- 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
- Image phenotyping for necrosis in cassava roots
- Causal discovery in disease data
- Mobile monitoring of crop disease
- Automated malaria diagnosis with digital microscopy
- Data generation and language technology for low-resourced African languages
- Robust traffic flow monitoring
- Spatiotemporal models for biosurveillance
- Kudu: Auction design for agricultural commodity trading
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”.
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.For more information about this project here. Work supported by Microsoft Research.
The realization of developing natural language processing techniques in tasks such as Machine Translation (MT) requires the availability of monolingual and cross-lingual resources. Currently, the exploration of various advances in NLP techniques for low-resource languages and language pairs in the developing world is complicated by the lack of data resources. For example, in Uganda, where there are over 40 independent languages, there are no monolingual nor bi/multilingual resources for developing NLP systems such as those that significantly benefit well-resourced languages. Now, we are using both manual and existing automated methods to build bilingual corpora for several language pairs involving any low-resourced African language. We plan to use the corpora to explore several NLP applications involving any of the respective low-resourced African languages.
Work supported by a Google Research Award.
We are trialling an auction system called Kudu, which is designed for trading agricultural commodities in Uganda by phone or web. This is a double auction, meaning that buyers and sellers submit their information separately, and we computationally find the best matches.This approach seems more promising than both single auction systems (i.e. listings sites, which can’t be used with a basic phone or anywhere bandwidth is scarce) and price advisory systems, which have problems with accuracy and timeliness (wholesale market prices in Kampala change in the course of hours, hence a weekly price bulletin is of limited use). By matching buyers and sellers algorithmic-ally we can overcome these problems. The prototype web interface to the auction system can be tried here: kudu.ug (requires a Ugandan mobile phone number for registration), or text BUY or SELL to 8228. The crops we are currently supporting are coffee, beans, sweet banana and watermelon. Work supported by a Google Research Award.