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Abigail Birago Adomako

Abigail Birago Adomako
  • Program
    MPhil Scientific Computing & Industrial Modeling
  • Graduating Class of
  • Research Interests
    Climate modelling
  • Dissertation(s)
  • Affiliate Institution
    African Institute of Mathematical Sciences, Rwanda
  • Degree Obtained
    MSc Mathematical Sciences for Climate Resilience
  • Email


Abigail Birago Adomako earned her BSc Mathematics degree from Kwame Nkrumah University of Science and Technology in Ghana in 2017. She went on to undertake her National Service in the same institution as a Teaching Assistant.

Abigail grew up with a grandfather who loved nature and served it. She thus developed a passion for the environment and loved volunteering with 'Friends of the Earth', a non-governmental organization in Ghana where her grandfather worked. She has been involved in several climate projects including, 'Climate change adaptation', and 'Poverty and Women subsistence farmers'. This influenced her to pursue a masters in 'Mathematical Sciences for Climate Resilience' at the African Institute of Mathematical Sciences (AIMS), Rwanda, from 2018 to 2019. At AIMS, she had many opportunities to, and developed her mathematical and physical problem solving skills whilst exploring her interest in the climate through being taught, and interacting with different professors.

In September 2019, she was privileged to join The National Institute for Mathematical Sciences, Ghana, as a postgraduate student, where she is currently undertaking studies in 'Scientific Computing and Industrial Modeling'.

Abigail is interested in climate modeling and using Mathematics to help better predict the behavior of our climate. She believes that the world needs trained specialists, who understand the culture and politics of their countries, if adverse climate change is to be halted or reversed. She also believes that having more reliable predictions would help immensely in the struggle against global warming, and that policy-makers and the powers that be are more likely to listen when recommendations are backed-up by data-science and other reliable techniques.

Research Summary