Instructor & credits

Donald zvada

PhD student in Statistics at Trinity College Dublin working on Bayesian and statistical methodology for longitudinal dietary patterns, health outcomes, and computational modeling.

About the instructor

Donald Zvada is a PhD student in Statistics at Trinity College Dublin, where he is developing novel statistical methods for the analysis of longitudinal dietary patterns and their association with health outcomes. His work sits at the intersection of statistical modeling, machine learning, longitudinal data analysis, and decision-oriented data science.

Current research interests

Bayesian statistics and probabilistic modeling
longitudinal dietary patterns and health outcomes
nutritional epidemiology
machine learning for structured and sequential problems
large language models for sequential decision-making

Academic background

PhD in Statistics, Trinity College Dublin
T-DIET: developing novel statistical methods for longitudinal dietary patterns analysis.
MSc in Mathematical Sciences, AIMS South Africa / Stellenbosch University
BSc Honours in Operations Research and Statistics, National University of Science and Technology

Professional and research experience

Doctoral Researcher, Trinity College Dublin
Longitudinal dietary patterns analysis and statistical methodology development.
Quantitative Risk Analyst, EY Zimbabwe
Credit risk model development and validation for IFRS 9 workflows.
Research projects in large language models for sequential decision-making, quantum key distribution protocol design, and customer churn prediction using deep learning.

Awards and honours

T-DIET fully funded PhD scholarship
MasterCard Foundation scholarship
Chancellor’s Award for overall best graduating male student
Vice Chancellor’s Award in Operations Research and Statistics
multiple departmental and faculty academic awards

Teaching and leadership

statistics and mathematics tutoring
scholarship student representation
outreach and tutoring leadership roles

Contact

Course credits

This course website is authored in Quarto and draws on the Bayesian statistics, computation, and workflow literature listed throughout the site. The structure of the site is intentionally course-like rather than blog-like: modular navigation, part overviews, executable labs, and a dedicated references workflow are all deliberate design choices.

The site organization and front-matter course framing were inspired in part by Claire David’s course website, Introduction to Machine Learning. The Bayesian curriculum, technical content, derivations, examples, and software workflow in this repository were written specifically for this course project.

Software credits

The course relies especially on:

Notes on source material

Biographical details on this page were adapted from the CV provided for this project.

License

Course content: CC BY-NC-SA 4.0
Code examples: MIT

--- title: "Instructor & credits" number-sections: false --- ::: {.instructor-hero} ![Donald Zvada](assets/images/instructor.jpg){.instructor-photo} ::: {.instructor-summary} ## Donald zvada PhD student in Statistics at Trinity College Dublin working on Bayesian and statistical methodology for longitudinal dietary patterns, health outcomes, and computational modeling. ::: ::: ## About the instructor Donald Zvada is a PhD student in Statistics at [Trinity College Dublin](https://www.tcd.ie/), where he is developing novel statistical methods for the analysis of longitudinal dietary patterns and their association with health outcomes. His work sits at the intersection of statistical modeling, machine learning, longitudinal data analysis, and decision-oriented data science. ### Current research interests - Bayesian statistics and probabilistic modeling - longitudinal dietary patterns and health outcomes - nutritional epidemiology - machine learning for structured and sequential problems - large language models for sequential decision-making ### Academic background - **PhD in Statistics, Trinity College Dublin** T-DIET: developing novel statistical methods for longitudinal dietary patterns analysis. - **MSc in Mathematical Sciences, AIMS South Africa / Stellenbosch University** - **BSc Honours in Operations Research and Statistics, National University of Science and Technology** ### Professional and research experience - **Doctoral Researcher, Trinity College Dublin** Longitudinal dietary patterns analysis and statistical methodology development. - **Quantitative Risk Analyst, EY Zimbabwe** Credit risk model development and validation for IFRS 9 workflows. - **Research projects** in large language models for sequential decision-making, quantum key distribution protocol design, and customer churn prediction using deep learning. ### Awards and honours - T-DIET fully funded PhD scholarship - MasterCard Foundation scholarship - Chancellor's Award for overall best graduating male student - Vice Chancellor's Award in Operations Research and Statistics - multiple departmental and faculty academic awards ### Teaching and leadership - statistics and mathematics tutoring - scholarship student representation - outreach and tutoring leadership roles ## Contact - Email: [zvadad@tcd.ie](mailto:zvadad@tcd.ie) - GitHub: [github.com/Donniezvadah](https://github.com/Donniezvadah) - LinkedIn: [donald-zvada](https://www.linkedin.com/in/donald-zvada/) ## Course credits This course website is authored in Quarto and draws on the Bayesian statistics, computation, and workflow literature listed throughout the site. The structure of the site is intentionally course-like rather than blog-like: modular navigation, part overviews, executable labs, and a dedicated references workflow are all deliberate design choices. The site organization and front-matter course framing were inspired in part by Claire David's course website, [Introduction to Machine Learning](https://clairedavid.github.io/intro_to_ml/intro.html). The Bayesian curriculum, technical content, derivations, examples, and software workflow in this repository were written specifically for this course project. ## Software credits The course relies especially on: - [Quarto](https://quarto.org/) - [R](https://www.r-project.org/) - [Stan](https://mc-stan.org/) - [CmdStanR](https://mc-stan.org/cmdstanr/) - [posterior](https://mc-stan.org/posterior/) - [bayesplot](https://mc-stan.org/bayesplot/) - [loo](https://mc-stan.org/loo/) - [JAX](https://github.com/google/jax) ## Notes on source material Biographical details on this page were adapted from the CV provided for this project. ## License - Course content: [CC BY-NC-SA 4.0](LICENSE-CONTENT.md) - Code examples: [MIT](LICENSE-CODE)