Ralph Abboud

Bio. I hold a D.Phil in Computer Science from the University of Oxford, supervised by Dr. İsmail İlkan Ceylan and Prof. Thomas Lukasiewicz. My main research area is graph representation learning (GRL). More specifically, I am interested in identifying the strengths and limitations of GRL models, namely shallow embedding models and graph neural networks (GNNs), and proposing machine learning approaches with improved inductive capacity, interpretability, and representation power over relational data.

Current Work. I am working on large language models (LLMs) and natural language processing with the Learning Agency Lab and supporting the Learning Engineering program at Schmidt Futures. I am also a visiting scientist at the Francis Crick Institute.

Contact. Please get in touch at ralph {at} You can also find my old departmental webpage here.


Selected Publications

Shortest Path Networks for Graph Property Prediction, LoG 2022.
Ralph Abboud , Radoslav Dimitrov , İsmail İlkan Ceylan
Approximate Weighted Model Integration on DNF Structures, AIJ 2022.
Ralph Abboud , İsmail İlkan Ceylan , Radoslav Dimitrov
The Surprising Power of Graph Neural Networks with Random Node Initialization, IJCAI 2021.
Ralph Abboud , İsmail İlkan Ceylan , Martin Grohe , Thomas Lukasiewicz
BoxE: A Box Embedding Model for Knowledge Base Completion, NeurIPS 2020.
Ralph Abboud , İsmail İlkan Ceylan , Thomas Lukasiewicz , Tommaso Salvatori
For a complete list of my publications, please check my DBLP and Google Scholar profiles.


D.Phil in Computer Science
2018 - 2022
University of Oxford
Thesis Title: Learning and Inference over Relational Data
M.Sc. in Computer Science
2017 - 2018
University of Oxford
B.E. in Computer Engineering
2013 - 2017
Lebanese American University (LAU) , Minor: Mathematics