PeopleofSuperintelligence

Researcher

Artur d'Avila Garcez

Artur d'Avila Garcez works on neurosymbolic AI, which links learning in neural networks with symbolic reasoning. He earned a PhD in Computing at Imperial College London in 2000 and coauthored books on neural-symbolic learning in 2002 and neural-symbolic reasoning in 2009. He edited NeSy workshop proceedings in 2009, co-organized a 2014 Dagstuhl seminar, surveyed the field in 2020, and posted work on deep-learning semantics with Simon Odense in November 2025. In March 2025, he published a semantic framework for neurosymbolic computation, and in 2026 he coauthored the RAIL principles. As of October 2026, he is Professor of Computer Science at City St George's, University of London, and president of the NeSy Association.

Now at
City St George's, University of London and NeSy Association
Works
247
Citations
4,310
h-index
29
Badges

Publication numbers from OpenAlex, as of 18 October 2026.

Timeline

  1. 2000

    A PhD in computing

    He completes a PhD in Computing at Imperial College London in 2000.

  2. 2002

    Neural-Symbolic Learning Systems

    He coauthors Neural-Symbolic Learning Systems: Foundations and Applications in 2002.

  3. 2009

    Neural-Symbolic Cognitive Reasoning

    He coauthors Neural-Symbolic Cognitive Reasoning in 2009. He also edits the proceedings of the fifth NeSy workshop, held in Pasadena in 2009.

  4. 2014

    A Dagstuhl seminar

    He co-organizes Dagstuhl Seminar 14381 on Neural-Symbolic Learning and Reasoning in 2014. The seminar focuses on integrating machine learning, cognitive science, applied logic and visual information processing.

  5. 2020

    Neurosymbolic AI's third wave

    With Luis C. Lamb, he surveys past and current neurosymbolic AI research in 2020 and identifies ingredients for a next wave of AI systems. The paper links neural-network learning with symbolic knowledge representation and reasoning.

  6. 2025

    A semantic framework

    He publishes A Semantic Framework for Neurosymbolic Computation in Artificial Intelligence in March 2025. In November 2025, he and Simon Odense post Neurosymbolic Deep Learning Semantics, which uses logic to define semantics for deep learning.

  7. 2026

    The RAIL principles

    He coauthors the 2026 RAIL paper, which presents principles for reasoning, assurances, interfacing and learning in neurosymbolic AI.

  8. As of October 2026

    Professor and NeSy president

    As of October 2026, he is Professor of Computer Science at City St George's, University of London, and president of the NeSy Association. He has held a professorship at City since 2015.

Career

  1. Since 2015City St George's, University of LondonProfessor of Computer Science
  2. NowNeSy AssociationPresident
  3. 2017–2025University of London
  4. 2002–2015City, University of London
  5. 2001–2005Imperial College London

Education

  1. 2000Imperial College LondonPhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Most-cited works

  1. Neurosymbolic AI: the 3rd wave2023 · Artificial Intelligence Review · 509 citations
  2. Logic Tensor Networks2021 · Artificial Intelligence · 266 citations
  3. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation2017 · 242 citationsWith Pedro Domingos
  4. Symbolic knowledge extraction from trained neural networks: A sound approach2001 · Artificial Intelligence · 217 citations
  5. Logic Tensor Networks for Semantic Image Interpretation2017 · International Joint Conference on Artificial Intelligence · 179 citations

From OpenAlex, as of 18 October 2026.

  1. The company share of AI researchers’ top papers rose from 3% to 32%Includes City St George's, University of London. We estimate where leading AI researchers worked when their most-cited papers appeared. The company share rose from 3% in 1975–1979 to 32% in 2020–2024.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of London. Our estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.
  3. Google has the highest combined h-index among AI organizationsIncludes Imperial College London. Our estimates of combined and median h-index at AI organizations, using current affiliations and OpenAlex records.
  4. Stanford University trained or employed 27 of the 111 AI major-prize winners, more than any other universityIncludes Imperial College London. Stanford University trained or employed 27 of the 111 AI major-prize winners, more than any other university. We compare doctorate and employment links for major-prize and best-paper winners.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Artur d’Avila Garcez | City University London staff.city.ac.uk. Checked 18 October 2026.
  2. Artur d’Avila Garcez | City University London staff.city.ac.uk. Checked 18 October 2026.
  3. CEUR-WS.org/Vol-481 - Neural Symbolic Learning and Reasoning, NeSy'09 ceur-ws.org. Checked 18 October 2026.
  4. Dagstuhl Seminar 14381: Neural-Symbolic Learning and Reasoning dagstuhl.de. Checked 18 October 2026.
  5. [2012.05876] Neurosymbolic AI: The 3rd Wave arxiv.org. Checked 18 October 2026.
  6. [2511.02825] Neurosymbolic Deep Learning Semantics arxiv.org. Checked 18 October 2026.
  7. [2608.04285] The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning arxiv.org. Checked 18 October 2026.
  8. Artur d’Avila Garcez | City University London staff.city.ac.uk. Checked 18 October 2026.
  9. Neurosymbolic AI (NeSy) Association website nesy-ai.org. Checked 18 October 2026.