developing AI-powered clinical decision tools, digital triage platforms, and EHR-integrated diagnostic models.
- Computational Genomics: Designing machine learning pipelines to analyze genomic variation, gene expression, and regulatory mechanisms for precision medicine and population-scale insights
Selected Publications: Books, Refereed publications & Non Refereed publications
- Optimized K-Means Clustering Algorithm using an Intelligent Stable-plastic Variational Autoencoder with Self-intrinsic Cluster Validation Mechanism. In Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications (pp. 1-11). ICONIC. Plaine Magnien, Mauritius. Available in the ACM Digital Library. doi.org/10.1145/3415088.3415125
- K-hyperparameter Tuning in High-dimensional Genomics using Joint Optimization of Deep Differential Evolutionary Algorithm and Unsupervised Learning from Intelligent GenoUMAP Embeddings. International Journal of Information Technology, Springer Nature. https://DOI: 10.1007/s41870-024-02279-x
- K-hyperparameter tuning in High-dimensional Space Clustering: Solving Smooth Elbow Challenges using an Ensemble Based Technique of a Self-adapting Autoencoder and Internal Validation Indexes. Journal of Artificial Intelligence. https://doi.org/10.32604/jai.2023.043229
- Computational Anatomy: K-hyperparameter Tuning of Heart Beat Phonocardiograms using an Improved Autoencoder Pre-trained on Multi-core tSNE. Artificial Intelligence in Medicine, Elsevier. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4820716
- Trends and Advances on the K-Hyperparameter Tuning Techniques in High-Dimensional Space clustering. Indonesian Journal of Artificial Intelligence and Data Mining, 6(2). https://ejournal.uin-suska.ac.id/index.php/IJAIDM/article/view/22718
- Computational Neuroimaging: A systematic Review of Brain MRI Segmentation using K-Means clustering. Clinical Imaging, Elsevier.
- Computational Neuroimaging: Joint Optimization of Deep Multimodal MRI Feature Fusion and Monte-Carol Drop out for Robust Segmentation of Non-Ellipsoidal Brain Tumours. Artificial Intelligence in Medicine, Elsevier.
- Computational Neuroimaging: Deep Ensemble Model with Attention Mechanism for Precise Classification of Intracranial Hemorrhages in CT scans using Hounsfield Units due to Acute Head Trauma. Artificial Intelligence in Medicine, Elsevier.
Research Interest
His special research interests include Neuroinformatics, Neurogenomics, Neuroproteomics, with a special focus on modeling brain diseases using Markov Chain Monte Carlo. With a PhD in Computer Science of Kenyatta University, and currently pursuing an MSc AI in Medicine at the Heersink School of Medicine, University of Alabama Birmingham, Dr. Gikera is driven by a bold and transformative vision of bridging the gap between artificial intelligence and clinical care, delivering innovations that are scientifically rigorous, bio-ethically grounded, and globally inclusive, while at the same time inspiring a new generation of translational AI scientists who will reimagine the future of medicine.
Current Research Project(s)
- Ensemble Model for Real-Time Monitoring of Waterborne Gastrointestinal Pathogens in the Eastern Cape Province, South Africa.
- Computational Neuroimaging: Deep Ensemble Model with Attention Mechanism for Precise Classification of Intracranial Hemorrhages in CT scans using Hounsfield Units due to Acute Head Trauma
Conferences & Chapters
- Optimized K-Means Clustering Algorithm using an Intelligent Stable-plastic Variational Autoencoder with Self-intrinsic Cluster Validation Mechanism. In Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications (pp. 1-11). ICONIC. Plaine Magnien, Mauritius. Available in the ACM Digital Library. https://doi.org/10.1145/3415088.3415125
Research Quality
Social Media and Academic Networking Tools
https://www.researchgate.net/profile/Rufus-Gikera
