Integrating single-cell multi-omic data is crucial for comprehensive biological discovery, yet it remains challenging due to the weak correlation between modalities, data heterogeneity, and stringent privacy regulations. Conventional integration methods that depend on shared features or matched cells, which are rarely available in practice. While some diagonal integration approaches might mitigate some of these limitations, they are sensitive to noise, prone to overfitting, and challenging to validate, especially in the absence of centralized data access. This thesis introduces Federated Matching xcross modalities via Fuzzy smoothed embeddings (MaxFuse), a novel adaptation of MaxFuse within a Federated Learning (FL) framework, which enables privacy-preserving diagonal integration through fuzzy smoothing, federated Canonical Correlation Analysis (CCA), and iterative matching without exchanging raw data. We validate Federated MaxFuse on benchmark single-cell datasets, demonstrating that it achieves matching accuracy and embedding quality comparable to centralized baselines across supervised and unsupervised metrics. These findings establish Federated MaxFuse as a practical and scalable solution for privacy-preserving integration of multi-omic data, enabling robust cross-institutional analyses under real-world constraints.

TOPdesk is a service management software provider in a wide variety of domains and industries. TOPdesk also offers consultancy to their customers that aims to continuously assess and improve the customer’s experience and service efficiency. TOPdesk offers a Mini Health Check (MHC) to their customers in which aconsultant analyzes how efficiently the customer uses their software based on six Key Performance Indicators (KPI). However, the process of creating an MHC report is very time-consuming as it requires performing a lot of manual steps. Also, the norms used for the KPIs provide little meaning as they are arbitrarily chosen and not specific to the customer’s industry. This report aims to improve the current process of performing an MHC. Research has been done on how the MHC is performed, identifying the suitable technologies and learning the currently existing infrastructure that helped us pave the way to create our product. During our project we managed to create a product that automates the MHC. Through user testing we found that this process now takes about two minutes, where the manual process took about two hours. To create more meaningful norms for the KPIs, we also implemented a benchmarking feature. This allows a company to compare the results of their MHC to other TOPdesk customers in the same sector, country or of similar size. We have some recommendations for TOPdesk for the further development of our product. The MHC process could be streamlined in a few ways, most importantly with respect to the process for getting access to customer data. Benchmarking could become even more useful if data can be more easily gathered from more TOPdesk customers.

The monumental goal of Artificial Intelligence (AI) is to model general solutions that can be applied to perform a variety of tasks that normally demand human intelligence to solve. Traditionally human game developers painstakingly design and tweak levels until achieving the precise output of their heart’s desire. In the gaming industry, AI for level generation can reduce the need for labour-intensive human design. A general game AI for level generation can only be created once we have a method to describe video games. Video Game Description Language (VGDL) is a high-level language for describing 2D arcade games that consists of two parts, a game and level description. Using this language allows us to analyze games at their mechanical level. The problem of General Video Game Level Generation (GVG-LG) can thus be defined as follows: construct a generator that, given a game (e.g. described in VGDL) which can be played by some AI player, builds any required number of different levels for that game which are enjoyable for humans to play [1]. This research investigates the characteristics of what makes automation of general level generation for 2D video games difficult, identifying what exactly makes it so challenging. Solutions such as algorithmic approaches and design patterns shall be presented. By investigating the techniques that have been used so far, empirical evidence will provide key insight into which techniques are most promising to improve level generation quality in the future.