{"id":1061,"date":"2026-07-12T20:49:48","date_gmt":"2026-07-12T20:49:48","guid":{"rendered":"https:\/\/deeplearningindaba.com\/2026\/?page_id=1061"},"modified":"2026-07-13T09:30:51","modified_gmt":"2026-07-13T09:30:51","slug":"programme-details","status":"publish","type":"page","link":"https:\/\/deeplearningindaba.com\/2026\/programme-details\/","title":{"rendered":"Programme Details"},"content":{"rendered":"<div class=\"et_pb_section_0 et_pb_section et_pb_fullwidth_section et_section_regular et_block_section\"><span class=\"et_pb_background_pattern\"><\/span>\n<section class=\"et_pb_fullwidth_header_0 et_pb_fullwidth_header et_pb_bg_layout_dark et_pb_text_align_left et_pb_module et_flex_module\"><div class=\"et_pb_fullwidth_header_container left\"><div class=\"header-content-container center\"><div class=\"header-content et_flex_module\"><h1 class=\"et_pb_module_header\"> Programme<\/h1><div class=\"et_pb_header_button_wrapper\"><\/div><\/div><\/div><\/div><div class=\"et_pb_fullwidth_header_overlay\"><\/div><div class=\"et_pb_fullwidth_header_scroll\"><\/div><\/section>\n<\/div>\n\n<div class=\"et_pb_section_1 et_pb_section et_section_regular et_flex_section preset--module--divi-section--default\">\n<div class=\"et_pb_row_0 et_pb_row et_flex_row\">\n<div class=\"et_pb_column_0 et_pb_column et-last-child et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_24_24 et_flex_column_24_24_tablet et_flex_column_24_24_phone\">\n<div class=\"et_pb_text_0 et_pb_text et_pb_bg_layout_light et_pb_module et_flex_module\"><div class=\"et_pb_text_inner\"><p>This year's Deep Learning Indaba promises a mix of inspiring keynotes, tutorials, skills sessions, and advanced workshops. Combined with plenty of time for networking, the Indaba promises to leave you with new skills and a larger network. Session venues are available on the Baobab app for registered attendees.<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"et_pb_section_2 et_pb_section et_section_regular et_flex_section preset--module--divi-section--default\">\n<div class=\"et_pb_row_1 et_pb_row et_flex_row\">\n<div class=\"et_pb_column_1 et_pb_column et-last-child et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_24_24 et_flex_column_24_24_tablet et_flex_column_24_24_phone\">\n\n\n\t\t<div class=\"bep-programme\" id=\"bep-1-fe7df8\" data-bep-config='{&quot;locale&quot;:&quot;en&quot;,&quot;deskMax&quot;:8,&quot;mobMax&quot;:3,&quot;showType&quot;:true,&quot;showTrack&quot;:true,&quot;timeFmt&quot;:&quot;24&quot;,&quot;dayPrefix&quot;:&quot;Day&quot;,&quot;emptyText&quot;:&quot;No sessions are available for this day yet.&quot;,&quot;i18n&quot;:{&quot;sessionType&quot;:&quot;Session type&quot;,&quot;track&quot;:&quot;Track&quot;,&quot;uncategorized&quot;:&quot;Uncategorized&quot;,&quot;noTrack&quot;:&quot;No track&quot;,&quot;details&quot;:&quot;View details&quot;,&quot;close&quot;:&quot;Close&quot;,&quot;speakers&quot;:&quot;Speakers&quot;,&quot;errorLoad&quot;:&quot;Something went wrong while displaying the programme.&quot;}}'>\n\t\t\t\n\t\t\t\t\t\t\t<script type=\"application\/json\" class=\"bep-data\">[{\"id\":27,\"title\":\"Breakfast\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Breakfast\",\"description\":\"\"}],\"session_type\":null,\"session_type_id\":null,\"start_time\":\"2026-08-02T06:30:00+01:00\",\"end_time\":\"2026-08-02T09:00:00+01:00\",\"sort_order\":0,\"speakers\":[],\"tracks\":[]},{\"id\":55,\"title\":\"Registration\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Registration\",\"description\":\"\"}],\"session_type\":null,\"session_type_id\":null,\"start_time\":\"2026-08-02T09:00:00+01:00\",\"end_time\":\"2026-08-02T13:30:00+01:00\",\"sort_order\":0,\"speakers\":[],\"tracks\":[]},{\"id\":29,\"title\":\"First-time Attendees Story Telling Mentorship Session\",\"description\":\"This session is designed primarily for first-time Indaba attendees, though all are welcome to join. Structured as an interactive Q\\u0026A, the session brings together a panel of returning Indaba attendees who will share their personal experiences, lessons learned, and practical advice for newcomers. Through open dialogue, participants will hear firsthand accounts of how past attendees navigated their own first Indaba, and gain insight into strategies for making the most of the week. As a concrete outcome, each participant is encouraged to leave the session with a personal list of actionable goals to pursue throughout the conference. This session offers a valuable opportunity to learn from the journeys of others and set intentions for a rewarding Indaba experience.\\n\",\"translations\":[{\"language\":\"en\",\"title\":\"First-time Attendees Story Telling Mentorship Session\",\"description\":\"This session is designed primarily for first-time Indaba attendees, though all are welcome to join. Structured as an interactive Q\\u0026A, the session brings together a panel of returning Indaba attendees who will share their personal experiences, lessons learned, and practical advice for newcomers. Through open dialogue, participants will hear firsthand accounts of how past attendees navigated their own first Indaba, and gain insight into strategies for making the most of the week. As a concrete outcome, each participant is encouraged to leave the session with a personal list of actionable goals to pursue throughout the conference. This session offers a valuable opportunity to learn from the journeys of others and set intentions for a rewarding Indaba experience.\\n\"}],\"session_type\":{\"id\":244,\"name\":\"Panel\"},\"session_type_id\":244,\"start_time\":\"2026-08-02T11:00:00+01:00\",\"end_time\":\"2026-08-02T12:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":30,\"name\":\"Lesego Makhafola\",\"photo_url\":null,\"bio\":null},{\"id\":31,\"name\":\"Yousra Farhani\",\"photo_url\":null,\"bio\":null},{\"id\":32,\"name\":\"Pegdwend\\u00e9 Nicolas Sawadogo\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":28,\"title\":\"Lunch\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Lunch\",\"description\":\"\"}],\"session_type\":null,\"session_type_id\":null,\"start_time\":\"2026-08-02T12:00:00+01:00\",\"end_time\":\"2026-08-02T13:30:00+01:00\",\"sort_order\":0,\"speakers\":[],\"tracks\":[]},{\"id\":31,\"title\":\"Introduction to Agentic AI: Building Conversational Agents with Persistent Memory\",\"description\":\"Agentic AI, systems that hold a conversation, remember context, and act over time, is moving from research into production. Yet most introductory material treats agents as stateless chatbots and ignores the memory that makes them useful. In this tutorial I give undergraduates from any ML background a practical, code-forward introduction to building conversational agents with persistent memory. We start from first principles: what an AI agent is, how conversational systems track state, and why a model\\u0027s short-term context window is not enough. We then build a working agent in Python from scratch and give it long-term memory using vector embeddings, retrieving relevant memories on demand. To make the lesson concrete, the agent we build is a griot, a storyteller that must remember an evolving folktale across turns and sessions. Everything runs on  small open models, locally, with no API key and no data leaving the machine. That keeps the session easy to run for everyone, and it doubles as a working demonstration of sovereign, self-hostable AI. Attendees leave able to build a memory-backed agent themselves and to reason about deploying and owning one responsibly.\\n\",\"translations\":[{\"language\":\"en\",\"title\":\"Introduction to Agentic AI: Building Conversational Agents with Persistent Memory\",\"description\":\"Agentic AI, systems that hold a conversation, remember context, and act over time, is moving from research into production. Yet most introductory material treats agents as stateless chatbots and ignores the memory that makes them useful. In this tutorial I give undergraduates from any ML background a practical, code-forward introduction to building conversational agents with persistent memory. We start from first principles: what an AI agent is, how conversational systems track state, and why a model\\u0027s short-term context window is not enough. We then build a working agent in Python from scratch and give it long-term memory using vector embeddings, retrieving relevant memories on demand. To make the lesson concrete, the agent we build is a griot, a storyteller that must remember an evolving folktale across turns and sessions. Everything runs on  small open models, locally, with no API key and no data leaving the machine. That keeps the session easy to run for everyone, and it doubles as a working demonstration of sovereign, self-hostable AI. Attendees leave able to build a memory-backed agent themselves and to reason about deploying and owning one responsibly.\\n\"},{\"language\":\"fr\",\"title\":\"Introduction \\u00e0 l\\u0026#39;IA agentique\\u00a0: Cr\\u00e9ation d\\u0026#39;agents conversationnels dot\\u00e9s d\\u0026#39;une m\\u00e9moire persistante\",\"description\":\"L\\u0026#39;IA agentique, ces syst\\u00e8mes capables de tenir une conversation, de se souvenir du contexte et d\\u0026#39;agir au fil du temps, passe de la recherche \\u00e0 la production. Pourtant, la plupart des ressources d\\u0026#39;introduction traitent les agents comme de simples chatbots sans \\u00e9tat et ignorent la m\\u00e9moire qui les rend utiles. Ce tutoriel propose aux \\u00e9tudiants de tous niveaux, quelle que soit leur formation en apprentissage automatique, une introduction pratique et orient\\u00e9e code \\u00e0 la cr\\u00e9ation d\\u0026#39;agents conversationnels dot\\u00e9s d\\u0026#39;une m\\u00e9moire persistante. Nous partons des principes fondamentaux\\u00a0: qu\\u0026#39;est-ce qu\\u0026#39;un agent d\\u0026#39;IA, comment les syst\\u00e8mes conversationnels g\\u00e8rent l\\u0026#39;\\u00e9tat et pourquoi la fen\\u00eatre de contexte \\u00e0 court terme d\\u0026#39;un mod\\u00e8le est insuffisante. Nous cr\\u00e9ons ensuite un agent fonctionnel en Python, en partant de z\\u00e9ro, et nous lui attribuons une m\\u00e9moire \\u00e0 long terme gr\\u00e2ce aux plongements vectoriels, permettant ainsi de r\\u00e9cup\\u00e9rer les souvenirs pertinents \\u00e0 la demande. Pour illustrer concr\\u00e8tement ce que nous cr\\u00e9ons, l\\u0026#39;agent que nous d\\u00e9veloppons est un griot, un conteur qui doit se souvenir d\\u0026#39;un conte populaire \\u00e9volutif au fil des \\u00e9changes et des sessions. Tout s\\u0026#39;ex\\u00e9cute sur de petits mod\\u00e8les ouverts, en local, sans cl\\u00e9 API et sans que les donn\\u00e9es ne quittent la machine. Cela simplifie le d\\u00e9ploiement pour tous et constitue une d\\u00e9monstration pratique d\\u0026#39;IA souveraine et auto-h\\u00e9berg\\u00e9e. Les participants repartent capables de construire eux-m\\u00eames un agent bas\\u00e9 sur la m\\u00e9moire et de r\\u00e9fl\\u00e9chir \\u00e0 son d\\u00e9ploiement et \\u00e0 sa gestion responsable.\"}],\"session_type\":{\"id\":249,\"name\":\"Tutorials\"},\"session_type_id\":249,\"start_time\":\"2026-08-02T13:30:00+01:00\",\"end_time\":\"2026-08-02T16:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":33,\"name\":\"David Agbolade\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":30,\"title\":\"The Math Behind Neural Networks - Pelumi Victor\",\"description\":\"This tutorial offers a deep dive into the mathematics of neural networks, the building blocks of contemporary AI. Starting from an intuitive understanding of \\u0027what it means for a machine to learn,\\u0027 the tutorial introduces essential concepts such as cost functions and gradient descent, before presenting the artificial neuron and the feedforward mechanism. The centrepiece is backpropagation: participants work through the gradients themselves, guided by slides that reveal the derivations progressively, ensuring active engagement rather than passive reception. The concepts of memoization and tensor dimensions are also addressed. By the end, participants will have a good understanding of how neural networks are trained. The tutorial closes with a live demo of TensorFlow Playground in which we  explore how architecture choices (depth, width, activation functions) shape the resulting decision boundaries. A follow-up skills session, \\u0022Code a Neural Network From Scratch\\u0022 is  a hands-on session that complements the theory.\\n\",\"translations\":[{\"language\":\"en\",\"title\":\"The Math Behind Neural Networks - Pelumi Victor\",\"description\":\"This tutorial offers a deep dive into the mathematics of neural networks, the building blocks of contemporary AI. Starting from an intuitive understanding of \\u0027what it means for a machine to learn,\\u0027 the tutorial introduces essential concepts such as cost functions and gradient descent, before presenting the artificial neuron and the feedforward mechanism. The centrepiece is backpropagation: participants work through the gradients themselves, guided by slides that reveal the derivations progressively, ensuring active engagement rather than passive reception. The concepts of memoization and tensor dimensions are also addressed. By the end, participants will have a good understanding of how neural networks are trained. The tutorial closes with a live demo of TensorFlow Playground in which we  explore how architecture choices (depth, width, activation functions) shape the resulting decision boundaries. A follow-up skills session, \\u0022Code a Neural Network From Scratch\\u0022 is  a hands-on session that complements the theory.\\n\"}],\"session_type\":{\"id\":249,\"name\":\"Tutorials\"},\"session_type_id\":249,\"start_time\":\"2026-08-02T13:30:00+01:00\",\"end_time\":\"2026-08-02T16:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":34,\"name\":\"Catherine Essuman\",\"photo_url\":null,\"bio\":null},{\"id\":35,\"name\":\"Pelumi Victor\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":32,\"title\":\"Understanding Model Alignment: Concepts and Practical Steps\",\"description\":\"As artificial intelligence systems grow more capable, ensuring they behave in ways consistent with human values and intentions\\u2014known as model alignment\\u2014has become a central concern in AI safety. This tutorial, presented at the Deep Learning Indaba, offers an accessible, hands-on introduction to the core concepts and practical techniques for aligning modern AI models. We begin by motivating the alignment problem through real-world examples of misaligned behaviour in deployed systems, illustrating how models can produce harmful, biased, or unintended outputs even when achieving strong benchmark performance. We then introduce foundational alignment concepts, including the distinction between outer and inner alignment, reward misspecification, and goal misgeneralization. The tutorial surveys the most widely adopted alignment techniques\\u2014reinforcement learning from human feedback (RLHF), direct preference optimisation (DPO), and constitutional AI\\u2014explaining each method\\u0027s intuition, workflow, and trade-offs in a manner accessible to participants with a working knowledge of deep learning. A significant portion of the session is dedicated to practical steps: we walk-through how-to curate preference datasets, fine-tune a language model using open-source alignment toolkits, design red-teaming exercises, and evaluate alignment using both automated and human evaluation protocols. We also address considerations especially relevant to the African AI community, including aligning models to diverse cultural values, handling low-resource languages, and building locally relevant evaluation benchmarks. The tutorial concludes by highlighting open challenges, such as scalable oversight and superalignment, and encourages participants to contribute to this rapidly evolving and globally important field.\\n\",\"translations\":[{\"language\":\"en\",\"title\":\"Understanding Model Alignment: Concepts and Practical Steps\",\"description\":\"As artificial intelligence systems grow more capable, ensuring they behave in ways consistent with human values and intentions\\u2014known as model alignment\\u2014has become a central concern in AI safety. This tutorial, presented at the Deep Learning Indaba, offers an accessible, hands-on introduction to the core concepts and practical techniques for aligning modern AI models. We begin by motivating the alignment problem through real-world examples of misaligned behaviour in deployed systems, illustrating how models can produce harmful, biased, or unintended outputs even when achieving strong benchmark performance. We then introduce foundational alignment concepts, including the distinction between outer and inner alignment, reward misspecification, and goal misgeneralization. The tutorial surveys the most widely adopted alignment techniques\\u2014reinforcement learning from human feedback (RLHF), direct preference optimisation (DPO), and constitutional AI\\u2014explaining each method\\u0027s intuition, workflow, and trade-offs in a manner accessible to participants with a working knowledge of deep learning. A significant portion of the session is dedicated to practical steps: we walk-through how-to curate preference datasets, fine-tune a language model using open-source alignment toolkits, design red-teaming exercises, and evaluate alignment using both automated and human evaluation protocols. We also address considerations especially relevant to the African AI community, including aligning models to diverse cultural values, handling low-resource languages, and building locally relevant evaluation benchmarks. The tutorial concludes by highlighting open challenges, such as scalable oversight and superalignment, and encourages participants to contribute to this rapidly evolving and globally important field.\\n\"}],\"session_type\":{\"id\":249,\"name\":\"Tutorials\"},\"session_type_id\":249,\"start_time\":\"2026-08-02T13:30:00+01:00\",\"end_time\":\"2026-08-02T16:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":36,\"name\":\"Michael Mollel\",\"photo_url\":null,\"bio\":null},{\"id\":37,\"name\":\"Winnie Mangeni\",\"photo_url\":null,\"bio\":null},{\"id\":38,\"name\":\"Innocent Charles\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":105,\"title\":\"Building Faster Transformers with Triton: A Hands-On Introduction to GPU Kernel Programming\",\"description\":\"Modern Transformer models are increasingly powerful, but their real-world usefulness depends heavily on how efficiently they can be trained and served on GPU hardware. This tutorial introduces participants to GPU kernel programming with Triton through the practical goal of building faster Transformer components from the ground up. We will begin with the performance bottlenecks that commonly appear in Transformer workloads, including memory bandwidth limits, attention computation, operator overhead, and inefficient data movement. Participants will then learn the core ideas behind GPU execution, tensor layouts, parallelism, tiling, and memory hierarchy before implementing simple Triton kernels.\\nThe hands-on section will focus on writing, benchmarking, and optimizing kernels for common Transformer operations, gradually building toward attention-related kernels used in modern LLM systems. By the end of the tutorial, participants will understand how custom GPU kernels can improve Transformer performance, how to reason about throughput and memory efficiency, and how to begin applying Triton to their own machine learning systems research and engineering projects.\\n\",\"translations\":[{\"language\":\"en\",\"title\":\"Building Faster Transformers with Triton: A Hands-On Introduction to GPU Kernel Programming\",\"description\":\"Modern Transformer models are increasingly powerful, but their real-world usefulness depends heavily on how efficiently they can be trained and served on GPU hardware. This tutorial introduces participants to GPU kernel programming with Triton through the practical goal of building faster Transformer components from the ground up. We will begin with the performance bottlenecks that commonly appear in Transformer workloads, including memory bandwidth limits, attention computation, operator overhead, and inefficient data movement. Participants will then learn the core ideas behind GPU execution, tensor layouts, parallelism, tiling, and memory hierarchy before implementing simple Triton kernels.\\nThe hands-on section will focus on writing, benchmarking, and optimizing kernels for common Transformer operations, gradually building toward attention-related kernels used in modern LLM systems. By the end of the tutorial, participants will understand how custom GPU kernels can improve Transformer performance, how to reason about throughput and memory efficiency, and how to begin applying Triton to their own machine learning systems research and engineering projects.\\n\"},{\"language\":\"fr\",\"title\":\"Cr\\u00e9ation de transformateurs plus rapides avec Triton\\u00a0: une introduction pratique \\u00e0 la programmation du noyau GPU\",\"description\":\"Les mod\\u00e8les Transformer modernes sont de plus en plus puissants, mais leur utilit\\u00e9 concr\\u00e8te d\\u00e9pend fortement de l\\u0026#39;efficacit\\u00e9 de leur entra\\u00eenement et de leur ex\\u00e9cution sur GPU. Ce tutoriel initie les participants \\u00e0 la programmation de noyaux GPU avec Triton, avec pour objectif pratique la cr\\u00e9ation de composants Transformer plus rapides. Nous commencerons par les goulots d\\u0026#39;\\u00e9tranglement de performance fr\\u00e9quemment rencontr\\u00e9s dans les charges de travail Transformer, notamment les limites de bande passante m\\u00e9moire, le calcul de l\\u0026#39;attention, la surcharge des op\\u00e9rateurs et les transferts de donn\\u00e9es inefficaces. Les participants d\\u00e9couvriront ensuite les concepts fondamentaux de l\\u0026#39;ex\\u00e9cution sur GPU, l\\u0026#39;organisation des tenseurs, le parall\\u00e9lisme, le pavage et la hi\\u00e9rarchie m\\u00e9moire avant d\\u0026#39;impl\\u00e9menter des noyaux Triton simples. La partie pratique sera consacr\\u00e9e \\u00e0 l\\u0026#39;\\u00e9criture, \\u00e0 l\\u0026#39;\\u00e9valuation des performances et \\u00e0 l\\u0026#39;optimisation de noyaux pour les op\\u00e9rations Transformer courantes, pour ensuite aborder progressivement les noyaux li\\u00e9s \\u00e0 l\\u0026#39;attention utilis\\u00e9s dans les syst\\u00e8mes LLM modernes. \\u00c0 l\\u0026#39;issue de ce tutoriel, les participants comprendront comment des noyaux GPU personnalis\\u00e9s peuvent am\\u00e9liorer les performances des Transformer, comment analyser le d\\u00e9bit et l\\u0026#39;efficacit\\u00e9 de la m\\u00e9moire, et comment appliquer Triton \\u00e0 leurs propres projets de recherche et d\\u0026#39;ing\\u00e9nierie en apprentissage automatique.\"}],\"session_type\":{\"id\":249,\"name\":\"Tutorials\"},\"session_type_id\":249,\"start_time\":\"2026-08-02T13:30:00+01:00\",\"end_time\":\"2026-08-02T16:00:00+01:00\",\"sort_order\":3,\"speakers\":[{\"id\":46,\"name\":\"Ayomide Odumakinde\",\"photo_url\":null,\"bio\":null},{\"id\":47,\"name\":\"Katherine Oluwadarasimi Olowookere\",\"photo_url\":null,\"bio\":null},{\"id\":48,\"name\":\"Adeniyi Mayokun\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":56,\"title\":\"Coffee Break \\u0026 Networking\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Coffee Break \\u0026 Networking\",\"description\":\"\"}],\"session_type\":null,\"session_type_id\":null,\"start_time\":\"2026-08-02T16:00:00+01:00\",\"end_time\":\"2026-08-02T16:30:00+01:00\",\"sort_order\":0,\"speakers\":[],\"tracks\":[]},{\"id\":59,\"title\":\"Mastering the Scientific Narrative: A Practical Guide to Reading and Writing High-Impact AI Research Papers\",\"description\":\"To achieve \\u0022Sovereign Intelligence,\\u0022 African researchers must not only build innovative models but also master the art of global scientific communication. Many young scholars in Africa possess groundbreaking ideas but face barriers in effectively deconstructing dense AI literature or structuring their findings for top-tier venues. This Skill Session is designed to bridge this gap.\\nWe will move beyond theory to provide a systematic \\u0022how-to\\u0022 for both consuming and producing research. The session will cover the \\u00223-pass\\u0022 reading strategy to extract core contributions from complex papers quickly. On the writing side, it will demystify the scientific storytelling process\\u2014from crafting a compelling Abstract to presenting results that meet international standards. By equipping participants with these reusable competencies, we empower African AI agency, ensuring that our local innovations are documented, recognized, and integrated into the global frontier of AI. This session targets undergraduate and early-graduate students, providing them with a concrete blueprint for scientific success.\",\"translations\":[{\"language\":\"en\",\"title\":\"Mastering the Scientific Narrative: A Practical Guide to Reading and Writing High-Impact AI Research Papers\",\"description\":\"To achieve \\u0022Sovereign Intelligence,\\u0022 African researchers must not only build innovative models but also master the art of global scientific communication. Many young scholars in Africa possess groundbreaking ideas but face barriers in effectively deconstructing dense AI literature or structuring their findings for top-tier venues. This Skill Session is designed to bridge this gap.\\nWe will move beyond theory to provide a systematic \\u0022how-to\\u0022 for both consuming and producing research. The session will cover the \\u00223-pass\\u0022 reading strategy to extract core contributions from complex papers quickly. On the writing side, it will demystify the scientific storytelling process\\u2014from crafting a compelling Abstract to presenting results that meet international standards. By equipping participants with these reusable competencies, we empower African AI agency, ensuring that our local innovations are documented, recognized, and integrated into the global frontier of AI. This session targets undergraduate and early-graduate students, providing them with a concrete blueprint for scientific success.\"}],\"session_type\":{\"id\":250,\"name\":\"Skills Session\"},\"session_type_id\":250,\"start_time\":\"2026-08-02T16:30:00+01:00\",\"end_time\":\"2026-08-02T18:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":42,\"name\":\"Castro Gb\\u00eam\\u00eamali HOUNMENOU\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":57,\"title\":\"Doing ML research from the margins\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Doing ML research from the margins\",\"description\":\"\"}],\"session_type\":{\"id\":250,\"name\":\"Skills Session\"},\"session_type_id\":250,\"start_time\":\"2026-08-02T16:30:00+01:00\",\"end_time\":\"2026-08-02T18:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":39,\"name\":\"Steven Kolawole\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":93,\"title\":\"Adversarial Thinking in AI Systems: Understanding and Mitigating Real-World Model Failures\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Adversarial Thinking in AI Systems: Understanding and Mitigating Real-World Model Failures\",\"description\":\"\"}],\"session_type\":{\"id\":250,\"name\":\"Skills Session\"},\"session_type_id\":250,\"start_time\":\"2026-08-02T16:30:00+01:00\",\"end_time\":\"2026-08-02T18:00:00+01:00\",\"sort_order\":0,\"speakers\":[{\"id\":40,\"name\":\"Manar Hamed\",\"photo_url\":null,\"bio\":null}],\"tracks\":[]},{\"id\":58,\"title\":\"Building On-Device AI Applications\",\"description\":null,\"translations\":[{\"language\":\"en\",\"title\":\"Building On-Device AI Applications\",\"description\":\"\"}],\"session_type\":{\"id\":250,\"name\":\"Skills 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