More text data is available today than ever before -- from scientific literature to social media to clinical records. Learning representations of this data is key to making it searchable, interpretable, and ultimately useful for discovering patterns that would be impossible to find manually.
Rita González Márquez
Rita is a PhD student in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen and the IMPRS-IS graduate school. She is interested in using machine learning methods to learn meaningful representations of text data. Her research spans both high-dimensional and low-dimensional embedding spaces: she works on fine-tuning transformer-based models to produce text representations that accurately reflect semantic relationships, and on adapting dimensionality reduction methods to large datasets for visualizing and exploring scientific corpora. A common thread across her work are the questions of what makes a representation good and what can the representation reveal about the underlying data. Applying these methods to large scientific corpora, she investigates questions about the structure and evolution of research fields, scientific trends, and research integrity.
More text data is available today than ever before -- from scientific literature to social media to clinical records. Learning representations of this data is key to making it searchable, interpretable, and ultimately useful for discovering patterns that would be impossible to find manually.