VICTOR WANDERA LUMUMBA

My mission is to skillfully apply statistical and research expertise to generate actionable insights. As a dedicated statistician and an aspiring research officer, I am committed to conducting meticulous quantitative and qualitative analyses that empower informed decision-making. By employing cutting-edge methodologies, I strive to unravel meaningful patterns within data, contributing to the organization's mission of innovation and profitability. My mission extends to maximizing the value derived from available resources, fostering a culture of efficiency, and driving continuous improvement. Through my endeavors, I aim to be a catalyst for positive change, ensuring the organization remains at the forefront of data-driven excellence.

• Black Foxes Football Club, Patron, Mungoni, Present
• Eco-Stat Club Chuka University, Member, Chuka, 2016-09, 2020-12

• Member of Kenya National Statistical Society (KNSS)/The Statistical Society of Kenya-Member No. 752493
• Member of International Biometrics Society, Kenya Chapter. Member No. 1508813

• Muriithi, D., Lumumba, V. W., Okongo, M. (2024). A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. American Journal of Theoretical and Applied Statistics, 13(4), 65-72. https://doi.org/10.11648/j.ajtas.20241304.11
• Wanjuki, T. M., Lumumba, V. W., Kimtai, E. K., Mbaluka, M. K., & Njoroge, E. W. (2024). Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya. European Journal of Mathematics and Statistics, 5(4), 1–18. https://doi.org/10.24018/ejmath.2024.5.4.310
• Lumumba, V. W., Mutugi, T. W., & Adolphus Wagala. (2024). Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models. Asian Journal of Probability and Statistics, 26(9), 97–109. https://doi.org/10.9734/ajpas/2024/v26i9648
• Lumumba, V. W., Kiprotich, D., Dpaine, M., Makena, N., & Kavita, M. (2024). Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models. American Journal of Theoretical and Applied Statistics, 13(5), 127–137. https://doi.org/10.11648/j.ajtas.20241305.14
• Muriithi, D. K., Lumumba, V. W., Awe, O. O., & Muriithi, D. M. (2025). Explainable Artificial Intelligence Models for Predicting Malaria Risk in Kenya. European Journal of Artificial Intelligence and Machine Learning, 4(1), 1–8. https://doi.org/10.24018/ejai.2025.4.1.47
• Lumumba, V. W., Wanjuki, T. M., & Njoroge, E. W. (2025). Evaluating the Performance of Ensemble and Single Classifiers with Explainable Artificial Intelligence (XAI) on Hypertension Risk Prediction. Computational Intelligence and Machine Learning, 6(1). https://doi.org/10.36647/ciml/06.01.a004
• 7th August 2025 – 8th August 2025: Hands-on Workshop for AI and Machine Learning Modeling on Malaria Prediction in Kenya, held at the Centre for Data Analytics and Modeling (CDAM), Chuka University.
• 14th July 2025 – 17th July 2025: Learning in Health Research: Participated in the NAMBARI 8th workshop from 14th of July 2025 to 17th July 2025. NAMBARI is the Moi-Brown Partnership for HIV Biostatistics Training, and the Data Science for Social Determinants Program that hosted a four-day course on machine learning and predictive inference, with emphasis on topics in HIV. The workshop was conducted by Moi University in collaboration with Brown University (USA), Hasselt University (Belgium) and New York University (USA).
• 29th Oct 2024 – 31st Oct 2024: Attended and presented a paper in the 6th ANNUAL INTERNATIONAL RESEARCH CONFERENCE, Tharaka University.
• 23rd Oct 2024 – 24th Oct 2024: Attended and presented a paper in the 11th ANNUAL INTERNATIONAL RESEARCH CONFERENCE, Chuka University.
• 7th October 2024 – 9th October 2024: NAMBARI-DSSD: Workshop on Advanced Biostatistical Methods: Machine Leaning for Real World Data. Participated in the 7th Workshop on the Advanced Biostatistical Methods: Machine Learning for Real World Data held on 7th to 9th October 2024 at Eka Hotel Eldoret Kenya, hosted by Moi University /AMPATH in collaboration with Brown University (USA), Hasselt University (Belgium) and New York University (USA)
• 1st October 2023 – 4th October: Mentorship for the Tidy Time Series and Forecasting Webinar Workshop in R: Provided mentorship and guidance to trainees in a workshop focused on time series modeling and forecasting using R. Assisted participants in handling exercises related to time series analysis, helping them to develop advanced R programming skills and apply tidy principles for effective time series forecasting. The mentorship aimed to enhance trainees' capabilities in modeling and analyzing time series data, ensuring a practical understanding of the concepts covered.
• 29th August 2024 – 30th August 2024: Malaria Modeling Workshop at Chuka University: I participated in a workshop on Malaria Modeling using R software to develop practical skills to combat malaria within the Kenyan context. The workshop was held at Chuka University, Kenya
• 13th July 2024 – 17th July 2024: WASHA TAKWIMU Workshop: Data Science Initiative for Africa (DSI-A) Training Program: A Short Course in Machine Learning for Health Data Science. University of KawZulu-Natal Venue: Southern Sun Elangeni and Maharani hotel, 63 Snell Parade, North Beach, Durban, South Africa
• 23rd Oct 2023 – 27th Oct 2023: AFRICAST workshop on tidy time series & Forecasting in R and Rstudio: This workshop was part of the Forecasting for Social Good (F4SG) initiative, and was conducted online from the 23rd-27th October 2023. The objectives of the forecast were to preparing time series data for analysis and exploration, extracting and computing useful features from time series data and effectively visualizing it and finally identifying appropriate forecasting algorithms for time series and selecting the best approach for the data at hand.
• 3rd May 2023 – 5th May 2023: NAMBARI-DSSD: Workshop on Advanced Biostatistical Methods: Machine Learning for Real World Data. Participated in the 6th Workshop on the Advanced Biostatistical Methods: Machine Learning for Real World Data held on 3rd to 5th July 2023 at Noble Hotel Eldoret Kenya, hosted by Moi University /AMPATH in collaboration with Brown University (USA), Hasselt University (Belgium) and New York University (USA)

• Jan 2020 – April 2024: Tutor: Statistical Analysis and Modeling at Eco-Stat Club, Chuka University
• Extensive Tutoring Experience: Over two (2) years of experience in tutoring students in statistical analysis, helping them master complex concepts and methodologies.
• Proficiency in Statistical Software: Proficient in teaching a wide range of statistical programs, including SPSS, STATA, EViews, Minitab, SAS, R, RStudio, Gretl, and more.
• Customized Learning Plans: Developed and implemented customized tutoring plans tailored to individual student needs, enhancing their understanding and application of statistical techniques.
• Hands-on Practical Sessions: Conducted practical sessions that focused on real-world data analysis, allowing students to apply theoretical knowledge to actual data sets.
• August 2024 – To Date: Data Analyst: Center for Data Analytics and Modeling (CDAM), Chuka University
• Student Performance Improvement: Consistently helped students achieve significant improvements in their capstone projects, with many advancing to higher levels of statistical proficiency.
• Mentorship and Guidance: Provided ongoing mentorship, offering guidance on research projects, thesis work, and data analysis strategies using various statistical tools.
• Diverse Student Engagement: Worked with a diverse student population, including undergraduates, postgraduates, and professionals from various disciplines, ensuring a comprehensive understanding of statistical methods.
• Workshops and Seminars: Organized and led workshops and seminars on advanced statistical topics, contributing to students' deeper understanding of data analysis techniques.
• Feedback and Assessment: Regularly provided detailed feedback on student work, including assignments and projects, to foster continuous learning and improvement in statistical analysis skills.

• Modelling and forecasting Malaria-A concept in Machine Learning
• Modelling and predicting psychological mental health distress scores among university students in Kenya-A concept in Machine Learning
• Applied Statistics: Health sciences, Economic growth, Climate Changes
• Designs and Analysis of Experiment: Response Surface Methodology
• Time Series Analysis and Spatial temporal modelling in Machine learning
• Data science- Big data Analytics via Data Integration from multiple sources
• Optimal designs and their applications in Agriculture and other related fields