This is the homepage of the AI-DEV group in the School of Computing and Informatics Technology, Makerere University, a research group studying applications of machine learning, pattern recognition and computer vision in the developing world.
People: Jennifer Aduwo, Guy Acellam, Ernest Mwebaze, John Quinn
Cassava is the world's third largest source of carbohydrate, and can grow in hostile conditions where other crops cannot, but has one major weakness: susceptiblity to viral disease. Monitoring the spread of disease is essential in countries which depend on it as a staple crop, but the processes currently employed are expensive and slow. We are working on an automated system using $100 smartphones to capture images, diagnose disease with computer vision techniques and provide real time map information.
See the live crop surveillance app for more information.
Work supported by a Google Research Award.
People: Martin Mubangizi, John Quinn.
It is useful to know the geographical density of a transmittable disease in order to plan interventions and to predict its future developments. This can be difficult where there is a lack of co-ordinated statistics, which is often the case where diseases like malaria or tuberculosis are endemic. In such situations it is possible to combine irregular updates from a variety of less consistent sources. We are looking at the use of spatiotemporal state space models for biosurveillance, for use when there are irregular updates about disease counts.
Work supported by IBM.
People: John Quinn, Ian Munabi, Edison Mworozi, Alfred Andama.
The most reliable test for malaria is microscopic examination of blood films for presence of the parasite. The problem with this is that it requires equipment, and an expert on-site to use it. Some researchers have recently indicated the promise of combining microscopy with mobile phones, in order to mitigate the requirement for an expert to be physically present, and others have investigated the use of computer vision techniques for automatic classification, so that a human expert need not be available at all. However, all of this work has been undertaken in ideal laboratory conditions. We are working on developing these ideas and to trial an automated diagnosis system in the field, intended for use by non-experts. We deal with thick blood film slides as shown.
Work supported by Microsoft Research.
People: Ernest Mwebaze, John Quinn
It is sometimes thought to be impossible to discover causes of events without any background knowledge or the ability to do experiments. However, the field of inferring causes and effects with purely observational data is developing. Correlation does not directly imply causation, but some patterns of association make particular causal relationships more likely than others.
This work is focused on developing fast methods to find strong causes and effects related to a target variable from a large set of covariates. This is useful (1) for gaining insight into a domain, and (2) for prediction of the effects of interventions. We are particularly interested in applying this to data collected in Uganda concerning prevalence of disease and the outbreak of epidemics such as cholera and ebola. This analysis could confirm or disconfirm our ideas about climatic, demographic and environmental factors which are thought to influence such events. An indication of the relative strengths of different causes can also help in predicting the efficacy of different eradication policies.
Entry to NIPS 2008 causal discovery competition received honourable mention for "significant advance on the REGED dataset".
People: George Okori, Ernest Mwebaze, John Quinn
Food shortages are increasing in many areas of the world. We are looking at how to infer the probability of households experiencing famine, based on demographic and geographical features. We are also interested in using structure learning techniques to understanding the causal relationships between these factors and famine risk.
People: Rose Nakibuule, John Quinn.
Traffic monitoring systems usually make assumptions about the movement of vehicles, such as that they drive in dedicated lanes, and that those lanes rarely include non-vehicle clutter. Urban settings within developing countries often present extremely chaotic traffic scenarios which make these assumptions unrealistic. We are working on robust techniques for traffic congestion monitoring. Instead of tracking individual vehicles we treat a lane of traffic as a fluid and estimate the rate of flow.