Abdulganiyu Jimoh

I am a passionate researcher and engineer specializing in deep learning (DL), machine learning (ML), artificial intelligence (AI), computer vision (CV), and their applications to real-world problems. I recently completed and serve as a Research Engineer and Assistant an at the School of Collective Intelligence, where I contributed to developing an AI-powered platform to enhance scientific collaboration and decision-making. My research interests span a variety of areas, including AI for: healthcare, engineering, environmental, water, and agriculture.

I hold a graduate degree from the School of Collective Intelligence, where I also contributed to groundbreaking research in the field of AI and multimodal data for healthcare and diagnosis. My work has been presented at prestigious international conferences such as MICCAI, NeurIPS, and the AAAI Conference on Artificial Intelligence. Additionally, I have had the privilege of being involved in several impactful projects in healthcare, including applying AI techniques for the diagnosis of breast cancer contributing to Africa.

As a dedicated advocate for data-driven decision-making, I am involved in several initiatives aimed at leveraging AI, deep learning, and machine learning for societal good. In addition to my research, I have mentored students and worked on various projects that address pressing global challenges, including food insecurity and the impact of COVID-19.

I have received numerous awards and grants, including those from the MICCAI Society, NeurIPS Conference, and Max Planck Society, and I am a recipient of prestigious scholarships like the CIFAR Inclusive AI Scholarship and the Excellence Cluster Science of Intelligence Grant from Technical University of Berlin.

I have actively contributed to the exchange of research advances in artificial intelligence and machine learning through various reviewing and volunteering roles. I have served as a reviewer for prestigious conferences and workshops, including the NeurIPS Workshops on Women in Machine Learning, Machine Learning and Compression, and Music Informatics & Machine Learning, as well as the ICLR Workshops on Machine Learning for Global Health and Time Series for Health. Additionally, I have reviewed publications for the Deep Learning Indaba Short Paper Track, the AMLD Africa 2024 Conference, and the African Industrial Engineering and Operations Management Conference 2025. My expertise has also been recognized in journal reviewing, including my recent review for Computing&AI Connect on the manuscript “Multimodal Sentence-Level Sentiment Analysis of Sign Language Using Deep Learning and Language Models.” These experiences have strengthened my ability to critically evaluate research, provide constructive feedback, and collaborate with diverse teams in the global AI and machine learning community.

I am committed to advancing scientific discovery through AI and am continually seeking to collaborate with other researchers and institutions to push the boundaries of knowledge and technology.