Ideathon @ DLI 2022

by arannen | Aug 17, 2022 | Annual Indaba, Blog, News

The Indaba is a learning opportunity, but also an opportunity to connect different actors in the continent around Machine Learning and related topics. This proposal took shape after hearing about the successful experiences from a sister summer school in a different part of the world to build research bridges across the borders. We propose therefore to mimic their experience, and get researchers from different countries to come up with research projects.

How would it work?

The Ideathon is a competition where candidates propose research projects. The only requirements about the project are: 

  • It needs to include a machine/deep learning component. This component does not need to be at the core of the project. 
  • It needs to include researchers from at least two African countries. International collaborators are welcomed as long as at least two members of the team are based in two different countries on the continent. 

A jury then selects the best 3 projects. The winners will receive support from the Deep Learning Indaba to turn their idea into an actual project in two forms:

  • A seed funding 
  • Mentorship from experienced researchers 

Schedule

  • Monday 22/08: Official start 
  • Monday 22/08 to Thursday 25/08: Meet potential partners, form the idea and prepare a 3 minutes pitch. There won’t be any dedicated time to work on this, but there will be many networking opportunities for you to meet potential partners and prepare your idea and pitch. 
  • Wednesday 24/08: Mentorship to refine the project and the pitch.
  • Thursday 25/08: Present the projects  - 3 minutes per team, subject to change in function of the number of participants
  • Friday 26/08: Project selection and announcement

The project

The project can be anything, as long as it has a machine/deep learning component. It can be a pure theoretical research pushing the understanding of one particular topic that you are passionate about, as it can be a pure applied research taking recent successful advances into an application that you find particularly motivating, or anywhere in between. The most important requirement here is that the proposal is made by at least two people from at least two countries in Africa. 

The pitch

This competition is happening over 4 days. Given the short period of time, we are not expecting full research proposals. On Thursday evening, you will have 3 minutes to convince the jury that your idea is interesting to pursue and that it is realisable. You will have complete freedom on how to shape this presentation. Here is a non-exhaustive list of questions that you could use for inspiration: 

  • What do you propose to study and what is your motivation? What could be the impact of this research locally? For the ML community in general? 
  • How much would you expect such a research to cost? What are the needed resources for it? Do you need additional/external collaborations? Are there structures that could help you with this? 
  • What makes your idea realisable? What are the challenges you could face? What are the factors that could facilitate the realisation of this idea?

The judging

Your projects will be evaluated by a diverse jury. The judging will be through a standardised scoring form, focusing on three axes: 

  • Motivation and Impact: Is the idea original? Is it well motivated and sound?If conducted successfully, would it have a significant impact? 
  • Feasibility: Is the idea likely to lead to a successful project? Have the team considered potential challenges? 
  • Team: geographic, gender, language and other dimensions of diversity.

More details on the judging procedure will be shared at the start of the Indaba week.

The DLI support

We will support you during this new experiment in different ways, both during the indaba week and after the Indaba week.


During the Indaba week: On Wednesday afternoon, we will offer you mentorship to refine your project definition and pitches. As soon as you meet your potential partner, make sure to register your interest using this form by Tuesday EOD (Tunisia time). This would allow us to plan for the mentorship session and for the presentations accordingly, and to match you as much as possible with a mentor with an expertise that is relevant to your project. The mentors’ role would be to give constructive feedback to improve your proposal. This feedback can be technical (e.g. pointing to some recent work that can help you improve your proposal) or non-technical (e.g. give you advice on how to improve your presentation).

 

After the Indaba week: The Indaba will support the 5 winning projects not only with  seed funding, but also through our mentorship program in order to help turn your wonderful ideas into actual projects. The goal of this initial period is to turn the idea and the pitch into a well-structured research proposal, with initial studies and empirical investigations that can allow the winning teams to apply for other sources of funding: 

  • Financial support: Each of the winning teams will receive cloud compute credits of a minimum value of 500 USD.  
  • Mentorship: The Deep Learning Indaba has now a well established mentorship programme which supports short-term, transactional mentorship. This platform could be leveraged to connect you with experienced researchers to help you progress in your project definition and give you feedback on the research questions or obstacles you are facing at various points in your project. This mentorship can also have a non-technical nature such as helping you with structuring your research proposal or helping setting up a formal collaboration across the borders. 

After one year from DLI2022, the 5 winning teams would be able to independently run their projects. They will also be invited to present their initial results and progress at future Indaba meetings.  

Moreover, the Deep Learning Indaba’s support will continue beyond this initial phase. If a project is particularly successful (e.g. accepted to a top-tier conference or journal, or has a high societal impact), the Indaba will offer the team support to publicise their work at a wider scope.

Possible challenges and potential impact

Putting together an interesting and realisable research project is always a challenge. It is even more so when it brings together diverse parties. We are aware that these proposals could be faced with bureaucratic and political realities that makes their realisation 

harder or less likely. Our hope is that we can work together to overcome these kinds of challenges, effectively building bridges not only for the competing teams to benefit from but all the machine learning communities in their countries. If successful, this experiment can go one step further towards building a more solid and connected machine learning community on our continent, in addition to the technical contributions it can yield. 

Cheikh Anta Diop Award

The Cheikh Anta Diop Award recognises, encourages, and celebrates excellence in research, teaching, and community service by early- to mid-career academics and researchers at African universities in any area of artificial intelligence, as well as computational and statistical sciences. Its recipients are those who uphold Cheikh Anta Diop’s legacy as a multidisciplinary scientist and visionary intellectual.

The DLI 2026 Cheikh Anta Diop Award is awarded to:

Winner: Prof Omneya Attallah 

Country: Egypt

Affiliation: Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Essence of the Award: Prof. Omneya Attallah says that this award reminds her that every late night, sacrifice, and challenge she has faced as a working mother and African researcher has been worthwhile. It proves that perseverance truly pays off.

Professor Omneya Attallah is recognised for a career that integrates research excellence, teaching, and community service into a single mission. At AASTMT, she has built a research programme spanning breast cancer detection, paediatric epilepsy diagnosis, wearable biosensing, explainable AI, and CanSense, a breathomics AI system for non-invasive breast cancer screening developed on locally collected Egyptian clinical data in partnership with Egyptian clinicians. As a teacher she designed the curriculum for a new undergraduate Biomedical Engineering programme. She has supervised 13 postgraduate researchers to completion, the majority of them women. Through HERBioLab, WeBios, and her IEEE and editorial activities, she has built laboratories, curricula, training pathways, and partnerships that are shaping the next generation of African biomedical AI researchers

Wangari Maathai Impact Award

The Wangari Maathai Impact Award recognises and celebrates work by African innovators, thinkers, and advocates who demonstrate impactful work, including, but not limited to, technical, societal, environmental, and economic domains, around machine learning and artificial intelligence. This award reinforces the legacy of Wangari Maathai by acknowledging the capacity of individuals to be a positive force for change: by recognising ideas and initiatives that demonstrate that each of us, no matter how small, can make a difference.

The DLI 2026 Wangari Maathai Impact Award is awarded to:

Winner: Dr Hellina Hailu Nigatu

Country: Ethiopia

Affiliation: Artificial Intelligence Accountability Lab (AIAL) at Trinity College Dublin

Essence of the Award: Dr Hellina Hailu Nigatu says that having read Wangari Maathai's autobiography, Unbowed, it is a personal privilege to be recognized with an award in her name. She wants to give gratitude to her mentees on the project who carried the majority of the work. This recognition is as much, if not more, a fruit of their effort and hard work. She hopes this will also inspire more grassroots ventures in the African AI landscape that foster meaningful community engagement and participation.

Dr Hellina Hailu Nigatu is recognised for her work addressing gender bias in machine translation across three Ethiopian languages: Amharic, Afan Oromo, and Tigrinya. Rather than adapting English benchmarks, she built evaluation frameworks and datasets directly within African languages, preserving cultural nuances that translation from English cannot capture. Key to this work is the Yeswa Stories dataset, centered on African women's narratives and lived experiences. Seven Ethiopian female students participated as paid researchers, benefiting from funding, compute access, and substantive mentorship throughout. The project produced open-release benchmark datasets now available to the broader African NLP community. Her mentorship has been described by those she supervised as transformative, not nominal.

The DLI 2026 Wangari Maathai Impact Award runner-up is: 

Runner-up: Abdel-aziz Harane Abounounou 

Country: Chad

Affiliation: Chad AI Network

Essence of the Award: Abdel-aziz Harane says that he is deeply honored by this award. This recognition reflects his constant obsession: solving Chad's real problems through AI and software engineering. It renews his commitment to the Chad AI Network's mission of sustainable, locally driven transformation.

Abdel-aziz Harane Abounounou is recognised for his work building the first natural language processing tools for Chad's indigenous languages, a country with 123 languages yet only two represented in existing digital tools. Rather than waiting for a foreign lab to take interest, he set out to build the entire pipeline himself, working directly with the communities who speak these languages. The Kalam-na project anchors this work, collecting audio and text data across 16 dialects of the Sara language family. Native speakers contribute their own voices through Kalam-na Voice, a platform built to remove technical barriers to participation. The project has gathered over 68,000 transcribed audio clips and 72,000 aligned texts, laying the groundwork for open ASR, TTS, and MT models. He also founded the Chad AI Network, now over 200 members strong, and ran two editions of IndabaX Chad. This work reaches an estimated 4 million speakers of Chadian languages currently shut out of the digital world.

Thamsanqa Kambule Doctoral Award

The Thamsanqa Kambule Doctoral Award recognises, encourages, and celebrates excellence in research and writing by doctoral candidates at African universities in any area of computational and statistical sciences. Its recipients are those who uphold Thamsanqa Kambule’s legacy as a defender of learning, a seeker of knowledge, and an activist for equality.

The DLI 2026 Thamsanqa Kambule Doctoral Award is awarded to:

Winner: Dr Everlyn Chimoto 

Country: Kenya

Affiliation: Lelapa AI & Riara University

Essence of the Award: “Receiving this award is deeply meaningful because it recognises not only the years of work invested in this thesis, but also the support of my supervisor, collaborators, family, and the African NLP community that has continually inspired and challenged me. To me, it also recognises the importance of pursuing research motivated by the needs of underrepresented language communities. I believe this work demonstrates that data-centric approaches can make meaningful progress in low-resource settings while remaining relevant to many other machine learning problems. I am encouraged to continue pursuing research that advances knowledge while creating opportunities for more languages and more people to participate in language technology,” says Dr Everlyn Chimoto.

Dr Everlyn Chimoto is recognised for a doctoral thesis on sentence alignment for the Marama dialect of Luhya, CAT data pruning which achieves 92% of full-data performance at 50% of the data with no additional compute cost, GrammaMT which delivers over 12 BLEU point improvements for endangered languages from as few as 21 grammatical examples, and COMET-QE active learning for efficient data selection. Together these form a framework immediately usable by African NLP practitioners working under real compute and data constraints. Her work is centred on improving model performance to make AI systems more inclusive for African language communities.

The DLI 2026 Thamsanqa Kambule Doctoral Award runner-up is:

Runner-up: Dr Devon Jarvis 

Country: South Africa

Affiliation: University of the Witwatersrand

Essence of the Award: “I started learning ML while volunteering at the Indaba in 2017. To be recognised nine years later is gratifying and reflects the impact this community has had on my career,” says Dr Devon Jarvis.

Dr Devon Jarvis is recognised for his doctoral work developing a theoretical framework for controlled semantic cognition using deep and nonlinear neural networks to explain how the human brain flexibly applies learned semantic concepts to new contexts. Rather than treating machine learning purely as an engineering tool, he built tractable theoretical models that shed light on the principles of human cognition itself. His dissertation extends deep linear network theory to explain systematic generalisation, introduces a novel framework (the Rectified Linear Network) to model ReLU network training dynamics, and shows that these networks can capture all six classical properties of controlled semantic cognition while still falling short of systematic generalisation. He then proposes a meta-learning model, the Meta-GDLN, as a path toward closing that gap. He has since remained on the continent to establish the CAandL Lab, one of Africa's first computational neuroscience labs.

Grace Alele-Williams Masters Award

The Grace Alele-Williams Masters Award recognises and celebrates excellence in research and writing by master's candidates at African universities in any area of computational and statistical sciences. Its recipients are those who uphold Grace Alele-Williams’ legacy as a defender of learning, champion of academic excellence, and activist for access to education.

The DLI 2026 Grace Alele-Williams Masters Award is awarded to:

Winner: Akinbobola Adegboyega

Country: Nigeria

Affiliation: Olabisi Onabanjo University

Essence of the Award: According to Akinbobola Adegboyega, “The Alele-Williams Award is a reminder that hard work, curiosity, and dedication matter, and it inspires me to keep making meaningful contributions for societal benefit.”

Akinbobola Adegboyega is recognised for a Master's thesis that introduces affective fidelity as a new evaluation lens for machine translation in low-resource tonal languages, reframing emotion as a core measure of translation faithfulness rather than a secondary concern. His work targets Yorùbá, a language representing just 0.008% of Common Crawl training data. He produced a culturally validated bilingual emotion dataset of over 200 annotated paragraph pairs verified by native speakers, created a reproducible benchmarking framework evaluating four major language models, and identified six distinct failure modes specific to tonal low-resource language translation. The dataset has been released openly, contributing usable infrastructure to the African NLP community.

The DLI 2026 Grace Alele-Williams Masters Award runner-up is:

Runner-up: Imen Habibi 

Country: Tunisia

Affiliation: LARIA, ENSI, University of Manouba, Tunisia

Essence of the Award: “This recognition is the reward for five years of dedication and perseverance. It honors my supervisors' support and inspires me to continue pursuing research with confidence,” says Imen Habibi.

Imen Habibi is recognised for her master's thesis designing a drone-based inspection system that integrates optimized UAV path planning with AI-powered imaging for industrial applications. She developed spiral and zigzag trajectory strategies for nuclear facility inspection (validated through 2D and 3D simulations) alongside a CNN-based anomaly detection pipeline for PV solar panels achieving 91% accuracy. Her work bridges trajectory optimization and intelligent image analysis into a unified system applied to real industrial use cases, improving inspection reliability, precision, and efficiency.