Cláudia Antunes has concluded her PhD in the domain of machine learning and data mining, proposing new methods and methodologies for dealing with temporal data, in particular for mining event sequential patterns. She argued that was possible to efficiently use constrained sequential pattern mining algorithms over nominal temporal data to discover unknown information, keeping the process centered on the user. For proving her claim, she introduced the concept of constraint relaxation and proposed methods for mining frequent patterns in the presence of domain knowledge, represented as a context-free language. The work was validated in the context of some case studies in education and retail analysis. Cláudia has been working on algorithms and methodologies for pattern mining, from transactional to structured data. Her main interests are centered on mining complex knowledge from complex data, with emphasis on the incorporation of background knowledge in the data mining process. Her work has been around the usage of domain ontologies and database models (in particular star schemas) to represent that knowledge, and she has been working on methods to efficiently mine patterns, guided by that knowledge. From the experience on pattern mining with background knowledge, her research has additionally embraced the classification task, either by extending traditional training algorithms or by enriching the learning process to make use of the discovered patterns. Time is a particular dimension that has deserved considerable attention along the years. However, the obtainable models continue to present poor results on problems where time is essential, like for example prognosis problems. Along years, Cláudia’s research has addressed this issue. With some interesting results. Cláudia has been applying and validating these methods mainly on the areas of healthcare and education. She has more than seventy papers published in international conferences and journals, and has supervised more than thirty Master thesis and a few PhDs. She has been lecturing data science courses for twenty years, both in graduation and post-graduation programs.