PRESS RELEASE – DLI 2024

by rimallouche | Aug 17, 2024 | Annual Indaba, Blog, Media

Deep Learning Indaba 2024: Africa's biggest AI Community Gathers in Dakar, Senegal

Deep Learning Indaba 2023, Accra, Ghana

Dakar, Senegal (August, 2024) – The Deep Learning Indaba (DLI), Africa's premier machine learning and artificial intelligence (AI) event, returns for its 6th edition, promising an inspiring and collaborative experience for the continent's AI community. The event will be held from September 1st to 7th at Amadou Mahtar Mbow University (UAM) in Dakar, Senegal, comprising keynotes, tutorials, workshops, panel discussions, and paper presentations for a community of 600+ participants travelling from across Africa and beyond.

The DLI is a critical platform for addressing the unique challenges and opportunities facing AI development in Africa. Its mission is to empower Africans to become not just consumers of AI, but active contributors and leaders in its advancement.

Highlights of the 2024 Deep Learning Indaba:

  • Keynote Speakers: 
    • Samy Bengio, Senior Director of AI and Machine Learning Research at Apple
    • Caglar Gulcehre, Professor and Lead of CLAIRE lab at EPFL
    • Judy Gichoya, associate professor in the Department of Radiology and Imaging Sciences at Emory University School of Medicine
    • Isabel Valera, Professor on Machine Learning at the Department of Computer Science of Saarland University (Saarbrücken, Germany)
  • Key Themes:
    • Responsible AI
    • Robot Learning for Africa
    • Empowering African Voices in AI
    • Climate-Resilient AI Solutions
    • Data Science for Health in Africa
  • Workshops: 
    • Focused sessions for in-depth discussions and collaboration on specialised technical topics.
  • Diverse Participation:
    • Over 600 attendees, including researchers, students, industry professionals, startups, and policymakers from across Africa and beyond.
  • Inclusive Environment: 
    • This edition will continue to prioritise inclusivity and diversity, with efforts to ensure representation from across the African continent and beyond. Through scholarships, mentorship programs, and community engagement initiatives, the organisers aim to create a welcoming and inclusive environment for all participants.

Supporting Africa's AI Growth

The Deep Learning Indaba began in 2017, organised by a group of friends and collaborators who were able to envision the future potential of AI and the role that the African continent would be able to play in shaping this future. Fast forward to 2024, the Deep Learning Indaba is officially a registered charity with over 60 dedicated organisers helping to bring its vision to reality.

Through participation in the Deep Learning Indaba, individuals are amassed into the DLI community:  a community of passionate researchers and practitioners, aiming to foster learning, collaboration, and innovation in the field of AI across the African continent. This is continuously exemplified through new research collaborations, the birth of new research communities, innovation projects being seed funded through DLI and opportunities for career progress.

The Deep Learning Indaba is more than just a conference; it's a movement to strengthen Africa's AI ecosystem. It builds in collaboration with other pan-African communities which are working towards the same and is strengthened through international partnerships which often go above and beyond fiscal sponsorship. The event fosters the exchange of knowledge, the formation of new research collaborations, and the development of local AI talent.

We execute our mission through three principal programmes: 

  • the annual Deep Learning Indaba event
  • the IndabaX local chapters which are run in a distributed manner across 47 African countries, ensuring wide reach into the continent and continued ambassadorship across Africa.
  • Excellence in Research and Community Awards that recognise the efforts of individual contributors

These programmes aim to build a sustainable pan-African community of AI expertise, create local leadership in AI in every country across the continent, and recognise excellence in research and application of AI technologies, respectively.

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Indaba’s 2024 General Chairs: Emily Muller, Daouda Tandiang Djiba and Elizabeth Benson:

"The Deep Learning Indaba 2024 marks a pivotal moment in our journey. As we mature and refine our focus, we are thrilled to convene in Dakar, a city that embodies the dynamism and potential of Africa's AI ecosystem. Senegal's vibrant IndabaX and Galsen AI communities, robust partnerships with AI Hub Senegal, and commitment to a national AI strategy perfectly align with our vision for a more collaborative, inclusive, and impactful Indaba experience. We're excited to celebrate our collective achievements and further empower African voices in AI, together.” 

Professor Ibrahima Cissé, Rector of Amadou Mahtar Mbow University:

"By hosting the Deep Learning Indaba, Amadou Mahtar Mbow University (UAM) reaffirms its commitment to accelerating research, innovation, and community service for all Africans. This event is a unique opportunity to contribute to the advancement of artificial intelligence on the continent and to promote diversity of talent in scientific fields," 

Samy Bengio, Senior Director of AI and Machine Learning Research at Apple: 

“I look forward to participating in the upcoming Deep Learning Indaba in Dakar. Machine Learning has a growing impact on society and we should make sure everyone gets to understand and contribute to its latest advances so that it benefits everyone. My presentation will focus on problems involving advanced reasoning capabilities which will remain hard, despite current scaling approaches and hype.”

Christine Fages, Ambassador Extraordinary and Plenipotentiary of the French Republic to the Republic of Senegal: 

“More than an economic and technological revolution, artificial intelligence carries the potential for a profound paradigm shift in our societies. By strengthening the development of scientific communities and building bridges between research and innovation across the African continent, the Deep learning Indaba is an essential event for imagining a sustainable and inclusive impact of artificial intelligence  France is proud to stand alongside the African artificial intelligence community, with which we share the conviction that artificial intelligence will be a powerful driver of economic development. We have been working in this direction with the vibrant Senegalese artificial intelligence ecosystem, which is full of talented entrepreneurs, and researchers.”

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About the Deep Learning Indaba:

The Deep Learning Indaba is a grassroots movement. It is a culmination of the collective efforts of people across the continent, all working together towards a goal larger than ourselves: to strengthen and empower the African AI community, to make sure Africans are contributors, shapers and owners of the coming advances in machine learning. The organisation is made up of volunteers of teams who organise events, coordinate activities, cry for a community’s losses and celebrate each other’s successes. 

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Media contact: comms@deeplearningindaba.com

French Version

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.