Supervisors and external partners: Rafael Messias Martins

My main research is in the intersection between visual analytics and unsupervised machine learning, which currently leads commonly into interpretable/explainable machine learning. That is, in general, the study of novel ways to make machine learning more accessible, less obscure, and more accountable to anyone who needs it. In the context of Digital Humanities, I can contribute to any thesis that includes aspects of data science (visualization, machine learning, etc.), for example: the analysis of interesting data sets, the proposal of new interfaces and algorithms to investigate and explore data, or the evaluation of how the use of such concepts affect the workflow of digital humanities-related use cases. I’m open to discuss and come up with new research proposals based on the interests and expertise of the student and any other external stakeholders.