Dunstan Matekenya

Dunstan Matekenya#

Data Scientist#

Dr. Dunstan Matekenya is a consummate Data Scientist with over 10 years’ experience in both traditional statistics and modern machine learning methods. Currently, he works as a Data Scientist at the World Bank Group Headquarters in Washington DC. Prior to joining the WBG, Dunstan completed his PhD at the University of Tokyo in 2016. His PhD research focused on use of machine learning methods to explore insights from mobile phone data. Before re-orienting his career into Data Science, Dunstan earlier worked as a Statistician at the National Statistical Office in Malawi from 2007 up until 2017. While there he actively contributed to flagship projects such as the 2008 Malawi Population and Housing Census and also led the GIS unit. His passion includes contributing to modernization of official statistics in developing countries with use of alternative data sources such as mobile phone data as well improving capacity in Data Science.

Ben Stewart

Products and Projects#

  1. Geo-enhancement - Frameworks and best practices for adding geospatial variables to survey data.

  2. Health Accessibility Metrics - The focus of this project is to enhance GOST tools for performing physical accessibility analysis to healcare.

  3. Small Area Poverty Estimation - This project invloved using ML to provide poverty and child health indicator (stunting) at small areas in Madagascar.

  4. Big Data Analytics course- A data science course equiping participants with skills to process large scale datasets using Apache Spark in Python.

  5. Spatial Anonymization - This is a Python package offering functionality to enable generation of anonymized coordinates from raw confidential coordinates.

Selected Publications#

  1. D. Matekenya(2022, April 26). Educating the next generation of African data scientists: my experience teaching data science at the African institute for mathematical sciences in Rwanda. World Bank Group-Data Blog

  2. Milusheva, S., Lewin, A., Begazo Gomez, T., Matekenya, D., & Reid, K. (2021). Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countries. Data & Policy, 3, E20. doi:10.1017/dap.2021.10

  3. D. Matekenya et al. Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone. Policy Research Working Paper. Washington, D.C.: World Bank Group.

  4. D. Matekenya, M. Ito, R. Shibasaki, K. Sezaki, Enhancing Location Prediction with Big Data: Evidence from Dhaka. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp ‘16)

  5. D. Matekenya et al. Communal Parameters: A Study into Using Community-wide Learned Prediction Models in Individual Users. Proceedings of the Second International Conference on IoT in Urban Space. 2016.

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