I work on visualization algorithms. It is a great topic to work in as it provides a very concrete output of otherwise opaque data.
One downside is that it can be hard to work in only 2 dimensions (or three) instead of a latent space with hundreds on dimensions. This usually changes the problem in unexpected ways, leading to interesting questions about the model in general.
If you are a student at the University of Tübingen and want to work in this domain, please reach out. We have opportunities for lab rotations or Master thesis supervision.
Currently I work on visualization for image data. It neatly ties together nonlinear dimension reduction, à la t-SNE, with contrastive learning. While the former shines in the visualization domain by optimizing and extracting information from a kNN graph (with a lot of hand-waiving), the latter learns a good representation by implicitly defining a graph due to transformations on the original data. By combining those two approaches, we can leverage a new optimization goal for image data that produces sensible visualizations of natural images.
My topic for the MSc thesis was mostly concerned with making a comprehensive comparison between t-SNE and UMAP (and others). We noticed that these algorithms can be generalized as a visualization that blanaces attraction and repulsion, which revealed that UMAP relates to another parameter configuration of t-SNE. This means that the purported dissimilarity the algrithms are optimizing the same objective which lies on a spectrum.