Model-based clustering and classification group

The Model-based clustering and classification group works at the intersection of clustering and classification to meaningfully model and interpret real-life uncertainty.

We develop new statistical models and methods to better understand complex data, while also applying these techniques to real-world problems in science, health, and technology. With a strong foundation in statistical theory, our work advances both methodology and application, offering exciting opportunities for postgraduate research.

Under the guidance of Prof Johan Ferreira, many students have completed master's and PhD projects in this focus area within mathematical statistics and continued on their journey to make an impact in academic or corporate roles.

Some recently published papers:

1. 2025. "A refreshing take on the inverted Dirichlet distribution via a mode-parameterization with some statistical illustrations."

2. 2025. "A contaminated regression model for count health data."

3. 2025. "Soft computing for the posterior of a matrix t graphical network."

4. 2025. "A Power-Cardioid candidate for wind direction modelling motivated by two South African case studies".

5. 2025. "Discrete mode-mixtures of unimodal positive distributions with an application to solar energy in South Africa".

Some examples of recent masters and PhD project titles:

PhD projects:

Essays on autoregressive models with nonnormal errors

Advances and considerations of Dirichlet with an emphasis on entropy

Advancements in contaminated models: from count to continuous data

Masters projects:

Skew-Laplace candidates emanating from scale mixtures for insightful computational modelling

Alternative parametric considerations for direction and distance when modeling animal movement

Views on an adaptive wavelet graph Laplacian mixture model

A contaminated generalised t model for cryptocurrency returns

Opportunities for interested students:

Master’s and PhD students joining our group will gain experience in both the mathematical foundations of statistics and the practical implementation of methods for complex data. 足球竞彩app排名s are encouraged to work on projects that balance theory and application, often with opportunities for collaboration across disciplines.

What can you gain?

Our group is led by researchers with expertise in mathematical statistics, clustering, and machine learning. We welcome students interested in both the theoretical and applied sides of mathematical statistical research. We regularly collaborate with other researchers within South Africa and abroad! For further reading, have a look at Prof Ferreira's site.

For further details:

Information about funding and scholarships

The Wits application process

Prospective students can contact johan.ferreira@wits.ac.za with copies of their academic transcripts, a one-page motivation of their interest to work in this team in this area, and any potential budding ideas towards a possible research topic and focus.