Artur d’Avila Garcez | City University London

Artur Garcez is Professor of Computer Science at City, University of London. He holds a Ph.D. in Computing (2000) from Imperial College London. He is a Fellow of the British Computer Society (FBCS), member of the ACM, AAAI, IEEE, CGCA, and partner at Cognitive Intelligence, London, and Performance Systems, Rio de Janeiro.

Garcez is Director of the Data Science Institute, and president of the Steering Committee of the Neural-Symbolic Learning and Reasoning Association, London. He was the founding course director of City's MSc in Data Science programme.

Garcez has an established track record of research in Machine Learning, Neural Computation and Artificial Intelligence. He has co-authored two books: Neural-Symbolic Cognitive Reasoning, with Lamb and Gabbay (Springer 2009), and Neural-Symbolic Learning Systems, with Broda and Gabbay (Springer 2002). Garcez has published at Theoretical Computer Science, Neural Computation, Machine Learning, Journal of Logic and Computation, Artificial Intelligence journal, Journal of Applied Logic, Behavioral & Brain Sciences, IEEE Transactions on Neural Networks and Learning Systems, and Studia Logica. He has consistently published at the flagship Artificial Intelligence and Neural Computation conferences AAAI, NeurIPS, IJCAI, AAMAS, IJCNN, ECAI.

Garcez is editor-in-chief of Neurosymbolic AI, IOS Press, editor of the Machine Learning journal, editor of the Journal of Logic and Computation (Reasoning and Learning, with L. Valiant), member of the editorial board of the Journal of AI Research (JAIR), and was associate editor of IEEE Transactions in Neural Networks and Learning Systems (2019-2023). Garcez is the founding co-chair of the International Conference on Neural-Symbolic Learning and Reasoning (NeSy), Springer LNCS, held yearly since 2005. He was co-organiser of Dagstuhl seminar 14381 on Neural-Symbolic Learning and Reasoning, September 2014, and Dagstuhl seminar 17192 on Human-like Neural-Symbolic Computing, May 2017.

Garcez has acted as reviewer for most of the leading international journals on Logic, Cognitive Science, Neural Computation and Artificial Intelligence, including IEEE Transactions on Neural Networks and Learning Systems, Artificial Intelligence, Machine Learning, Journal of Machine Learning Research, Cognitive Systems Research, AI Communications, Cognitive Science, Neurocomputing, Information and Computation. He has guest-edited three journal special issues, and co-edited two research monographs. He has served on the Senior Programme Committee or Organizing Committee of a large number of international conferences and workshops, including IJCAI, NeurIPS, AAAI, KR, AAMAS, ICLR, ECAI, ICANN, ASE, HLAI, IJCNN and NeSy.

Garcez was awarded a two-year Nuffield foundation research grant in the area of neural-symbolic integration (2002-2004). He was Principal Investigator for the EU-funded research project BioGrid (2003-2004) and industry-funded projects RoboCup Physical Visualization League (2007) and Dynamic Fraud Prevention (2009). He was co-investigator in the EU-funded research project Genestream (2003), was awarded a Daiwa Foundation Grant (2006), and has been consistently awarded conference travel grants by The Royal Society (2002, 2003, 2004, 2005, 2007, 2010). Garcez was Principal Investigator for the EPSRC/ESRC/Innovate UK project EP/M50712X/1 Advancing Consumer Protection through Machine Learning (with BetBuddy Ltd.), and the EPSRC/Innovate UK project EP/M507064/1 FareViz: On the Design of Real-Time Data Exploration Tools. Garcez is a member of the EPSRC Research Network+ on Human-like Computing, Principal or Co-Investigator in the projects: Smart Big Data Platform for Evidence-based Personalised Support for Healthy and Independent Living at Home (EU H2020, 2019-2023), Yuvoh Investment Analytics Platform (Innovate UK, 2020-2021), and industry-funded projects Deep Learning for Compliance and Fraud Prevention (Kindred plc, 2018-2021), Safety Validation and Explanation of Autonomous Vehicle Systems (Intel, 2019-2023) and Neurosymbolic AI for Trust and Accountability (Fujitsu Research, 2021-24).

Selected Publications (complete list available here; google scholar profile here):

S. Tran, A. S. d'Avila Garcez. Neurosymbolic Reasoning and Learning with Restricted Boltzmann Machines. Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Washington DC, USA, Feb 2023.

H. Stromfelt, L. Dickens, A. d'Avila Garcez, A. Russo. Formalizing Consistency and Coherence of Representation Learning. NeurIPS 2022, New Orleans, LA, Dec 2022.

K. Ngan, A. d’Avila Garcez, J. Townsend. Extracting Meaningful High-Fidelity Knowledge from Convolutional Neural Networks. IEEE WCCI 2022, IJCNN, Padua, Italy, July 2022.

S. Badreddine, A. d'Avila Garcez, L. Serafini and M. Spranger. Logic Tensor Networks. Artificial Intelligence, Vol. 303, Feb 2022.

L. C. Lamb, A. d'Avila Garcez, M. Gori, M. O. Prates, P. H. Avelar and M. Y. Vardi. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. In Proc. IJCAI 2020, Yokohama, Japan, July 2020.

I. Donadello, L. Serafini and A. S. d'Avila Garcez. Logic Tensor Networks for Semantic Image Interpretation. In Proc. IJCAI'17, Melbourne, Australia, Aug 2017.

T. Besold, A. S. d'Avila Garcez, K. Stenning, L. van der Torre and M. van Lambalgen. Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples. Minds and Machines, Springer. DOI:10.1007/s11023-017-9428-3, March 2017.

S. Tran and A. S. d'Avila Garcez. Deep Logic Networks: Inserting and Extracting Knowledge from Deep Belief Networks. IEEE Transactions on Neural Networks and Learning Systems. DOI 10.1109/TNNLS.2016.2603784, Nov 2016.

M. Franca, G. Zaverucha and A. S. d'Avila Garcez. Fast Relational Learning using Bottom Clause Propositionalization with Artificial Neural Networks, Machine Learning 94(1):81-104, Springer, 2014.

R. V. Borges, A. S. d'Avila Garcez and L. C. Lamb. Learning and Representing Temporal Knowledge in Recurrent Networks. IEEE Transactions on Neural Networks, 22(12):2409 - 2421, December 2011.

L. de Penning, A. S. d'Avila Garcez, L. C. Lamb and J. J. Meyer. A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning. In Proc. IJCAI'11, Barcelona, Spain, July 2011.

Artur S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay. Neural-Symbolic Cognitive Reasoning. Cognitive Technologies, Springer, ISBN 978-3-540-73245-7, 2009.

Artur S. d'Avila Garcez, D. M. Gabbay, O. Ray and J. Woods. Abductive Reasoning in Neural-Symbolic Learning Systems. Topoi: An International Review of Philosophy, 26:37-49, March 2007.

Artur S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay. Connectionist Modal Logic: Representing Modalities in Neural Networks. Theoretical Computer Science, 371(1-2):34-53, February 2007.

Artur S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay. Connectionist Computations of Intuitionistic Reasoning. Theoretical Computer Science, 358(1):34-55, July 2006.

Artur S. d'Avila Garcez and L. C. Lamb. A Connectionist Computational Model for Epistemic and Temporal Reasoning. Neural Computation, 18(7):1711-1738, July 2006.

Artur S. d'Avila Garcez, K. Broda and D. M. Gabbay. Neural-Symbolic Learning Systems: Foundations and Applications, Perspectives in Neural Computing, Springer, ISBN 1-85233-512-2, 2002.

Artur S. d'Avila Garcez, K. Broda and D. M. Gabbay. Symbolic Knowledge Extraction from Trained Neural Networks: A Sound Approach. Artificial Intelligence, 125(1-2):153-205, January 2001.

Contact details
Artur d'Avila Garcez, FBCS
Professor of Computer Science
Department of Computer Science
City, University of London, EC1V 0HB, UK
Tel: + 44 (0)20 7040 8344
Email: a.garcez@city.ac.uk
URL: http://staff.city.ac.uk/~aag/
Twitter: @AvilaGarcez