I'm interested in computational modeling of language learning, in particular about non-arbitrary form-meaning mappings and their possible role in language learning, language processing and language eveolution. My research agenda revolves around investigating whether non-arbitrary mappings between how words sound and what they mean influence the way children learn language, the way people process and comprehend it, and the way languages evolved as communication systems to be easy to learn and convenient to process.

I'm also interested in using sequence modeling techniques to investigate data science questions in different domains, including but not limited to user intent prediction, educational paths, employment trajectories, and learning tasks.

The master course in Natural Language Processingfor the DSS master focuses on core NLP problems and tools and how they are used to develop end-to-end applications such as language generation, text classification and machine translation are reviewed. Students learn to design an NLP tool to address a specific problem (choose the data, pre-process it, extract the relevant information, evaluate the system and improve on it). This course assumes a non-technical audience.

The bachelor course in Computational Linguisticsfor the CSAIprogram (3rd y) covers similar topics but focuses more on the algorithms and the solutions developed in the last decades, to provide students with the ability to implement these solutions. The relation between computational linguistics, cognitive science and AI is also investigated. The focus is more the applied and a rather technical audience is assumed.

I also teach the R practical sessions for theStatistics for CSAI II(CSAI bachelor, 2nd y)

Slides and materials for all courses are available upon request.