Humus Labs is a forward-thinking AI research laboratory advancing the frontiers of artificial intelligence—with particular focus on the communities most often absent from the technology's design and governance. Like humus enriches soil to support life, we enrich the technological landscape with foundational research that enables breakthrough innovations.
At Humus Labs, we conduct cutting-edge research that shapes the future of technology. Our work spans the full arc from fundamental theory to real-world deployment—always with an eye toward ethical development, equitable access, and genuine societal impact.
We believe the most important problems in AI are not the most commercially attractive ones. The communities facing the most acute challenges—across sub-Saharan Africa, South Asia, Latin America, and the Pacific—are also those most underrepresented in the research and governance institutions shaping AI's trajectory. Changing that is not a side project for us. It is the project.
Our interdisciplinary team brings together machine learning researchers, ethicists, policymakers, domain scientists, and community practitioners. We publish openly, collaborate widely, and resist the temptation to mistake benchmark progress for real-world impact.
Rigorous methodology and peer-reviewed research that advances the field and withstands scrutiny. We do not trade accuracy for speed.
Cross-disciplinary partnerships that spark breakthrough discoveries. The hardest problems in AI are not solved by AI researchers alone.
Research that centers communities typically absent from AI's design: speakers of low-resource languages, smallholder farmers, patients in under-resourced health systems.
Careful attention to fairness, transparency, accountability, and the cultural contexts in which AI systems actually operate—not just those they were designed for.
We invest in the theory and methods that make applied AI possible—neural architectures, optimization, learning theory, representation learning. Foundational work is the only reliable path to genuine progress.
We close the gap between research performance and real-world deployment. The systems that matter most—healthcare AI in low-resource settings, agricultural AI for smallholder farmers, language technology for underserved communities—require as much engineering judgment as scientific insight.
We engage directly with AI governance debates at the national, continental, and global level. Research institutions that stay silent on how AI is regulated cede that territory to actors with narrower interests. We don't stay silent.
Six interconnected domains where we concentrate our investigative effort.
Advancing neural architectures, optimization techniques, and learning paradigms to create more capable and efficient AI systems.
Learn More →Developing systems that understand, generate, and reason with human language across diverse contexts and modalities.
Learn More →Creating intelligent systems that can perceive, interpret, and understand visual information from the world around us.
Learn More →Building intelligent agents that can interact with and navigate the physical world safely and effectively.
Learn More →Ensuring AI systems are developed responsibly, with careful attention to fairness, transparency, and societal impact.
Learn More →Designing intuitive interfaces and interaction paradigms that enable seamless collaboration between humans and AI.
Learn More →Brought together by a shared conviction that the questions AI raises are too important to leave to any single discipline, institution, or geography.
AI researcher and computational scientist focused on real estate data analytics, intelligent systems, and the governance of AI in emerging economies. Lead author on AI applications in property management and digital infrastructure.
Specializes in the intersection of AI policy and human rights in developing-country contexts. Advises government agencies on algorithmic accountability frameworks and contributes to AU Continental AI Strategy implementation dialogues.
Develops low-resource NLP methods for Amharic, Tigrinya, and Oromo. Based in Addis Ababa, she contributes to the Masakhane African Languages Hub and co-organizes the AfricaNLP workshop series at ACL and EMNLP.
Works on the engineering problems that separate AI from AI at scale: compute infrastructure, model compression, offline-first deployment, and systems built for the connectivity and power conditions across Southern Africa.
Whether you are a researcher, funder, policymaker, or practitioner working on AI in the Global South—we want to hear from you.