School: School of Computing and Informatics
Department: Computing Department
Email Address: tnjoroge@karu.ac.ke
Area/ Field of specialization: Machine Learning and Artificial Intelligence
Research interests:
Machine Learning; Artificial Intelligence; Fuzzy Logics; Data Mining; Computer Vision; Natural Language Processing; IoT Applications in Agriculture; Collaborative Computing and Edge computing
Research Links:
ORCID ID: 0009-0000-2147-9848
Google Scholar: https://scholar.google.com/citations?hl=en&authuser=2&user=5c2J2BEAAAAJ
https://www.researchgate.net/profile/Thomas-Njoroge-4/research
Dr. Thomas Kinyanjui Njoroge is a PhD-trained Artificial Intelligence researcher and applied machine learning engineer specializing in deep learning, edge-optimized AI, and multimodal intelligence systems. He holds a PhD in Artificial Intelligence and focuses on designing intelligent systems that balance predictive accuracy, computational efficiency, and real-world deployability in resource-constrained environments.
Currently serving as Lecturer and Head of the Computing Department at Karatina University, Dr. Njoroge has over 15 years of experience teaching, supervising, and mentoring undergraduate, master’s, and PhD students in Artificial Intelligence, Software Engineering, Machine Learning, and Information Systems. His technical expertise spans supervised and unsupervised learning, convolutional neural networks (CNNs), Vision Transformers (ViTs), transfer learning, attention mechanisms, feature engineering, explainable AI, and multimodal fusion architectures.
His research is particularly centered on intelligent agriculture and healthcare applications, where he integrates computer vision with IoT sensor streams such as temperature, humidity, and environmental telemetry for robust predictive modeling. He developed AgriScan, an edge-optimized multimodal crop disease detection framework combining lightweight CNN backbones, transformer encoders, and gated cross-attention modules for deployment on Android devices. He also developed SemaDeep, an AI-powered research assistant built using Retrieval-Augmented Generation (RAG), advanced prompt engineering, and agentic reasoning workflows.
Dr. Njoroge has published extensively in Scopus-indexed journals on hybrid CNN-Transformer systems, statistical validation of AI models, efficiency-accuracy trade-offs, and real-time intelligent decision support systems. His mission is to advance ethical, explainable, and human-centered AI through scalable machine learning solutions that create measurable impact in food security, healthcare, and intelligent automation.