IndabaX Uganda, Unity Road, Namugongo (2026)

02/04/2026

Thank you to all our speakers for being part of Deep Learning IndabaX Uganda 2026.

Your knowledge, time, and willingness to share made this experience meaningful for everyone involved🙏🏽

Deep Learning Indaba ACM - Association for Computing Machinery

30/03/2026

Thank You to Our Amazing Sponsors 🙏🏽

Deep Learning IndabaX Uganda 2026 would not have been possible without the incredible support of our partners.

Your commitment to advancing AI, supporting learning, and creating opportunities for researchers and practitioners continues to make a real difference.

Deep Learning Indaba ACM - Association for Computing Machinery Google

29/03/2026

IndabaX Spring School 2026 – Participant Group Project Presentations.

Group Six (Beyond the Ambulance) explored how Agent-Based Modeling can be used to bridge the critical "Golden Hour" gap on Ugandan highways.

Their project, Beyond the Ambulance, focuses on leveraging a socio-technical Digital Twin to formalize informal responder networks. With road traffic fatalities on the rise and formal EMS response times often exceeding 45 minutes, the team investigated how the spatial density of Boda-boda riders can be used for rapid trauma stabilization.

Using the Mesa and NetLogo frameworks, their system simulates microscopic traffic dynamics and physiological health decay to identify the exact "Biological Deadline" where intervention is most effective.

From their simulation, the team discovered a 22-minute critical failure threshold. They observed that empowering local Boda-boda stages with first-aid skills can flip a 92% mortality rate to significant survival, proving that in resource-constrained environments, proximity and skill are more powerful than traditional ambulance fleet expansion.

Their work highlights how AI can inform national policy, specifically through their proposed NIERF Framework and a real-time dispatch app designed to turn our greatest traffic challenge into Uganda’s most effective life-saving network.

Group members: Phillip Ssempeebwa Jovia Nakazibwe Allen Ayebare PUTSHU LUNGHE Samuel

ACM - Association for Computing Machinery Deep Learning Indaba

29/03/2026

IndabaX Spring School 2026 – Participant Group Project Presentations.

Group Five – AINA (Agricultural Intelligence for Native Africa) addressed a critical challenge in the region:
How can AI help reduce massive crop losses and improve food security for smallholder farmers?

AINA is a multimodal, agentic AI system designed to act as a digital agronomist, especially in regions where agricultural extension services are limited. Across countries like Uganda, Kenya, Democratic Republic of the Congo, and Burundi, many farmers lack timely guidance, leading to significant yield losses from diseases such as Banana Xanthomonas Wilt, Cassava Mosaic Disease, and Fall Armyworm.

The system combines computer vision and natural language processing, allowing it to analyze crop images and understand farmer descriptions in local languages. This enables accurate diagnosis and personalized recommendations, even in low-resource settings.

The team validated their solution using agent-based simulation (NetLogo). Their results showed that when farmers are equipped with AI-driven diagnosis and guidance, awareness increases and disease spread can be significantly reduced through earlier detection and timely intervention.

Designed for accessibility, AINA works on low-end devices and integrates with platforms like WhatsApp and USSD, ensuring farmers can access support without needing expensive data or smartphones.

Their work demonstrates how combining AI and simulation can support practical, scalable solutions that protect livelihoods and strengthen food systems across Africa.

Group members are: Danson githuka, Armand Bukaba, Waako Shadidu Ismail, Asther Irakaza, Nankya Zahara, Joseph Bill Awany, Famina Ayebare

Deep Learning Indaba ACM - Association for Computing Machinery

27/03/2026

IndabaX Spring School 2026 – Participant Group Project presentations.

Group Four - Kazi Skills explored how AI can support youth empowerment and economic inclusion in Uganda.

Their project focuses on individuals who are often left out of traditional systems, such as school dropouts and unemployed youth who have the desire to start businesses but lack access to resources, guidance, or funding.

The team proposed a phased approach, starting with Kampala and later expanding across the country, with the aim of reaching more underserved communities over time.

By identifying different user categories and their needs, the solution is designed to provide more targeted support, helping young people move from uncertainty to opportunity.

Their work highlights how AI can be used not just for efficiency, but to create pathways for

Group Members: Kasagga Gordon Kimera Seanice Nabasirye PrimroseTendo Sanyu Precious

27/03/2026

A deeply reflective talk from Professor Sennay Ghebreab on I and I and AI that looked at AI beyond the technical side and into its role in shaping society.

The talk explored AI as a kind of mirror, one that reflects our behaviors, values, and collective realities. But it also challenged us to go further, to use AI not just for reflection, but for introspection, imagination, and transformation.

It highlighted an important gap between the technical questions of how we build AI and the social questions of what values we embed and who benefits from it. These two sides are closely connected, yet often treated separately.

In the end, the message was clear: AI is not just about systems and data. It is about people, power, and the kind of society we choose to build.

27/03/2026

IndabaX Uganda Spring School 2026 – Participant Group Project Presentations.

Group Three – Brain Byte explored how AI can be used to tackle mental health challenges in Uganda.

Their project, Brain Byte, focuses on leveraging agentic AI to detect and respond to depression risk using social media behavior. With mental health cases on the rise and limited access to professional care, the team looked at how digital signals can support earlier intervention.

Using Natural Language Processing and Large Language Models, their system analyzes online activity to identify individuals at different risk levels and recommend context-based interventions.

From their simulation, the team observed that AI-driven detection and intervention can significantly reduce the number of high-risk individuals. Over time, more individuals transition toward a healthier state, showing the potential impact of early, data-driven support.

Their work highlights how AI can enable proactive mental health care, especially in environments where access and awareness remain key challenges.

Group Members: Angel Nagaba Bridgette Kyalimpa Nathaniel Liswa SSENYONGA YASIN Mjuni Abel Masota

Deep Learning Indaba ACM - Association for Computing Machinery

27/03/2026

The invited talk: Indigenous AI by Professor Frank Dignum at Deep Learning IndabaX Uganda 2026 was really insightful.

It highlighted on what it truly means to build AI for real communities.

A strong message from the session is that responsible AI starts with people. Before building any system, we need to understand how people live, their knowledge, and their context, not just rely on data or assumptions.

It was also a reminder that sometimes the most effective solutions are simple. Real impact comes from working with communities, building trust, and solving the right problem, not just applying technology.

Deep Learning Indaba ACM - Association for Computing Machinery

27/03/2026

IndabaX Uganda Spring School 2026 – Participant Group Project Presentations

Group Two (Phase Zero) explored a critical question in food security:
Is it more effective to respond to emergencies or prevent them before they happen?

By simulating 5,000 households with netlogo, the examines how early intervention can prevent vulnerable communities from slipping into severe food insecurity.

Instead of focusing only on emergency response, PhaseZero emphasizes acting earlier at the “stressed” stage, helping stop a full crisis before it happens - according to the simulation.

This creates a “policy testing environment” where different aid strategies can be evaluated before real-world implementation.

Their work demonstrates how simulation and AI can support more proactive and informed decision-making in humanitarian efforts.

Group Members: Irene Phoebe Akitwi, Namutebi Esther, Nakabuuka Regina Desire, Sandra Nakacwa

Deep Learning Indaba ACM - Association for Computing Machinery

27/03/2026

IndabaX Spring School 2026 – Group Participant Project Presentations.

Over the course of the week, participants at the IndabaX Spring School worked in teams to design and present solutions tackling real-world challenges using Agent Based Modeling.

Group One(Cervixity) explored an important public health question:
Can local-language digital health assistants accelerate early HPV screening among women living with HIV?

Their solution, Cervixity, is a localized NLP-powered chatbot designed to bridge the language gap in healthcare.

In Uganda, many women receive health information in English, yet are more comfortable communicating in local languages like Luganda and Acholi. This disconnect can affect trust and delay critical decisions such as seeking screening.

To evaluate their idea, the team built a simulation using NetLogo with 10,000 agents, comparing three scenarios: no intervention, English-only support, and a fully localized chatbot. The focus was to understand how language influences trust and how quickly patients move from awareness to screening.

Their work shows how AI, when designed with local context in mind, can support better health outcomes and contribute to national efforts like cervical cancer prevention.

Cervixity group memebers: Daphne Machangara, Jane Mutie, Annet Najjuma, Ruth Maina, Lucky Morgan, Caleb Huang, Mark Kalungi, Hassan Bahati Mukisa

Deep Learning Indaba ACM - Association for Computing Machinery

27/03/2026

Panel Discussion: AI for Local Impact: From Prototypes to Policy at Deep Learning IndabaX Uganda 2026 is on🔥🔥.

A really engaging conversation on what it takes to move AI from ideas to real-world impact.

One clear message is that building impactful AI requires stronger collaboration between researchers, industry, and communities. It’s not always the easiest path, but it’s the one that leads to meaningful solutions.

The discussion also highlighted the need for inclusive design from the start, continuous evaluation in real-world settings, and thoughtful regulation that supports innovation while ensuring responsibility.

At the core of it all is a simple idea: AI only matters if it works for people and within their context.

Deep Learning Indaba

Google

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