Mälardalen–Kenya Workshop
Engineering Mathematics, Mathematical Statistics and Financial Engineering, Algebra, Analysis and Applications

Mälardalen University, Västerås, Sweden
May 8, 2026

This workshop is part of a strategic initiative to develop sustainable international collaboration in research and higher education in Mathematics and Applied Mathematics. It brings together researchers and educators from the Institute of Mathematical Sciences, Strathmore University (Nairobi, Kenya) and the Department of Business and Mathematics, Mälardalen University (Västerås, Sweden), with the objective of strengthening academic excellence, fostering capacity building, and establishing a foundation for long-term joint research projects, supervision, and academic mobility.

Organizers:

• Professor Sergei Silvestrov, Mathematics and Applied Mathematics (MAM), Department of Business and Mathematics, Mälardalen University, Västerås, Sweden

• Dr. Elvice Ongong’a, Academic Director, Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya

Programme:

Time and room: 13:15-17:00, Room U2-158 (2nd floor, U-building),

Mälardalen University, Västerås

13:15-13:40: Dr. Elvice Ongong’a, Academic Director, Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya.

Combinatorial structures associated with Lie and Hom-Lie algebras of finite dimension.

Abstract: In this talk, I will present on the structure of directed graphs associated with Lie algebras and a proposal on how we can extend these to Hom-Lie algebras of low-dimension and possible future research ideas.

13:40-14:05: Professor Ying Ni, Mathematics and Applied Mathematics (MAM), Department of Business and Mathematics, Mälardalen university.

Research in Financial Mathematics.

Abstract: In this talk, I will present selected research projects conducted within the research group Stochastic Processes, Statistics and Financial Engineering. The presentation will highlight interdisciplinary collaborations, including work at the intersection of financial mathematics with machine learning, economics, and finance. In addition, I will discuss applications in engineering domains such as energy systems and reliability engineering.

14:05-14:30: Dr. Jean-Paul Murara, Mathematics and Applied Mathematics (MAM), Department of Business and Mathematics, Mälardalen university

A Controlled Optimal Portfolio with Stochastic Behavior.

Abstract: In this talk, the Merton investment-consumption optimal control problem is modified considering a non-constant volatility. Different utility functions are also used, and the corresponding HJB equations are derived. Different investment strategies are constructed, and related simulations are performed.

14:30-14:55: Dr. Christopher Engström, Department of Business and Mathematics, Mälardalen university.

Applications of data analysis in health and medicine.

Abstract: In this talk we will discuss mathematical methods in two ongoing projects

1) Dust exposure and relation to inflammation in the metal industry. Analysing a collection of data from multiple studies, pitfalls and study design.

2) Metabolic syndrome, estimation of risk factors and alternative approaches. With a focus on feature selection in classification and evaluation with unbalanced data or goals.

Keywords: Anova, batch effects, feature selection, classification, regularization.

14:55-15:20: Dr. Lars Hellström, Mathematics and Applied Mathematics (MAM), Department of Business and Mathematics, Mälardalen university.

Probability from linear algebra.

Abstract: It's taught in basic courses how to compute the moments of a probability distribution (of course, the resulting integrals are sometimes very hard), but can one do the opposite: compute the distribution when given its moments? It turns out that one can, if taking a detour via spectral theory: recover the distribution as the spectral measure of an appropriate operator. What is not well known is that this detour is in fact computationally effective: there is an algorithm which computes, with rigorous error bounds, the probability density at a point of a distribution about which is only given its sequence of moments. That algorithm begins as linear algebra, but simplifies to a single iterative loop which runs in bounded memory! This talk explains how it works.

15:20-15:30: Break

15:30-15.50: Abaaluk Esmie Awonteme, Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya.

Promise or Peril? County-Level Evidence on Digital Loans and Financial Well-Being in Kenya.

Abstract: The rapid expansion of digital lending in Kenya has transformed access to credit, yet its implications for household financial well-being remain contested. This study investigates whether digital loans represent a promise or a peril for Kenyan households, using FinAccess survey data. A binary classification framework is adopted, beginning with survey-weighted logistic regression, followed by Random Forest and Gradient Boosting models. Model performance is evaluated using AUC-ROC and Brier Score metrics. Predicted probabilities are aggregated to generate county-level vulnerability maps across all 47 Kenyan counties. SHAP explainability is used to interpret model predictions. Results indicate that digital loan use is significantly associated with financial distress, mediated by income, employment, education, and health status.

15:50-16:10: Edwin Kumadoh, Institute of Mathematical Sciences, Strathmore University.

Hidden Naive Bayes Classifier for Continuous Features Using Semiparametric Density Estimation Method.

Abstract: This study extends the Hidden Naive Bayes classifier by replacing discretisation of continuous variables with a semiparametric density estimator combining Gaussian and kernel-based methods. Inter-feature dependencies are estimated using k-nearest neighbour conditional mutual information. Simulation studies across varying sample sizes, dimensions, correlations, and distributions show that the proposed model consistently achieves lower log-loss, particularly under non-Gaussian and highly correlated settings.

16:10-16:30: Antwi Pascal, Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya.

Spatio-Temporal Generalized Linear Modeling of Climatic Influences on Malaria Incidence in Ghana.

Abstract: Malaria incidence in Ghana is strongly influenced by climatic conditions. This study applies a Bayesian spatio-temporal generalized linear model to malaria incidence data from 2020–2024, incorporating rainfall, temperature, humidity, dew point, solar energy, UV index, and seasonal effects. Overdispersion testing motivated the use of a Negative Binomial likelihood, implemented via Integrated Nested Laplace Approximation. Results reveal significant lagged climatic effects and identify persistent high-risk regions, offering evidence for climate-informed malaria control and spatially targeted interventions.

16:30-16:50: Barry Mamadou, Institute of Mathematical Sciences, Strathmore University.

Mathematical Modeling of Chlorophyll Depletion and Red Spider Mite Population Dynamics.

Abstract: The phytophagous red spider mite (Tetranychus evansi) damages tomato plants by depleting chlorophyll within mesophyll parenchyma cells, reducing photosynthetic capacity. Existing pest models lack mechanistic representation of this process. This research develops a novel mathematical model linking chlorophyll loss, mite reproduction, and climatic variability, providing insights for improved pest management strategies under changing climate conditions.

16:50-… Collaboration discussions and End of the Workshop.