Edward Efui Salakpi


PhD Data Intensive Science; Satellite Earth Observation for Drought Modelling

University of Sussex

Could you tell us about your background? 

I hold a BSc in Agricultural Science from the University of Ghana and an MSc in Information Systems (data mining and information systems architecture) from the University of Reading, UK. Prior to starting my PhD in the UK, I worked at the West Africa Centre for Crop Improvement (WACCI) at the University of Ghana as an ICT Officer (Systems Administrator). 


Please tell us about your PhD and your research.

My PhD involves applying data-intensive techniques to satellite Earth Observation (Remote Sensing) datasets. My research explores information (Vegetation and Drought Indices) which is derived from satellite images, in order to study and understand pasture and rangeland dynamics in African pastoral communities. The overall goal is to determine variables (such as extreme weather) and their underlying causes to use their temporal information in a Dynamic (Temporal) Bayesian Network, allowing us to develop a drought early warning system. 


What are your plans for when you finish your PhD? 

After my PhD I plan to work in an industry that applies data science and machine learning to solve social problems, preferably in the area of satellite Earth Observation. I would like to explore the possibility of getting a grant to enable further research into drought and flood early warning systems in Africa. I also have some interest in teaching and would like to do that on a part-time basis.    











What started your interest in agriculture and data science? 

After my Master’s degree I developed a strong interest in data science and machine learning. I was intrigued by the fact that with the adequate amount of data and the right algorithm one can easily implement software solutions using models, without having to write several lines of computer code. This pushed me to explore how data science could be usefully applied in the field of agricultural science. My interest was further kindled when colleagues from the University of Ghana introduced me to some field experts from the College of Agriculture and Life Sciences (CALS) at Cornell University in Ithaca, USA. 


What would your dream job be, and where?

Aside from the analysis of big data and training of machine learning models I also have a lot of interest in the implementation side of things, so my dream job is to become a Machine Learning Solutions Architect. I would like to work for either Google AI or IBM, or become a Microsoft AI for Earth partner where I can help to implement AI solutions for sustainable agriculture.  

What accomplishment are you most proud of so far?

I am part of a team at the University of Sussex that works on using satellite Earth Observation data to build drought forecast models for pastoralist communities in Kenya and the East African Region. Our models have been accepted by some Kenyan government agencies and are currently being implemented, which is a big achievement. 

What advantages do you think there are for students with machine learning skills, particularly in Africa?

Data Science and Machine Learning are not new concepts, however recent advances in technology have enabled access to unlimited information and data. Students with an interest in this field can make use of many freely available internet resources such as Youtube or Udemy to learn about concepts, algorithms and how to code, using libraries like SciKit Learn and TensorFlow.  Aspiring African data scientists and students with skills in this field can easily develop models using these libraries alongside the available data, allowing them to solve key problems in their own communities. 


What have you enjoyed most about the UK while you're studying here?

I love visiting and seeing the historical sites and buildings, especially in England and Scotland.  I also enjoy meeting and interacting with people from various countries and backgrounds. 

Edward talking about his research at the 2019 Big Data Africa School, Cape Town (credit: SARAO)

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