If you are interested in a MSc thesis with our group, then please read these instructions for how to start and complete your thesis in our group.
The goal of the project is to create an application for the VR headset Meta Quest 2 (MQ2; previously known as Oculus). The application is meant to visualise a data distribution that is provided as input and enable interactions between the user and the data (for example, rotation, zooming, data selection, etc). The emphasis of this theme is on scalability: how to scale the visualization of very large datasets. The supervisor will provide a headset for the needs of the project.
Many researchers and practitioners target the predictive performance of Machine Learning (ML) models, e.g., they aim to develop ML models that have high accuracy on certain predictive tasks. By contrast, within this theme, you will work on aspects of computational efficiency, i.e., study and potentially improve the running time of ML pipelines. To do that, you will consider the various stages of ML pipelines, end-to-end (e.g., data acquisition, data sampling and processing, model training and validation, model deployment and retraining) and study how the individual and joint efficiency of these stages can be improved. For example, your thesis may study how big a data sample is necessary and sufficient for the ML training to lead to high accuracy in short time; or how often an ML model should be updated in order to maintain good model accuracy without wasteful/redundant training.
Background articles:
Within this theme, you will focus on the scalability of graph processing tasks for large graphs. One direction is to consider common graph-processing tasks (e.g., computation of connected components, clusters, distances, embeddings, etc) and study how existing state-of-the-art algorithms for these tasks perform for massive graphs (i.e., with more than 100 million nodes or edges). Another direction is to consider a specific massive graph (e.g., a dataset that you are interested in or that is available in the research group) and study how to perform efficiently tasks for this particular graph.
Background articles:
See here: /en/researchgroups/algorithmic-data-science/people#section-98775.