Advances in data and computing techniques have opened possibilities for real-time and cost-efficient conflict prediction and early warning capabilities, with news-based data being utilized to generate relevant forecasts. ...

This study delves into understanding and predicting user engagement in Enhance VR, a virtual reality cognitive training application, through data-driven approaches. The dataset encompasses de-identified user data including ...

This thesis presents a general-purpose corpus construction methodology with Twitter data for a given political topic in a given country. It applies the methodology to immigration in Chile from November 2021 to April 2022, ...

Bayesian optimization has emerged as an effective and efficient approach for finding the global optimum of highly complex derivative-free black-box functions. It typically models the objective function with Gaussian processes ...

Normalizing flows are an elegant approximation to generative modelling. It can be shown that learning a probability distribution of a continuous variable X is equivalent to learning a mapping f from the domain where X is ...

In this thesis project I analyse labour flow networks and company control networks in the UK. I observe that these networks exhibit characteristics that are typical of empirical networks, such as heavy-tailed degree ...

In this thesis we develop a traffic light control agent that can manage traffic lights with the objective to reduce traffic jams, trip time and other traffic metrics in a given network using reinforcement learning. To this ...

Hierarchical modeling is a practical approach with proven results in modeling real world data. This paper studies Gaussian hierarchical models and methods which exploit the sparse conditional independence structure of such ...

This paper studies a novel particle filter method proposed by Brownlees and Kristensen (2017) for parameter estimation of nonlinear state space models. The particle filter, named Importance Sampling Particle Filter, is tested ...