IBM research in Africa : an overview of the projects helping to build Africa's future and career opportunities for mathematicians

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• Dr. Kamal Bhattacharya, Dr. Osamuyimen Stewart and Dr. Meena Pore
• Tuesday 20 October 2015, 17:00-18:00
• MR4, Centre for Mathematical Sciences.

If you have a question about this talk, please contact John Shimmon.

Abstract: Abstract: Africa is poised to become a leading source of innovation in a variety of sectors, with an expected growth rate of 7% annually over the next 20 years. IBM recognizes the huge potential impact of research and smarter systems in helping to build Africa’s future, hence the lab is focused on technology applications in a range of industries at the core of Africa’s growth. Employing some of the best scientists from around the world and is partnering with universities around the world to develop and hire top talent. There are career opportunities for Masters, PhDs and Post Docs. Dr Kamal Bhattacharya (Director, IBM Research – Africa) and Dr Osamuyimen Stewart (Chief Scientist, IBM Research – Africa) will give an introduction to IBM Research – Africa, and an overview of teams and projects at the Nairobi and Johannesburg. The research areas include:

Active Learning, : Acquiring high-quality labelled data in resource-constrained settings is difficult. Active Learning can be used to drastically reduce the cost of gaining insights from noisy background data. For example, which household should be targeted in a healthcare survey based on the roof top composition of the dwelling estimated from satellite imagery?

Transfer Learning: Africa is an incredibly diverse continent and even the most successful innovation in one country may have very low performance in other regions. Transfer learning provides systematic approaches that allow researchers to re-use informative data streams for adaptation to a new task, new demographic, or both. For example, based on a supervised classification model, we predict that a particular cell phone user in Kenya will be able to repay a micro-loan – will this model work in Nigeria as well?

Object recognition: Given the proliferation of affordable imaging technologies, from drone aerial imaging to phone cameras, how can such technologies be used to lower the cost and improve the quality of evidence-based policy? For example, given the incredible rate of growth of Africa’s cities, how can city planners use drone imagery to better understand changes in population density and socioeconomic status of communities, and hence forecast the demand for various public services?

Distributed Computing: Mobile phones are the already-present incarnation of IoT in Africa. Harnessing this data requires new approaches that can handle society-scale data. IBM is globally investing in Apache® Spark™ to create advances in large scale data processing.

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