Ronald Wesonga

Researcher
About Me

A passionate statistician, researcher & specialist in statistical computing

Ronald Wesonga holds Ph.D. in Statistics and is a professional statistician with vast knowledge, skills and experience gained over the years through collaboration networks with other experts all over the world. He is keen on developing a hybrid between statistical theory & computational theory with applications. His current focus is on estimable biases in generalized linear models and misclassification problems with two Ph.D. students currently involved under his supervision. He heads the data science analytics lab (DSAL) in the Department of Statistics, a central research group used to collaborate on various datacentric statistical problems for informed decision-making to support international sustainable development goals (SDGs).


He has also got experience in the development and management of online database systems and management of information systems including the European Union Satellite Information and communication technologies related to Weather and Climate data. His interests lay in developing methods that link statistics, computing and sustainable development by providing real solutions through data modeling. His PhD research is focused on aspects of modeling using stochastic optimization techniques and since then many innovations have been implemented at Entebbe International Airport, his case study.


Dr. Ronald Wesonga has authored and widely published in international referred journals as indicated online at researhgate.net and scholar.google.com. He dynamically continues to be actively present in the research arena developing research that present solutions to the real world problems.


Career: Besides research work, Dr. Wesonga has taught and developed curricula development for both undergraduate and postgraduate degree programmes. Some of the courses taught include; statistical methods, sampling, probability theory, stochastic models, statistical computing, data mining, computer programming, management of information systems, data structures and algorithms among others for degree programmes leading to award of Bachelor of Statistics, Master of Statistics and Ph.D.


Honours: For his excellent work at the then, Institute of Statistics and Applied Economics, Makerere University, Dr. Ronald Wesonga was honored with a scholarship to study his Ph.D. in Statistics.


Dr. Ronald Wesonga has represented his country on a number of occasions, including; 58th World Statistics Congress of the International Statistical Institute (ISI), Dublin, Ireland (2011): I represented School of Statistics and Planning, Makerere University, Kampala and participated in a number of conference workshops and meetings.


57th World Statistics Congress of the International Statistics Institute (ISI), Durban, South Africa (2009): Statistics: Our Past, Present and Future: I Presented a Paper: Stochastic optimization model for monitoring millennium development goals: case of HIV/AIDS.


International Statistical Conference by South African Statistical Association (SASA), Pretoria, South Africa (2008): I Presented a paper: Stochastic optimization model for monitoring millennium development goals: case of HIV/AIDS.


3rd International Conference on Agricultural Statistics, Beijing China (2007): I Presented a Paper: Stochastic Optimization Models for Agricultural Management Using Remote Sensing Technologies.


International Civil Aviation Organization (ICAO) – MET Workshop (Co-jointly with the 15th Session of the WMO Commission for Aeronautical MET (CAeM) Including Technical Conference; ICAO Headquarters, Montreal, Canada (2014): I was the Uganda Country Representative.


11th EUMETSAT User Forum in Africa, Johannesburg, South Africa (2014): I was the Uganda Country Representative. Decisions and discussions were focused on exploitation of the European Satellite Data for the benefit of African countries.


10th EUMETSAT User Forum in Africa, Addis Ababa, Ethiopia (2012): I was the Uganda Country Representative. Decisions and discussions were focused on exploitation of the European Satellite Data for the benefit of African countries.


Biography

Education

  • PhD Statistics

    Makerere University
    2006-2011

    Ronald Wesonga Developed Stochastic Optimization Models for Air Traffic Flow Management Under the Supervision of Prof. Peter Jehopio, Prof. Xavier Mugisha & Prof. Venancius Baryamureeba. His work culminated in the modernization of aviation meteorology at the only international airport in Uganda.

  • Master of Statistics

    Makerere University
    1997-1999

    Ronald Wesonga developed a Computerized Package for Parameter Estimation under Systematic Sampling Theory under the supervision of Prof. Kathuria and Prof. P.J Jehopio.

  • BSc Statistics

    Makerere University
    1991-1995

    Ronald Wesonga worked of statistical learning project that developed a system for Students’ Results Management and Analysis System under Dr. Mulira.

Experience

  • Associate Professor

    Oman
    2016-2024

    Department of Statistics, College Of Science, Sultan Qaboos University

  • Visiting Professor

    Oman
    2014-2016

    Department of Mathematics & Statistics, College Of Science, Sultan Qaboos Univerity

  • Senior Lecturer

    Makerere University
    2011-2016

    Department of Planning and Applied Statistics, School of Statistics and Planning, Makerere University

  • Lecturer

    Makerere University
    2000-2011

    Department of Planning and Applied Statistics, School of Statistics and Planning, Makerere University

  • Lecturer

    Islamic University In Uganda
    1995-1999

    Department of Computer Science, Islamic University In Uganda

Personal Skills

Time Management90%
Effeciency70%
Intigrity80%

Computational Languages

  • C++

  • Php

  • MATLAB

  • Others

Service

Never compromise with quality

Publications

Publications

  • Guwatudde, D., Mutungi,

    Guwatudde, D., Mutungi, G., Wesonga, R., Kajjura, R., Kasule, H., Muwonge, J., Ssenono, V., and Bahendeka, S. K. (2015). The Epidemiology of Hypertension in Uganda: Findings from the National Non-Communicable Diseases Risk Factor Survey. PloS one, 10(9), e0138991.
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  • Wesonga R, Nabugoomu F.

    Wesonga R, Nabugoomu F., and Masimbi B., (2014) Airline Delay Time Series Differentials: Autoregressive Integrated Moving Average Model. International Journal of Aviation Systems, Operations and Training (IJASOT), 1(2), 64-76.
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  • Wesonga R, Owino A

    Wesonga R, Owino A., Ssekiboobo A. , Atuhaire L., Jehopio Peter (2015) Health and Human Rights: a statistical measurement framework using household survey data in Uganda, BMC International Health and Human Rights 05/2015; 15(1):11.
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  • Wesonga R (2015) Airport utility

    Wesonga R (2015) Airport utility stochastic optimization models for air traffic flow management, European Journal of Operational Research, ELSEVIER
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  • Owino A. Y., Wesonga R. and Nabugoomu F

    Owino A. Y., Wesonga R. and Nabugoomu F (2014) Determining Food Insecurity: an application of the Rasch Model to household survey data in Uganda, International Journal of Food Science, Hindawi Publishers,
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  • Wesonga R, Nabugoomu F., Owino A., Atuhaire L

    Wesonga R, Nabugoomu F., Owino A., Atuhaire L., Ssekiboobo A., Mugisha X., Ntozi J., Makumbi T., Jehopio P. and Ocaya B. (2014) On statistical definition of free and fair election: bivariate normal distribution model, International Journal of Mathematical Research, 2014, 3(5): 49-62.

  • Owino A. Y., Atuhaire L. K., Wesonga R

    Owino A. Y., Atuhaire L. K., Wesonga R., Nabugoomu F., and Muwanga-Zaake E. S. K. (2014) Logit Models for Household Food Insecurity Classification, American Journal of Theoretical and Applied Statistics, vol. 3, No.2, pp. 49-54.
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  • Owino A. Y., Atuhaire L. K., Wesonga R

    Owino A. Y., Atuhaire L. K., Wesonga R., Nabugoomu F., and Muwanga-Zaake E. S. K. (2014) Determining Factors that Influence Household Food Insecurity in Uganda: A case study of Tororo and Busia districts, International Journal of Sciences: Basic and Applied Research, vol. 14, No.1, pp. 394-404

  • Mbazira M., Wesonga R, and Nabugoomu F

    Mbazira M., Wesonga R, and Nabugoomu F. (2014) Survival Factors of Burkitts Lymphoma Patients at Discharge: The Case of St. Mary’s Hospital Lacor in Northern Uganda, American Journal of Health Research, vol. 2, pp. 9-14.
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  • Wesonga R. and Nabugoomu F. (2014)

    Wesonga R. and Nabugoomu F. (2014) Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda, American Journal of Theoretical and Applied Statistics, vol. 3, pp. 1-5.
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  • Wesonga R., Nabugoomu F., Jehopio P

    Wesonga R., Nabugoomu F., Jehopio P. and Mugisha, X (2013) Modelling Airport Efficiency With Distributions Of The Inefficient Error Term: An Application Of Time Series Data For Aircraft Departure Delay, International Journal of Sciences: Basic and Applied Research, 12, No.1. pp. 103-114.

  • Wesonga R, Nabugoomu F., and Masimbi B.

    Wesonga R, Nabugoomu F., and Masimbi B., (2013) Assessing Aircraft Timeliness Variations By Major Airlines: Passenger Travel Practice In Uganda,” International Journal of Sciences: Basic and Applied Research, 11, No.1, pp. 75-83.

  • Wesonga, R., Nabugoomu, F., & Jehopio, P. (2012). Parameterized framework for the analysis of probabilities of aircraft delay at an airport. Journal of Air Transport Management, 23, 1-4.

    The study analyses ground delays and air holding at Entebbe International Airport over five years. Daily probabilities for aircraft departure and arrival delays at are generated for each. The mean probabilities of delay for ground delays and air holding at 50% delay threshold levels are 0.94 and 0.82 that fall to 0.49 and 0.36 when 60% delay threshold levels are used. Simulations are performance for delay threshold levels to monitor for the trends of the daily probabilities for the study period. The general conclusion is that a parameter-based framework is best suited to determine the probability of aircraft delay at an airport.
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  • Wesonga, R., Nabugoomu, F., Ababneh, F., & Owino, A. (2019). Simulation of time series wind speed at an international airport. Simulation, 95(2), 171-184.

    The sporadic and unstable nature of wind speed renders it very difficult to predict accurately to serve various decisions, such as safety in the air traffic flow and reliable power generation system. In this study we assessed the autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models on the wind speed time series problem. Data on wind speed and minimum and maximum temperatures were evaluated. Wind speed was established to follow a time series that fluctuated around ARIMA (0,1,1) and ARIMA (1,1,1). The optimal ANN model was established at 10 hidden neurons. The performance indices considered all indicated that the ANN wind speed model was superior to the ARIMA model. Wind speed prediction accuracy can be improved to secure the safety of air traffic flow as well support the implementation of a reliable and secure power generation system at the airport.
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Blog

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Contact Me

  • Call Me

    +968-93559568
  • Address

    Department of Statistics, College of Science, Sultan Qaboos University, Muscat, Oman.
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Research

Research Interests

  • Deriving multi-factor model

    Deriving multi-factor models for predicting airline delays case study: Entebbe International Airport by Masimbi Brian (2010/HD15/1001U)

  • An efficient analysis of survey data using double sampling design

    An efficient analysis of survey data using double sampling design: a computerized approach by Nabuduwa Ketty (2012/HD06/584U)

  • A web-based clinical malaria diagnosis system using data mining

    A web-based clinical malaria diagnosis system using data mining techniques, the rule-based classification algorithm by Bbosa Francis Fuller (2012/HD06/581U)

  • A system for Implementation and evaluation of continuous assessments

    A system for Implementation and evaluation of continuous assessments in secondary schools: case of Seeta High School by Olal Daniel (2012/HD06/597U)

  • Academic decision support system

    Academic decision support system based on determinants of university students’ performance: case of Kyambogo University

  • Factors that influence tourism demand

    Factors that influence tourism demand in Uganda by Epiaka William

  • Survival of Burkitts Lymphoma patients in Northern Uganda

    Survival of Burkitts Lymphoma patients in Northern Uganda: Admissions at St. Mary’s Hospital Lacor by Mbazira Mike (2011/HD06/4025U)

  • Modeling the effect of ART use on survival of HIV and AIDS

    Modeling the effect of ART use on survival of HIV and AIDS patients in Rural Uganda: A case study of Kasese District by Bwambale Kyamakya Moses (2009/HD15/16063U)

  • Economic growth in the three traditional East African Countries

    Economic growth in the three traditional East African Countries: A panel data analysis approach by Ssebaggala Godfrey (2010/HD/646U)

  • Impact of foreign aid on government consumption

    Impact of foreign aid on government consumption in Uganda by Basala Martin (2010/HD15/1103U)

  • Reduced proportions of Malaria Hospitalizations

    Reduced proportions of Malaria Hospitalizations: a comparison of longitudinal and cross-sectional groups of children of five years and below in Tororo district by Okiring Jaffer (2011/HD06/4020U)

  • Simulation of time series wind speed at an international airport

    The sporadic and unstable nature of wind speed renders it very difficult to predict accurately to serve various decisions, such as safety in the air traffic flow and reliable power generation system. In this study we assessed the autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models on the wind speed time series problem. Data on wind speed and minimum and maximum temperatures were evaluated. Wind speed was established to follow a time series that fluctuated around ARIMA (0,1,1) and ARIMA (1,1,1). The optimal ANN model was established at 10 hidden neurons. The performance indices considered all indicated that the ANN wind speed model was superior to the ARIMA model. Wind speed prediction accuracy can be improved to secure the safety of air traffic flow as well support the implementation of a reliable and secure power generation system at the airport.
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Teachings

Checkout our current lessions

  • Postgraduate - STAT6024 Sampling Theory.

    This is a three-credit hour graduate course, which I have taught since Fall 2016. In this ...

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    img/service/e08d24-f451-35c1-21b2-eed7a7fc7f0f.jpg/

    This is a three-credit hour graduate course, which I have taught since Fall 2016. In this course, I employ didactic methods with analyses and research components to provide more skills and knowledge among my students to handle computational and data science related tasks. To be able to achieve the learning outcome, every student works on a course project, presents and defends the novelty of the research idea in class.

  • Postgraduate - STAT6012 Computational Statistics

    This is a three-credit hour graduate course, which I have taught since Fall 2016. I employ...

    X

    img/service/de3c31-bf20-787c-70a1-a3808e51c7.jpg/

    This is a three-credit hour graduate course, which I have taught since Fall 2016. I employ summative and didactic methods with computational and research components to provide more skills and knowledge among my students so as to handle computational and data science related tasks. To be able to achieve the learning outcome, every student works on a course project, presents and defends the novelty of the research idea in class.

  • Undergraduate - STAT5537 Multivariate Technique.

    Is a four-credit hour undergraduate course, which I have taught since Fall 2016. I have tr...

    X

    img/service/2a63c3b-ee20-b1a4-4cdd-e510b7e845dc.jpg/

    Is a four-credit hour undergraduate course, which I have taught since Fall 2016. I have transformed this course such that every student works on a course project, presents and defends the novelty of the multivariate data analysis in class. When teaching this course, I often emphasize depth of theory and application to ensure delivery of skills to perform data analytics and support data science.

  • Undergraduate - STAT3336 Computational Techniques in Statistics

    This is a four-semester hour undergraduate course, which I have taught since Fall 2017. I ...

    X

    img/service/26b353-8e0-4f-b3f-62076b1f42.jpg/

    This is a four-semester hour undergraduate course, which I have taught since Fall 2017. I emphasize depth of statistical knowledge and skill of programming with algorithm analysis to ensure delivery of skills to perform data analytics and support data science.

  • Undergraduate - STAT3335 Introduction to Sampling. Technique

    Is a four-credit hour undergraduate course, which I taught in Fall 2017 and SP 2017. I emp...

    X

    img/service/04cbbb-77f1-653f-038-642d8ddbd8b5.jpg/

    Is a four-credit hour undergraduate course, which I taught in Fall 2017 and SP 2017. I emphasize its importance as a core to a statistical problem because it builds a foundation for data, analyses and inference that are key components for handling statistical analysis and related tasks.

  • Undergraduate - STAT2103 Probability for Engineers.

    Is a four-credit hour undergraduate service course taught to engineering and computer scie...

    X

    img/service/c7ae5e-c4a2-3f6a-b710-0ab7f55f1031.jpg/

    Is a four-credit hour undergraduate service course taught to engineering and computer science students, which I taught in Fall 2018 and SP 2018. I cover the course content in a way to interest students of engineering tasks as I highlight its importance in quality control, measurements and need to identify the correct probability distribution when addressing engineering problems.

  • Undergraduate - STAT2102 Introduction to Probability

    Is a four-credit hour undergraduate course, which I taught in SP 2017 and Fall 2017. I cov...

    X

    img/service/644ba0d-f3b-8dd1-a6f-1b8e4447b72b.jpg/

    Is a four-credit hour undergraduate course, which I taught in SP 2017 and Fall 2017. I cover the course content with the objective to interest students to develop the required core analytical skills through understanding foundations of probability theories required to identify the correct probability distribution when tackling scientific problems.

  • Undergraduate - STAT2101 Introduction to Statistics

    Is a five-credit hour introductory undergraduate course, which I have taught since Fall 20...

    X

    img/service/52adf4e-eb4-067-41a-7528a3fce.jpg/

    Is a five-credit hour introductory undergraduate course, which I have taught since Fall 2016. It is multi-section, which recently together with the course coordinator, Dr. Iman Al-Hasani, we introduced a project to enable students directly apply the statistical concepts to real life problems using R for statistical analysis.