Dr. Washington Okori

Dr. Washington Okori


Dr. Washington Okori is a lecturer in the School of Computing and Engineering.

He is also a digital transformation and service delivery leader. He has provided leadership at the senior management level for over fifteen years giving strategic direction to many corporate organizations across countries and supervised Digital transformation in Corporate Organizations, Government Ministries and Departments; and Industry Apex Organizations.

He is an advisor on data privacy and protection and has carried out extensive research in the application of computing to support sustainable development as evident from the journals and conference proceedings he has authored.

Dr. Washington Okori is an experienced and skilled graduate trainer in the computing discipline who has taught, mentored and supervised students pursuing advanced degrees in different Universities.

  1. Lukyamuzi J. Ngubiri and W. OkoriPolarity and Similarity Measures Towards Classifying an Article on Food Insecurity. International Journal of Technology and Management, vol. 5, No. 2, pp 1-10, 2020.
  2. Lukyamuzi J. Ngubiri and W. Okori. "Towards Ensemble Learning for Tracking Food Insecurity From News Articles," International Journal of System Dynamics Applications (IJSDA), IGI Global, Vol. 9(4), pp 129-142, 2020.
  3. Lukyamuzi J. Ngubiri and W. Okori. “Topic Based Machine Learning Summarizer”. In Proceedings of the IEEE International Smart Cities Conference (ISC2), pp 288-291, 2019.
  4. Lukyamuzi J. Ngubiri and W. Okori. “Tracking Food Insecurity from Tweets Using Data Mining Techniques”. In Proceedings of the IEEE/ACM Symposium on Software Engineering in Africa (SEiA), pp. 27-34, 2018.
  5. W. Okori and J. Obua, “Integrated Risk Components in Data Modeling for Risk Databases”, Journal of Computer Science and Information Technology, Vol. 5, No. 1, pp. 15-25, 2017.
  6. Lukyamuzi J. Ngubiri and W. Okori. “Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis”, International Journal of System Dynamics Applications (IJSDA), Vol.4, Issue. 4, pp. 1-16, 2015.
  7. W. Okori and J. Obua, “Computational Learning in Climate Change Adaptation Support,” Journal of Computational Intelligence and Electronic Systems, Vol. 3, No. 3, pp. 220-224, 2014.
  8. W. Okori and J. Obua, “Contribution of Prior Knowledge to Probabilistic Prediction of Famine,” Applied Artificial Intelligence, Vol. 27, No. 10, pp 913-923, 2013.
  9. W. Okori and J. Obua, “Supervised learning algorithms for famine prediction,” Applied Artificial Intelligence, Vol. 25, No. 9, pp. 822–835, 2011.
  10. W. Okori and J. Obua, “Machine Learning Classification Technique for Famine Prediction,”In Proceedings of The World Congress on Engineering, vol. 2, pp. 991–996, 2011.
  11. W. Okori, J. Obua, and V. Baryamureeba, “Logit Analysis of Socioeconomic Factors Influencing Famine in Uganda,” Journal of Disaster Research, Vol. 5, No. 2, pp. 208–215, 2010.
  12. A. Quinn, W. Okori, and A. Gidudu, “Increased-specificity famine prediction using satellite observation data,” In Proceedings of the First ACM Symposium on Computing for Development, 2010.
  13. Mwebaze, W. Okori and J. A. Quinn, “Causal Structure Learning for Famine Prediction,” In Proceedings of AAAI Spring Symposium: Artificial Intelligence for Development, Technical Report SS-10-01, 2010.
  14. W. Okori, J. Obua, and V. Baryamureeba, “Famine Disaster Causes and Management Based on Local Community’s Perception in Northern Uganda,” Research Journal of Social Sciences, Vol. 4, pp. 21–32, 2009.

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