Ralph Abboud
Interests
My research interests primarily lie at the intersection between deep learning and knowledge representation and reasoning. In particular, I am interested in developing hybrid neuro-symbolic systems, based on neural networks and explicit logical structures, to more efficiently and reliably learn patterns and make inferences from data. The main aim of my research is to produce systems that combine the learning capacity of deep neural networks with the reliability, safety, and interpretability of logical systems.
Biography
I earned a B.E. in Computer Engineering from the Lebanese American University (LAU), with a minor in Mathematics, in 2017, and my capstone project, supervised by Dr. Joe Tekli, was on automated sentiment-aware music composition using machine learning and genetic algorithms. I then completed an MSc in Computer Science at the University of Oxford in 2018, with my thesis, supervised by Prof. Daniel Kroening, tackling neural program synthesis. Parallel to my studies, I also earned a Baccalaureate degree in Piano performance from the Lebanese National Higher Conservatory of Music (LNHCM) in 2017.
Selected Publications
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Temporal Knowledge Graph Completion using Box Embeddings
Johannes Messner‚ Ralph Abboud and İsmail İlkan Ceylan
In Proceedings of the 36th AAAI Conference on Artificial Intelligence‚ AAAI 2022‚ Vancouver‚ BC‚ Canada. AAAI Press. 2022.
Details about Temporal Knowledge Graph Completion using Box Embeddings | BibTeX data for Temporal Knowledge Graph Completion using Box Embeddings | Link to Temporal Knowledge Graph Completion using Box Embeddings
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The Surprising Power of Graph Neural Networks with Random Node Initialization
Ralph Abboud‚ İsmail İlkan Ceylan‚ Martin Grohe and Thomas Lukasiewicz
In Proceedings of the 30th International Joint Conference on Artificial Intelligence‚ IJCAI 2021‚ August 21–26‚ 2021. IJCAI. August, 2021.
Details about The Surprising Power of Graph Neural Networks with Random Node Initialization | BibTeX data for The Surprising Power of Graph Neural Networks with Random Node Initialization | Link to The Surprising Power of Graph Neural Networks with Random Node Initialization
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BoxE: A Box Embedding Model for Knowledge Base Completion
Ralph Abboud‚ İsmail İlkan Ceylan‚ Thomas Lukasiewicz and Tommaso Salvatori
In Proceedings of the 34th Annual Conference on Neural Information Processing Systems‚ NeurIPS 2020‚ December 6–12‚ 2020. December, 2020.
Details about BoxE: A Box Embedding Model for Knowledge Base Completion | BibTeX data for BoxE: A Box Embedding Model for Knowledge Base Completion | Link to BoxE: A Box Embedding Model for Knowledge Base Completion