In August 2019, I attended Deep Learning Indaba in Nairobi, Kenya. This annual meeting aimed at strengthening African Machine Learning. The conference brought together about 700 members of Africa’s Artificial Intelligence community at the Kenyatta University for a week-long event of teaching, research and debate around the state of the art in Machine Learning and Artificial Intelligence. The sessions were interactive and interesting. I was very much inspired by the keynote - Beyond Buzzwords: Innovation, Imagination, and Inequity in the 21st Century by Prof. Ruha Benjamin of African American Studies at the Princeton University.
In July 2019, I attended the second annual ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS 2019) in Accra, Ghana. The conference brought together researchers with broad expertise in artificial intelligence, human-computer interaction, networking, systems, speech and language processing, computer security, data mining, and computer vision. The focus was to apply this knowledge to solve the diverse problems in sustainable development, spanning health, accessibility, education, agriculture, financial services, job creation and governance.
In June 2019, I was able to attend summer school and workshop which was organised by Data Science Africa with the aim of training participants on Machine Learning and Data Science methods and provide an avenue for researchers to present their work and demonstrate the application of these techniques in solving problems in the African context. The event was held at Addis Ababa University in Ethiopia.
In early 2017, I was privileged to work as a researcher in the Dropwall project (by Rose Funja) which was among the winning project of the Data for Local Impact Innovation Challenge (DLIIC). The main focus of the project was to develop a tool that will help fighting dropout among secondary school girls. The findings from this project show a high rate of dropout among secondary school students particularly girls, and coincide with reports from other studies which show that school dropout is a big challenge in developing countries. On addressing this problem, machine learning techniques has gained much attention in recent years. However, most of the work has been carried out in developed countries, there are only a handful of studies conducted in developing countries on school dropout using machine learning techniques with the consideration of local context and data imbalance problem. This motivated me to continue working (in my PhD) on school dropout using machine learning.
In March, 2019, participants of the Deep Learning Indaba 2018 workshop (held in Stellenbosch, South Africa) from the Nelson Mandela African institution of Science and Technology (NM-AIST) organized a mini IndabaX event with the focus of highlighting fundamentals of Machine learning and Deep Learning research, applications and various potentials. The event also aimed in raising awareness and inspiring more participants from Tanzania to apply for Deep learning Indaba 2019 summer school which will held in Kenyatta university, Nairobi Kenya.