Lecturer and research Coordinator
Dr Allan Omondi is a Lecturer and Research Coordinator at Strathmore University, Kenya, with over ten years of experience in integrating teaching, research, and institutional leadership to advance technological innovation. He specializes in applying Artificial Intelligence and mathematical optimization techniques across the entire data lifecycle – ranging from data ingestion, storage, and processing to predictive and prescriptive modelling, visualization, interpretation, and archival. He extends this technical domain-specific expertise to development economics, where his work focuses on optimizing community-level resource allocation for sustainable social welfare.
He has supervised over twenty graduate-level research projects to completion as the lead research mentor. His research mentorship extends into a consultancy function through which he directs graduate research teams to deliver data-driven solutions for partners in the private sector and Non-Governmental Organizations. His consultancy work leverages data analytics to deliver intelligence that improves operational performance and guides strategic business decisions.
Dr Omondi received his Bachelor of Business Information Technology in 2012, Master of Science in Information Technology in 2015, and Doctor of Philosophy in Information Technology in 2020, all from Strathmore University. He facilitates learning across several IT-related programs with a particular passion for Business Intelligence and Advanced Database Systems.
His interests include conducting impact-driven research, mentoring emerging scholars, disseminating knowledge, and maintaining personal discipline through long-distance running and consistent fitness routines.
2016 to 2020: Doctor of Philosophy in Information Technology from Strathmore University. Thesis title: “A Monte Carlo Tree Search Algorithm for Optimization of Load Scalability in Database Systems”; supervised by Professor Ismail Ateya and Professor Gregory Wanyembi.
2013 to 2015: Master of Science in Information Technology (Systems Security and Audit Option) from Strathmore University. Thesis title: “Vision-Based Data Fruit Maturity-Level Prediction Model: A Case of Pawpaw in Makueni County”; supervised by Dr Joseph Orero.
2008 to 2012: Bachelor of Business Information Technology (Database Administration Option) from Strathmore University. Capstone project title: “Analysis and Design of the Vote Book System: A Computer Based Expenditure Accounting Management Information System for Kenya Wildlife Service”; supervised by Professor Ismail Ateya
His research focuses on the application of Artificial Intelligence and mathematical optimization techniques across the entire data lifecycle, with specific emphasis on autonomous database management systems. His work in this area examines self-driving database management systems capable of intelligent query optimization (particularly for join-crossing correlations), as well as automated performance tuning (knob configuration), physical database design (indexing and partitioning), and adaptive hardware provisioning.
He extends this technical domain-specific expertise to development economics, where he employs data analytics, Machine Learning, and mathematical optimization in community-level resource allocation for sustainable social welfare. He collaborates with private-sector and Non-Governmental Organization partners in a consultancy capacity to translate research output into practical, high-impact solutions.
Open Researcher and Contributor ID (ORCID): https://orcid.org/0000-0001-5752-8357
2024 University of Michigan African Presidential Scholar
Dr Allan Omondi is committed to facilitating learning through deliberately structured cognitive engagement that requires learners to master both the breadth and the depth of subject matter with academic discipline and intellectual clarity. His teaching philosophy prioritises rigorous analytical practice, habitual intellectual curiosity, and the consistent translation of theory into durable, real-world competence. Over time, his students are expected to develop the disciplined analytical reasoning, methodological maturity, practical skills and competencies, and professional readiness that enable them to excel in demanding academic and industry environments.
He has successfully supervised over twenty graduate-level research projects since 2022 and over one hundred and thirty undergraduate capstone projects since 2015, defining successful supervision as progression from proposal approval to a defended and passed final examination. He is currently co-supervising a doctoral candidate working on a thesis titled “Deep Learning Model for Cardinality Estimation of Multi-Attribute Queries in a Cost-Based Query Optimizer.”