Dr. Naeima Hamed hamednh@cardiff.ac.uk | naeima.hamed@gmail.com | https://naeima.github.io/Naeima Computer scientist specialising in data science, semantic web, and Artificial Intelligence (AI). I build ontologies and knowledge graphs for real-world applications. I also develop predictive models and AI tools for environmental and Internet of Things (IoT) systems. Skills & Expertise Data Science | Distributed Computing (Cloud, IoT) | Time-Series Analysis | Machine Learning | Deep Learning (TensorFlow, PyTorch) | Python | SPARQL | OWL2 | RDF | Google Colab | Jupyter Book | Academic writing using LaTeX | Fluent in Arabic & English. Education PhD in Computer Science, Cardiff University (2020–2024) Thesis title: Semantic Data Integration for the Forest Observatory Applications (Link). • Developed Forest Observatory Ontology and populated it with RDF datasets to create knowledge graphs (Ontology Link, w3id Link). • Achieved 99.04% accuracy predicting elephant movements using AI and then applied semantic reasoning to predict poaching incidents. MSc in Data Science, Cardiff University (2018–2019, Distinction) • Advanced training in statistics, operational research, and machine learning. • MSc project focused on predictive analytics using multivariate time series data. BSc (Hons) in Computer Engineering, The Future University, Sudan (1995–2000) • Designed and developed embedded systems combining electronic circuits and custom software for real-world applications. Experience Graduate Tutor, Cardiff University (2023–Present) • Supervise undergraduate projects and provide academic support to module instructors. Director, Cardiff Computer Centre- Wales, UK (2013–Present) • Design, build, and repair custom computer systems. Publications • Query Interface for Smart City IoT Data Marketplaces, ACM ToIT (2023), DOI • FOO: An Upper-Level Ontology for the Forest Observatory, Springer (2023), DOI • A Comparison of Open Data Observatories, ACM JDIQ (2024), DOI • FooDS: Knowledge Graphs for Forest Observatories, ACM JCSS (2024), DOI • PoachNet: Predicting Poaching Using Knowledge Graphs, Sensors (2024), DOI References: available upon request.