Leeds-Africa Conference on
Data Science & Artificial Intelligence
2—6 October 2023
Future Africa, University of Pretoria
Future Africa, University of Pretoria
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You can download all of the plenary presentations here. You will require the password circulated to all attendees via email.
| 09:00 | Conference Opening |
| Sunil Maharaj (Vice Principal, University of Pretoria) | |
| 09:15 | Welcome from the Organisers |
| Richard Mann, John Ilee (University of Leeds) & Jack Radcliffe (University of Pretoria) | |
| 09:30 | KEYNOTE - Evolving Data Science |
| Renette Blignaut (University of the Western Cape, South Africa) | |
| 10:30 | Registration & Coffee |
| 11:45 | A Multi-Agent System to Collect and Sort Waste |
| Jose Quenum (Namibia University of Science and Technology) | |
| 12:15 | Lunch |
| 13:30 | Wildlife Ecology and Opportunities with Big Data Sciences: Local Experiences |
| Mark Keith (University of Pretoria, South Africa) | |
| 14:00 | Gaussian Processes for Spatial Anomaly Detection |
| Ciira Maina (Dedan Kimathi University of Technology, Kenya) | |
| 14:30 | DLIndaba: Redefining AI in Africa through the Power of the Community |
| Bruno Ssekiwere (Deep Learning Indaba, Uganda) | |
| 15:00 | Coffee |
| 15:45 | One Minute Flash Talks |
| Poster Presenters | |
|
Bonelwa Sidumo — Classification on species distribution models using ensemble machine learning techniques Fernando Ventura — Using autoencoders to search for exotic radio sources Shonisani Percy Masibigiri — Investigating the level of compliance and adherence to IT governance by universities in Gauteng Baitshephi Mashabe — Dynamic multivariate POT modelling: a case study of 4 major UK banks Moyahabo Rabothata — Network analysis and topic modelling to identify influential authors Kelebogile Phatsimo Malibala — Automatic detection and population estimation of fish species using YOLO models Olayile Ejekwu — Optimisation of one-pot fermentation process parameters using artificial neural networks Mubela Mutale — Extended haloes around UC HII regions in the MeerKAT galactic plane survey Laing Lourens — Can we use a transformer-based approach for vessel type classification? Lesedi Mabitsela — Representation of dynamic capital allocation John Bagiliko — A PICSA fit-for-purpose validation of satellite rainfall products in Zambia Joshua Attih — Global warming in Ghana's major cities based on statistical analysis |
| 16:15 | KEYNOTE - Biological Networks Inference and Assessment |
| Luisa Cutillo (University of Leeds, UK) | |
| 17:15 | Poster Viewing & Refreshments |
| 18:15 | Evening Meal at Future Africa |
| 09:00 | KEYNOTE - Understanding the Radio Environment Using Data Science at the South African Radio Astronomy Observatory |
| Nadeem Oozeer (South African Radio Astronomy Observatory) | |
| 10:00 | The Eduardo Mondlane Unity Efforts on the Introduction of the AI Initiatives: The Case of Mozambique |
| Claudio Paolo (University Eduardo Mondlane, Mozambique) | |
| 10:30 | Coffee |
| 11:15 | Patient Waiting Time Predictions in a Hospital |
| Daphne Machangara (Deep Learning Indaba, Zimbabwe) | |
| 11:45 | Experimental Mathematics: A Computational Algebra Pipeline for a Data Science Take on Mathematical Datasets |
| Pierre-Philippe Dechant (University of Leeds, UK) | |
| 12:15 | Lunch |
| 13:30 | KEYNOTE - Natural Language Processing and Connections to Grassroots AI on the African Continent |
| Vukosi Marivate (University of Pretoria, South Africa) | |
| 14:30 | Global Research & Development at the University of Leeds |
| Siddeequah Azmi (University of Leeds, UK) | |
| 15:00 | Coffee |
| 15:45 | The African Institute for Mathematical Sciences |
| Musa Hussein (AIMS) | |
| 16:15 | KEYNOTE - Learning to See with Neural Networks |
| Anna Bosman (University of Pretoria, South Africa) | |
| 17:15 | Poster Viewing & Refreshments |
| 18:15 | Evening Meal at Future Africa |
| 09:00 | KEYNOTE - Responsible AI and AI Ethics in Africa |
| Kingsley Owadara (Tech Hive Advisory, Nigeria) | |
| 10:00 | Facilitating Ocean Wave Predictions Using Machine Learning Techniques on Weather and Climate Timescales |
| Nefeli Makrygianni (Met Office, UK) | |
| 10:30 | Coffee |
| 11:15 | Search Landscape Analysis in Optimisation and Machine Learning |
| Katherine Malan (University of South Africa) | |
| 11:45 | Translating Astronomical Big Data into Patterns of Discovery |
| Kshitij Thorat (University of Pretoria, South Africa) | |
| 12:15 | Conference Photo |
| 12:30 | Lunch |
| 14:00 | Challenges of Field Data Collection for Machine Learning in a Developing Country Context |
| Moses Thiga (Kabarak University, Kenya) | |
| 14:30 | KEYNOTE - When ML Met Stats: 2.5 Examples of “Better Together” |
| George Aivaliotis (University of Leeds, UK) | |
| 15:30 | Coffee |
| 16:45 | Unconference Overview |
| John Ilee (University of Leeds, UK) | |
| 17:15 | Poster Viewing & Refreshments |
| 18:15 | Evening Meal at Future Africa |
| 09:00 | The Unconference Sessions |
| All participants | |
|
The final two days of the meeting were made available to participant-led meetings. We had several rooms to run parallel sessions on any topics relevant to the conference. These included of topics relevant to researchers across multiple disciplines, seminars/tutorials on particular techniques, or 'hackathons' designed to tackle specific questions with a range of new approaches. The sessions and their organisers are listed below:
Building & connecting the communities — All What happens when we run out of good data? — Arné Schreuder A.I. specific challenges in Africa — Bruno Ssekiwere, Richard Mann
Tutorial: scRNA-seq data analysis — Luisa Cutillo A.I. & machine learning techniques applied to astronomical imaging — John Ilee, Anna Bosman Data science & sustainability — Pierre Phillipe Dechant A.I. in finance — George Aivaliotis Discussion on funding sources and the African Open Science Platform — Tiamo Motshegwa |
| 18:00 | Conference Dinner - The Blue Crane Restaurant & Bar |
| 09:00 | The Unconference Sessions (continued) |
| All participants | |
|
The final day included sessions on:
A.I. in healthcare — Moses Thiga Tutorial: A deep dive into A.I. explainability — Olayile Ejekwu Bridging the gap between academia and industry — AIMS students Data science & education — Pierre Phillip Dechant Women in tech & A.I. — Moyahabo Rabothata A.I. in ecology & agriculture — Richard Mann |