Dr. Neema Mduma is a computer scientist and senior lecturer at the Nelson Mandela African Institution of Science and Technology (NM-AIST). Passionate about education and technology, she founded the BakiShule initiative which promotes STEM education to girls in secondary schools in Tanzania. Dr. Mduma is also a Principal Investigator on several projects that apply Artificial Intelligence (AI) and Machine Learning (ML) to solve challenges within agriculture, health, and education sectors.

She currently serves as a reviewer for scientific journals, workshops and conferences including the International Conference of Machine Learning (ICML), Association for Computing Machinery (ACM), Black in Artificial Intelligence (BAI) and Women in Machine Learning (WiML). Most recently, Dr. Mduma was appointed by the Minister of State, President’s Office Planning and Investment, as a member of the technical team that will draft the Tanzania Development Vision 2050.

• Machine Learning
• Advanced Data Mining
• Mobile Commerce
• Document Engineering and Information Architecture

• Global Young Academy (GYA)
• Mandela Washington Fellowship (MWF)
• Blacks in Artificial Intelligence (BAI)
• Women in Machine Learning (WiML)

• 100 Tanzanian Changemakers awarded by Serengeti Bytes
• Best Worker for Academic Staff awarded by the Nelson Mandela African Institution of Science and Technology (NM-AIST)
• 20 Young Talents in Sub-Saharan Africa awarded by L’Oréal-UNESCO for Women in Science
• WIMA STEM awarded by Women in Management Africa (WIMA)
• Excellence in Science and Technology Leadership awarded by Coca-Cola Kwanza Ltd
• International Women’s Day Recognition awarded by Puma Energy Tanzania
• Women in Science awarded by Next Einstein Forum
• Tanzania Sheroes awarded by The Launchpad and Embassy of Sweden
• Deep Learning Indaba conference, MIT Press Book award at Nairobi, Kenya
  
  

Ongoing

• Machine Learning Tools for Early Detection of Maize and Common Bean Diseases for Climate Change Adaptation in Tanzania funded by GrowFurther
• Deep Learning Tools for Early Detection of Diseases Affecting Common Bean and Irish Potato in the Southern Highlands Regions of Tanzania funded by IDRC/SIDA and TWAS – UNESCO through Organization for Women in Science for the Developing World (OWSD)

Phased Out

• Machine Learning Datasets for Crop Diseases: Imagery and Spectrometry Data funded by International Development Research Centre (IDRC) and Swedish International Development Cooperation Agency (SIDA) through Lacuna Fund in Agriculture
• Data Science Africa University Program funded by Data Science Africa (DSA)
• Deep Learning Techniques for Early Detection of Crop Diseases funded by IDRC/SIDA through African Centre for Technology Studies (ACTS)
  
  

• Orcid ID
• Google Scholar