Call for reviewers for Indaba awards 2024

by rimallouche | Mar 13, 2024 | Annual Indaba, Awards, Blog

The Deep Learning Indaba 2024  has been announced officially ON since the start of the year ! The new edition will be taking place in Dakar, Senegal from the 1st to the 7th of September. 

With a new edition, this year’s awards has been announced with a new category this year ! 

  • The Kambule Doctoral Award
  • The Alele-Williams Masters Award
  • The Maathai Impact Award
  • The Anta Diop Award

As always, we are looking for brave individuals volunteering to take on the task of reviewing awards submissions and evaluating them! This role will be in support of the Awards committee! 

If you are willing to assist as a reviewer for one or more of the nominations, please provide your details in the form below by the 19th April, 2024.

Call for Reviewers

Following are more details about each award and the reviewing process. 

Calibrating your reviews

The most common question we receive from reviewers is: what standard should be used to assess nominations in the review. This is an important question since it recognises the impact of lasting historical disparities, maturity of the teaching, research and innovation environment across different countries, and resource limitations. We ask that you use your best judgement. 

Use a standard that both addresses excellence at the highest standard, and one that accounts for the impact the nomination can have in strengthening African machine learning, computational science and innovation in the current climate, in broad applications including but not limited to technical, societal,  environmental, and economic impacts. Please provide feedback that is as constructive and as detailed as you are able to offer. Provide a critique that helps both candidates and the awards committee understand where a nomination shines, or falls short. Without this type of feedback, positive growth will not be possible. In this way, you will help give the mentorship and guidance that will help all future candidates reach the highest levels of excellence possible.

About Cheikh Anta Diop early to mid career  Nomination Reviewers: 

What is the Cheikh Anta Diop early to mid career Award?

The Anta Diop Award recognises and encourages excellence in research, teaching and community service by early to mid-career academics and researchers at African universities, in any area of artificial intelligence, and computational and statistical sciences.

The Anta Diop Award celebrates African research excellence: its recipients are those that uphold Cheikh Anta Diop’s legacy as a multidisciplinary scientist and visionary intellectual.

The award will be presented at the annual Deep Learning Indaba, Senegal in September 2024. We welcome applications from faculty members themselves, or nominations from their colleagues, mentors, or department heads.

How were candidates nominated?

Candidates were either self-nominated or nominated by a third party. The application includes a 3-page outline covering the following: 

  • Community Engagement: Demonstrating the ability to support the broader African AI community, with a focus on the Deep Learning Indaba Community where applicable. 
  • Research Excellence: Showcasing impactful work and research excellence in computational and statistical sciences, various fields such as machine learning, deep learning, artificial intelligence, statistics, probability, data science, information theory, econometrics, optimization, statistical physics, biostatistics and bioinformatics, natural language processing, computer vision, computational neuroscience, and computational data science. 
  • Teaching Portfolio: Providing a teaching portfolio that illustrates how their research informs their teaching methodologies, 
  • Vision for the Future of AI in Africa: Articulating the candidate's vision for the future of AI in Africa and how they plan to contribute to its advancement.

How to assess a nomination

The application should be assessed based on:

  • The  significance of the candidate contributions to the AI Community
  • Research Excellence is crucial, highlighting the depth of their research and its impact in the field.
  • Evaluating teaching effectiveness and its contribution to the improvement of African institutions, students, or supervision.
  • Candidate's role in strengthening African machine learning and artificial intelligence as part of vision for the Future of AI in Africa.
  • The strength of the supporting letter

The review form asks for a 1-5 rating for each of the above items, as well as a longer-length answer (150-200 words) commenting more generally on the 3 pages portfolio and the candidate.

More information on eligibility and other application guidelines can be found here. 

About Kambule Doctoral & Alele-Williams Masters Nomination Reviewers:

What are the Kambule and Alele-Williams Awards?

The Thamsanqa Kambule and Grace Alele-Williams Awards recognise and encourage excellence in research and writing by doctoral and masters candidates, respectively, at African universities, in any area of computational and statistical sciences. 

The Kambule Doctoral Award celebrates African research excellence: its recipients are those that uphold Thamsanqa Kambule’s legacy as a defender of learning, a seeker of knowledge, and activist for equality. 

The Alele-William Masters Award celebrates African research excellence: its recipients are those that uphold Alele-Williams legacy as a mathematician, a tireless champion of women in science, and an educationalist who influenced modern mathematics curricula across Africa. 

How were candidates nominated?

Candidates were either self-nominated or nominated by a third party. The application includes the dissertation, examiners’ reports, a summary of the dissertation’s primary contributions to its field of research, and a supporting letter from a person in a position to comment on the candidate and the candidate’s work.

How to assess a nomination

The application should be assessed based on:

  • The dissertation’s technical depth
  • The dissertation’s contributions/significance to its field of research (whether in theory or practice)
  • Its quality of presentation - how well is the thesis communicated (clarity, succinctness, notation accuracy and consistency)
  • The thesis and candidate’s role in strengthening African machine learning and artificial intelligence
  • The strength of the supporting letter

The review form asks for a 1-5 rating for each of the above items, as well as a longer-length answer (150-200 words) commenting more generally on the thesis and the candidate.

More information on eligibility and other application guidelines can be found here (Kambule) and here (Alele-Williams). 

About Maathai Impact Nomination Reviewers:

What is the Maathai Impact Award?

The Wangari Maathai Impact Award encourages and recognises work by African innovators that shows impactful application (including but not limited to technical, societal,  environmental, and economic) of machine learning and artificial intelligence.

This award reinforces the legacy of Wangari Maathai in 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 award will be presented at the annual Deep Learning Indaba in Senegal in September 2024. We welcomed applications from individuals, teams and organisations themselves, or nominations by third parties.

How were candidates nominated?

Candidates were either self-nominated or nominated by a third party. The application includes a description of the impactful work being done, the people empowered by this work, and why the work is innovative. Two supporting letters from persons familiar with the work are also provided.

How to assess a nomination

Applications should be assessed based on:

  • The impactful work itself on concrete communities 
  • The breadth of impact they or their work has had in utilising technology to educate about, showcase, or preserve African culture, language and history
  • The work’s innovativeness and the extent to which it is grounded in African communities 
  • The individual/team’s role in empowering individuals and groups affected or involved with machine learning
  • The strength of the supporting letters

The review form asks for a 1-5 rating for each of the above items, as well as a longer-length answer (150-200 words) commenting more generally on the nominee and the work they are doing

More information on eligibility and other application guidelines can be found here. 


The awards will be presented at the annual Deep Learning Indaba, Dakar, Senegal in September 2024. We welcomed nominations from both students themselves, and their supervisors and mentors. The awards winners will be given the opportunity to travel to present  at the Deep Learning Indaba. ( nominations will be closed on the 21st of March )

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.