AI Research applied to African problems
An AI & Data Science Research Group based at Makerere University, Uganda
ABOUT MAKERERE AI LAB
The AI and data science research group at Makerere University specialises in the application of artificial intelligence and data science -including, for example, methods from machine learning, computer vision and predictive analytics-to problems in the developing world.
Applications: natural language processing for under-resourced languages, automated diagnosis of both crop and human diseases, auction design for mobile commodity markets, analysis of traffic patterns in African cities, and of telecoms and remote sensing data for anticipating the spread of infectious diseases
VISIONExcellence in Artificial Intelligence research for accessible solutions.
MISSIONTo advance artificial intelligence research to solve real-world challenges.
The Varied projects of Makerere AI lab
- End-to-end AI and data systems for targeted surveillance and management of COVID-19 and future pandemics affecting Uganda (COAST))
- Building NLP Text and Speech Datasets for Low Resourced Languages in East Africa
- Machine Learning Datasets for Crop Pest and Disease Diagnosis based on Crop Imagery and Spectrometry Data
End-to-end AI and data systems for targeted surveillance and management of COVID-19 and future pandemics affecting Uganda (COAST))
In sub-Saharan Africa data imbalances and underrepresentation can easily arise due to unequal access to government and private services where data is collected due to socio-demographic conditions. COAST will address these challenges through three specific objectives: 1- To strengthen data systems resulting in usable and equitable datasets for AI-driven COVID-19 responses and future pandemics. 2- To develop and deploy AI-driven detection and diagnosis tools for improved patient care and management. 3- To model and evaluate COVID-19 interventions for targeted government responses based on the fused datasets from objectives 1 and 2.
Building NLP Text and Speech Datasets for Low Resourced Languages in East Africa
The project will deliver open, accessible, and high-quality text and speech datasets for low-resource East African languages from Uganda, Tanzania, and Kenya. Taking advantage of the advances in NLP and voice technology requires a large corpora of high quality text and speech datasets. This project will aim to provide this data for these languages: Luganda, Runyankore-Rukiga, Acholi, Swahili, and Lumasaaba. The speech data for Luganda and Swahilli will be geared towards training a speech-to-text engine for an SDG relevant use-case and general-purpose ASR models that could be used in tasks such as driving aids for people with disabilities and development of AI tutors to support early education. Monolingual and parallel text corpora will be used in several NLP applications that need NLP models, including natural language classification, topic classification, sentiment analysis, spell checking and correction, and machine translation. This work is supported by;
Machine Learning Datasets for Crop Pest and Disease Diagnosis based on Crop Imagery and Spectrometry Data
This project will produce quality open and accessible image and spectrometry datasets from Uganda, Tanzania, Namibia, and Ghana for several crops that contribute to food security in Sub-Saharan Africa, including cassava, maize, beans, bananas, pearl millet, and cocoa. The team -composed of data scientists and researchers from Makerere University, The Nelson-Mandela African Institution of Science and Technology, Namibia University of Science and Technology, and the karaAgro AI Foundation – expect the image and spectral datasets will be used for early disease identification, disease diagnosis, and modelling disease spread, which will ultimately help in breeding resistant crop varieties. This work is supported by;
WE ARE A COLLABORATIVE TRANSDISCIPLINARY, AND DIVERSE TEAM WITH A BIAS FOR APPLYING MACHINE LEARNING TO SOLVE PROBLEMS IN THE DEVELOPING WORLD.
Research and Administrative Assistant
Research Software Engineer
Research Software Engineer
GODLIVER OWOMUGISHA (PhD)
ROSE NAKIBULE (PhD)
UPCOMING EVENTS , SEMINARS AND WORKSHOPS
Topic: Recommender systems to improve farmer to farmer expert interaction.
Presenter: Jeremy Francis Tusubira
Time: April 01, 2021 10:00 AM Nairobi
Join Zoom Meeting
Meeting ID: 962 2824 4771