Ricardo Gonçalves

Ricardo Gonçalves is an Assistant Professor at the Computer Science of Faculdade de Ciências e Tecnologia of Universidade NOVA de Lisboa. He is an effective member of NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), where he develops his research in the Intelligent Systems group, mainly focusing on Artificial Intelligence, on the area of Knowledge Representation and Reasoning (KRR). He completed his PhD in Mathematics from Instituto Superior Técnico, Universidade Técnica de Lisboa. He has co-authored 10 journal articles, notably two recent in the Artificial Intelligence Journal, and some others in top journals in his area of research (Theory and Practice of Logic Programming, Studia Logica, Logica Universalis, Journal of Applied Non-Classical Logics). He has co-authored more than 30 papers in international conferences, some of which published in some of the most important conferences in his research area (AAAI, KR, ECAI, AAMAS, JELIA, LPNMR). He is the co-author of the Best Paper at the 17th Edition of the (CORE Rank A) European Conference on Logics in Artificial Intelligence (JELIA 2021). He has served in the Program Committee of the International Joint Conference on Artificial Intelligence (IJCAI) (since 2013), the AAAI Conference on Artificial Intelligence (since 2015) and the European Conference on Artificial Intelligence (ECAI) (since 2014), the most prestigious conferences in the areas of Artificial Intelligence. He has been the co-chair of the Knowledge Representation and Reasoning track at the EPIA Conference on Artificial Intelligence, since 2017. He regularly participates in reviewing activities for top journal and conferences in its research area. He has participated in several projects and, in particular, has made substantial contributions to the projects FORGET and RIVER. He is currently a team member of two European projects, Sustainable Stone by Portugal and NEURASPACE. His work has focused on the integration of heterogeneous knowledge and its evolution, with contributions in stream reasoning on distributed and heterogeneous knowledge sources. He has also focused on the area of Forgetting in Answer Set Programming (ASP), where he made important contributions, namely in the study of the limits of forgetting variables in ASP programs. Recently he has also been interested in the important problem of explainability of sub-symbolic AI methods using symbolic AI methods. He has made contributions to increase the explainability of deep neural networks.