Summary
Daniel Ogenrwot is a CS PhD student and Graduate Research Assistant at UNLV’s EVOL Lab with a decade of experience building AI-driven and cloud-native systems. His research blends empirical software engineering, machine learning, and AI-assisted development—focusing on patch and clone detection, mining software repositories, and effect patch integration across structurally diverged codebases. He investigates how LLMs can improve automated patch generation, code comprehension, and developer productivity while keeping explainability and reliability front and center. Daniel has applied these skills in industry-facing projects, from designing the AirQo Analytics SaaS for near-real-time air quality monitoring to evaluating microservice performance in low-connectivity settings. A proven mentor and collaborator, he bridges academia and practice through interdisciplinary work across AI, DevOps, and software analytics. He pairs top-tier academic credentials and publication experience with hands-on system design in resource-constrained environments—bringing pragmatic rigor to research-driven engineering.
9 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Nevada-Las Vegas
Bachelor of Science (B.S.), Computer Science, 4.48/5.0 First Class Honours 🎖, Bachelor of Science (B.S.), Computer Science, 4.48/5.0 First Class Honours 🎖 at Gulu University
Master of Science - MS, Computer Science, 4.47/5.00 (Distinction), Master of Science - MS, Computer Science, 4.47/5.00 (Distinction) at Makerere University
English, luo, Swahili