The COVID-19 global pandemic is a threat not only to the health of millions of individuals,
but also to the stability of infrastructure and economies around the world. The disease will …

The discovery of structure from time series data is a key problem in fields of study working
with complex systems. Most identifiability results and learning algorithms assume the …

Risk stratification is an essential tool in the fight against many diseases, including chronic
kidney disease. Recent work has focused on applying techniques from machine learning …

Thanks to the significant developments in healthcare industries, various types of medical
data are generated. Analysing such valuable resources aid healthcare experts to …

Recurrent neural network based solutions are increasingly being used in the analysis of
longitudinal Electronic Health Record data. However, most works focus on prediction …

As more data is generated from medical attendances and as Artificial Neural Networks gain
momentum in research and industry, computer-aided medical prognosis has become a …

We present a general framework for kernel hypothesis testing on distributions of sets of
individual examples. Sets may represent many common data sources such as groups of …

Longitudinal data analysis is a crucial component of public health research because it
provides information about temporal changes and the progression of health outcomes …

Analysis of medical data and making precise decisions by machine learning is emerging as
a hot topic in healthcare. The ultimate goal of using these techniques is to transform data …

In this thesis, data driven approaches to sequential decision making problems within
process systems engineering (PSE) are developed. Specifically, the use of model-free …