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.
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.
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.
Merchant, A., Mathioudakis, M., & Wang, Y. (2023). Graph Summarization via Node Grouping: A Spectral Algorithm. In The 16th ACM International Conference On Web Search And Data Mining. [link]
Merchant, A., Gionis, A., & Mathioudakis, M. (2022). Succinct Graph Representations as Distance Oracles: An Experimental Evaluation. Proceedings of the VLDB Endowment, 15(11), 2297 - 2306. [link]
Merchant, A., Mahadevan, A., & Mathioudakis, M. (2022). Scalably Using Node Attributes and Graph Structure for Node Classification. Entropy, 24(7), 511-522. . [link]
This theme is related to the new Academy project High Performance Computing for the Detection and Analysis of Historical Discourses (HPC-HD), which aims to detect discourses from large historical corpora of the eighteenth century (e.g., books, pamphlets, newspapers). Historians and computer scientists in the project have identified a large number of cases of text-reuse between documents in the these corpora. Text-reuse may occur for various reasons, for example because one author quotes and discusses an opinion written by another author, or even when the same author reuses text from a previous published text of their own. For this thesis, you will analyze the text-reuse network. One possible task within the thesis is to identify clusters of text reuse (groups of documents that reuse heavily text from each other) or, more appropriately, ideas and lines of thought that have evolved over time, as evidence by text reuse (when author B discusses text by author A, and later an author C comments on the text written by author B, and so on). One concrete thesis topic is to apply the method by (Sharaf et.al., 2012) on the text reuse data.
See here: /en/researchgroups/algorithmic-data-science/people#section-98775.