Rukayat Olapojoye received her bachelor's (2014) and master's (2018) degrees in computer science from the University of Ibadan, Nigeria. Before coming to the United States, she worked in Nigerian industry as a software engineer, IT specialist, and system analyst. Currently, she is pursuing her PhD in computer science at Texas Tech University, where her research focuses on federated learning (FL), secure AI systems, and privacy-preserving technologies. Recently, she introduced RewardFL, a reputation-driven aggregation mechanism that defends against both static and adaptive poisoning attacks, setting a new benchmark in resilient FL. As a future faculty member, she intends to establish a lab that addresses blockchain-based FL coordination, explainable AI techniques, and adversarial robustness. She foresees her work offering a practical framework for deploying FL in sensitive domains like digital health, finance, and smart cities. Olapojoye considers mentorship central to her identity as a researcher and educator. Just as she has benefited as a mentee, she plans to equally give back by mentoring students. She has contributed to equity in STEM by serving as a reviewer for leading technical conferences, where she has provided constructive feedback for the success of diverse research communities. She also was a recipient of the 2022 Women Techsters Fellowship and the 2023 Google Computer Science Research Mentorship Program. Her teaching approach centers on demystifying complex concepts and guiding students through inquiry-based problem solving.

Research Interests

Federated Learning, Blockchains, Distributed Systems