{"id":1021599,"date":"2024-05-15T11:48:54","date_gmt":"2024-05-15T18:48:54","guid":{"rendered":"https:\/\/research.codeghost.online\/en-us\/research\/?post_type=msr-research-lab&p=1021599"},"modified":"2026-08-10T06:35:27","modified_gmt":"2026-08-10T13:35:27","slug":"microsoft-research-lab-africa-nairobi","status":"publish","type":"msr-research-lab","link":"https:\/\/research.codeghost.online\/en-us\/research\/lab\/microsoft-research-lab-africa-nairobi\/","title":{"rendered":"Microsoft Research Lab – Africa, Nairobi"},"content":{"rendered":"
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Microsoft Research Lab – Africa, Nairobi<\/h1>\n\n\n\n

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Building globally equitable AI models and tools<\/h2>\n\n\n\n
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At Microsoft Research Africa, Nairobi, we are driven by a mission to understand, build, and deploy innovative cloud and AI technologies for Africa and the world. Our work is grounded in a multidisciplinary approach that combines our diverse skill set and practices with community-based, human-centred methods to design AI to make work more seamless and enable people to grow their skills and knowledge. As AI unlocks new possibilities for billions, we are focused on ensuring that the models and tools we build are globally equitable, inclusive, and meaningful for the communities they serve. In doing so, we both advance AI and ensure it can work for everyone, everywhere.<\/p>\n\t\t\t\t\t\t\t<\/blockquote>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

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Our Focus Areas<\/h2>\n\n\n\n
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Advancing Robust Multimodal Foundation Models for Africa<\/h3>\n<\/div>\n\n\n\n
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Building and evaluating culturally-aware AI models for global deployment<\/h3>\n<\/div>\n\n\n\n
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Developing methods for human-centred and LLM-based benchmarking and evaluation<\/h3>\n<\/div>\n\n\n\n
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Driving community-led development of AI systems across the African ecosystem<\/h3>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n
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    At Microsoft Research Africa, we partner with universities and research institutions across Africa and globally to co-create AI systems that are grounded in local contexts and shaped through shared knowledge, research, and innovation.<\/p>\n\n\n\n

    Current Initiatives<\/h3>\n\n\n\n\n\n

    Past Initiatives<\/h3>\n\n\n\n

    In 2022, MSR Africa awarded Microsoft PhD fellowships to three students at African universities, marking the programme\u2019s first cohort on the continent: Dorcas Gicuku (opens in new tab)<\/span><\/a>, Olalekan Joseph Akintande (opens in new tab)<\/span><\/a>, and Sofia Bourhim (opens in new tab)<\/span><\/a>. In 2023, a further three fellows were selected from across Africa, including Tezira Wanyana (opens in new tab)<\/span><\/a>, Naoya Muramatsu (opens in new tab)<\/span><\/a>, and Hasnae Zerouaoui (opens in new tab)<\/span><\/a>. Throughout their fellowships, Microsoft worked closely with each researcher on areas of shared interest, supporting collaborative, impactful research.<\/p>\n\n\n\n

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    August 2026 Seminar:<\/h4>\n\n\n\n

    AI, Radio Astronomy, and the Search for Life Beyond Earth<\/h2>\n\n\n\n

    Radio astronomy combines astronomy, physics, signal processing, engineering, and computing to study the Universe through radio-frequency observations. As modern radio telescopes generate vast volumes of data, AI is becoming an essential tool for advancing the search for extraterrestrial intelligence (SETI). <\/p>\n\n\n\n

    In this talk Ramiro Caisse Saide will present a multimodal deep-learning approach for technosignature detection using observations from the Breakthrough Listen backend at MeerKAT. He will share investigations into whether combining spectrograms with in-phase and quadrature (I\/Q) signal representations can improve signal detection and classification, particularly in low signal-to-noise environments where traditional spectral features become less distinct.<\/p>\n\n\n\n

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    Speaker Bio:<\/strong><\/p>\n\n\n\n

    Ramiro Caisse Saide is a PhD student in Astrophysics at the University of Manchester, researching Earth radio signatures, deep learning, and the search for extraterrestrial intelligence (SETI). A former researcher and telescope operator at the SETI Institute, he has developed large-scale analysis pipelines for spectral data from the Allen Telescope Array and presented his work at NASA Technosignature Seminar, PSETI conferences at Penn State University, and meetings at the University of Oxford. He also builds AI and data platforms for scientific and industrial applications including Matilho (opens in new tab)<\/span><\/a>, a tool designed to support data labeling and dataset preparation for computer vision projectsand currently. He currently serves as Scientific Director of the Mozambique Astronomy Society.<\/p>\n\n\n\n

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