We’re delighted to announce that Matthias Lanzinger has received an Outstanding Reviewer Award at Conference on Neurosymbolic Learning and Reasoning 2025!

The award honors the reviewers whose feedback extends beyond careful evaluation to make a meaningful impact on the quality of submitted research. It celebrates reviews that are both insightful and constructive—identifying a paper’s key strengths and weaknesses while offering clear, detailed guidance for improvement. Recipients are recognized for the depth and clarity of their feedback, their thoughtful engagement with authors’ ideas, and their role in upholding the scientific rigor and collegial spirit of the review process.

The Conference on Neurosymbolic Learning and Reasoning (Nesy) is the leading forum for advancing the theory and practice of neurosymbolic computing, fostering an open and collaborative community of researchers and practitioners at the intersection of deep learning and symbolic AI. It focuses on the integration of deep learning and symbolic AI, uniting neural network–based statistical learning with symbolic knowledge representation and reasoning. By combining these strengths, neurosymbolic AI seeks to develop computational models, systems, and applications that integrate data-driven learning with structured reasoning, achieving capabilities neither paradigm can realize alone.

Congratulations, Matthias, on this excellent achievement!

Curious about Matthias? Read more about him and his research in our #5QW interview series.

About Matthias Lanzinger

Matthias Lanzinger is an Assistant Professor in the Research Unit Databases and Artificial Intelligence at TU Wien Informatics. His research lies at the intersection of logic, algorithms, and machine learning, exploring how structural insights can make intelligent systems more efficient, transparent, and reliable.

Matthias studies how the underlying abstract structure of data shapes reasoning and learning, and how this understanding can be used to design AI systems that scale gracefully to real-world complexity. His work bridges rigorous theoretical foundations with modern data-driven approaches, aiming to bring structure and interpretability into the core of machine intelligence.

Before joining TU Wien, Matthias was a Senior Research Associate at the University of Oxford. His research has been published in leading venues in theoretical computer science and artificial intelligence, and he is currently co-PI of the Vienna Science and Technology Fund project DeConquer on scalable reasoning in complex data systems.

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