2026 Thomas Lukasiewicz: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	60	3261	3167	194	192	383	13306

Overview

Thomas Lukasiewicz is affiliated with the University of Oxford in the United Kingdom. Their primary field of study is Computer Science, with a focus on Artificial Intelligence.

Their research covers several subfields, including:

Artificial Intelligence
Computer Vision and Pattern Recognition
Cognitive Neuroscience
Radiology, Nuclear Medicine and Imaging
Electrical and Electronic Engineering

Key research topics they have engaged in include:

Topic Modeling
Natural Language Processing Techniques
Explainable Artificial Intelligence (XAI)
Advanced Neural Network Applications
Adversarial Robustness in Machine Learning
Multimodal Machine Learning Applications
Generative Adversarial Networks and Image Synthesis

Frequent coauthors of Thomas Lukasiewicz include:

Zhenghua Xu
Tommaso Salvatori
Oana-Maria Camburu
Yuhang Song
Rafał Bogacz

Their research has appeared in multiple publication venues, with the most frequent being:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
Computers in Biology and Medicine
Artificial Intelligence
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Selected recent papers by Thomas Lukasiewicz are:

Mathematical Capabilities of ChatGPT (2023), published in arXiv (Cornell University)
An Explainable Transformer-Based Deep Learning Model for the Prediction of Incident Heart Failure (2022), published in IEEE Journal of Biomedical and Health Informatics
Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records (2022), published in IEEE Journal of Biomedical and Health Informatics
Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation (2022), published in Medical Image Analysis
Inferring neural activity before plasticity as a foundation for learning beyond backpropagation (2024), published in Nature Neuroscience

Best Publications

Combining answer set programming with description logics for the Semantic Web

Thomas Eiter;Giovambattista Ianni;Thomas Lukasiewicz;Roman Schindlauer

764
Citations
A general Datalog-based framework for tractable query answering over ontologies

Andrea Calí;Georg Gottlob;Thomas Lukasiewicz

666
Citations
Combining answer set programming with description logics for the semantic web

Thomas Eiter;Thomas Lukasiewicz;Roman Schindlauer;Hans Tompits

638
Citations
Managing uncertainty and vagueness in description logics for the Semantic Web

Thomas Lukasiewicz;Umberto Straccia

625
Citations
e-SNLI: Natural Language Inference with Natural Language Explanations

Oana-Maria Camburu;Tim Rocktäschel;Thomas Lukasiewicz;Phil Blunsom

515
Citations
Mathematical Capabilities of ChatGPT

Unknown

470
Citations
Expressive probabilistic description logics

Thomas Lukasiewicz

319
Citations
Proceedings of the 7th International Symposium on the Foundations of Information and Knowledge Systems‚ FoIKS 2012‚ Kiel‚ Germany‚ March 5−9‚ 2012

Thomas Lukasiewicz

281
Citations
ManiGAN: Text-Guided Image Manipulation

Bowen Li;Xiaojuan Qi;Thomas Lukasiewicz;Philip H.S. Torr

272
Citations
P-SHOQ(D): A Probabilistic Extension of SHOQ(D) for Probabilistic Ontologies in the Semantic Web

Rosalba Giugno;Thomas Lukasiewicz

253
Citations
A general datalog-based framework for tractable query answering over ontologies

Andrea Calì;Georg Gottlob;Thomas Lukasiewicz

236
Citations
Complexity results for structure-based causality

Thomas Eiter;Thomas Lukasiewicz

219
Citations
Controllable Text-to-Image Generation

Bowen Li;Xiaojuan Qi;Thomas Lukasiewicz;Philip H. S. Torr

202
Citations
Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications

Andrea Calì;Georg Gottlob;Thomas Lukasiewicz;Bruno Marnette

186
Citations
Datalog±: a unified approach to ontologies and integrity constraints

Andrea Calì;Georg Gottlob;Thomas Lukasiewicz

164
Citations
Probabilistic Logic Programming

Thomas Lukasiewicz

150
Citations
Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records

Unknown

146
Citations
Probabilistic deduction with conditional constraints over basic events

Thomas Lukasiewicz

140
Citations
Probabilistic logic programming with conditional constraints

Thomas Lukasiewicz

129
Citations
Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation

Unknown

129
Citations
Probabilistic Default Reasoning with Conditional Constraints

Thomas Lukasiewicz

126
Citations
The MASTRO system for ontology-based data

Thomas Lukasiewicz;Diego Calvanese;Giuseppe De Giacomo;Domenico Lembo

2
Citations
A Unified Approach to Ontologies and Integrity Constraints

Andrea Calì;Georg Gottlob;Thomas Lukasiewicz

1
Citations

Frequent Co-Authors

Georg Gottlob University of Calabria

Thomas Eiter TU Wien

Tommaso Di Noia Polytechnic University of Bari

Phil Blunsom University of Oxford

Xiaojuan Qi University of Hong Kong

Philip H. S. Torr University of Oxford

Umberto Straccia National Research Council (CNR)

Heiner Stuckenschmidt University of Mannheim

Mai Xu Beihang University

Kathryn B. Laskey George Mason University

External Links

Personal website of Thomas Lukasiewicz Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens up a variety of online education and career opportunities. Many students consider continuing their studies with specialized programs that can be completed in less time, such as 1 year masters options. These programs are ideal for those looking to advance their careers quickly or gain expertise in a new area.

For professionals aiming for leadership roles or a broader business perspective, affordable online mba programs provide flexibility and cost-effective learning while opening doors to management positions in tech fields.

If your goal is to maximize earning potential, it's worth exploring highest paying online degrees. Many of these degrees are directly related to technology and engineering, offering excellent return on investment and career mobility.

As artificial intelligence continues to transform industries, specialized degrees in ai can help you gain high-demand skills. Online programs make it easier to balance education with other commitments as you build a future-ready career.