Frank Miller

Spring 2023: Advanced computational statistics (PhD level course)

Spring 2022: Computational statistics (Master's level course)

Fall 2021: Experimental design (Master's level course)

Spring 2021: Computational statistics (Master's level course)
               Optimisation algorithms in Statistics II (PhD level course)
               Two lectures of Machine Learning (Master's level course)

Fall 2020: Experimental design (Master's level course)
                Optimisation algorithms in Statistics I (PhD level course)

Achievement tests, biostatistics, design of experiments (active machine learning, adaptive and sequential designs, clinical trials, optimal designs), optimization algorithms.

Project funded by the Swedish Research Council

Optimal calibration of questions in computerized achievement tests

Topics for future PhD projects

Improved methods for pretesting achievement tests and implementation. Questions for larger achievement tests like PISA, högskoleprovet, and national tests in school need to be pretested in advance. The Swedish research council funded a reseach project to improve methods for this pretesting. There is possibility for a further PhD student to contribute to this project.

Optimization algorithms. In several areas of statistics including optimal experimental design and machine learning, it is essential to have efficient algorithms for computing optimal solutions numerically. While optimization algorithms have a considerable history, many new algorithms have been suggested in recent years. In this PhD project, we will work on improving modern optimization algorithms.

Active machine learning. This area deals with situations where unlabeled data is available but there is the possibility to label some of the observations. Methods of optimal experimental design give the opportunity to choose observations for labeling which are most suitable. The PhD project aims to improve the current active machine learning methods.