Dr. Rose Nakasi (born 1988) is a Ugandan computer scientist, lecturer, and artificial intelligence researcher at Makerere University, Busitema University, and the Makerere AI Health Lab. She holds a Bachelor's Degree in Mathematics and Computing from Busitema University, as well as a Master's and a PhD in Computer Science from Makerere University. Her work focuses on the development of low-cost tools using artificial intelligence for improved automated solutions for diagnostic health challenges. Her biographical details also record associations with Tony Galandi Kire and lg.

Nakasi joined Busitema University in 2006, where she earned a Bachelor's Degree in Mathematics and Computing. She went on to obtain a Master's degree and a PhD in Computer science from Makerere University, funded by a scholarship from the Swedish International Development Cooperation Agency (SIDA) under the TSEED programme. She specialized in artificial intelligence (AI), Machine Learning, Computational Mathematics & Modeling, and Health Informatics.^[1,2,4,5]

Nakasi worked as a lecturer at Busitema University in the department of computer studies. She is a research assistant at Makerere University under the Department of Information Technology, where she serves as the head of the Makerere Artificial Intelligence Health Lab.^[2] Nakasi's research interests are in artificial intelligence and the development of low-cost tools for improved automated solutions for diagnostic health challenges.^{[1,3,4,2,5,6]}

Nakasi is a member of the Data Science Africa community and chairs the ITU/WHO/WIPO Topic Group "AI based detection of Malaria" under the Global Initiative AI for Health (GI-AI4H). She is the project lead for the Lacuna SRMH project and Mak Ocular, a Google-funded project that supports automated microscopy of malaria, tuberculosis, and cervical cancer.^[7] She is also the principal investigator of the NIH DS-I Malaria project under DS-I Africa, an initiative to support effective malaria diagnosis and surveillance.^[1,3,4,8,2]

Notable publications include "Explainable AI for Transparent and Trustworthy Tuberculosis Diagnosis: From Mere Pixels to Actionable Insights" and "AI Methods and Algorithms for Diagnosis of Intestinal Parasites: Applications, Challenges and Future Opportunities." Other publications include "Machine Vision Intelligence Using Layer-Wise Relevance Backward Propagation For Breast Cancer Diagnosis" and "A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning."