Ismail Ilkan Ceylan
Ismail Ilkan Ceylan
Wolfson Building, Parks Road, Oxford OX1 3QD
Themes:
- Artificial Intelligence and Machine Learning
- Algorithms and Complexity Theory
- Data, Knowledge and Action
Completed Projects:
Interests
My research interests are broadly in AI & machine learning with a particular focus on graph machine learning, which includes a class of challenging problems that can be naturally characterized using relational structures. The goal is to more efficiently and reliably learn from relational patterns and reason over them. This is a highly interactive field, where techniques from machine learning (e.g., deep learning, graph representation learning, probabilistic methods), knowledge representation (e.g., logical reasoning), and theoretical computer science (complexity theory, graph theory) are relevant. These methods are applied in a wide range of domains, ranging from systems in life-sciences (e.g., physical, chemical, and biological systems) to social networks.
Note: If you are interested in applying for a DPhil, it might be helpful to have a brief look at my graph representation learning course.
Selected Publications
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Homomorphism Counts as Structural Encodings for Graph Learning
Linus Bao‚ Emily Jin‚ Michael Bronstein‚ Ismail Ilkan Ceylan and Matthias Lanzinger
In Proceedings of the Thirteeneth International Conference on Learning Representations (ICLR). 2025.
Details about Homomorphism Counts as Structural Encodings for Graph Learning | BibTeX data for Homomorphism Counts as Structural Encodings for Graph Learning
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Explanations for Query Answers under Existential Rules
İsmail İlkan Ceylan‚ Thomas Lukasiewicz‚ Enrico Malizia and Andrius Vaicenavičius
In Artificial Intelligence. 2024.
Conditionally accepted for publication
Details about Explanations for Query Answers under Existential Rules | BibTeX data for Explanations for Query Answers under Existential Rules
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Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis‚ Samuel Kessler‚ Mircea Petrache‚ İsmail İlkan Ceylan‚ Michael Bronstein and Avishek Joey Bose
In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS). 2024.
Details about Fisher Flow Matching for Generative Modeling over Discrete Data | BibTeX data for Fisher Flow Matching for Generative Modeling over Discrete Data | Link to Fisher Flow Matching for Generative Modeling over Discrete Data