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Department of Computer Science and Technology

  • Professor of Artificial Intelligence

I am a full Professor of Artificial Intelligence in the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge, UK. I am also an associate fellow at the Leverhulme Centre for the Future of Intelligence. Recently I served as Specialist Adviser to the House of Lords Select Committee on Artificial Intelligence. I founded the women@CL initiative.

More information is on my personal homepage.

 

Research

In my work I want to build AI systems that work in more human-like ways. I explore how people represent their problems and how they solve them using informal techniques. I combine AI reasoning with machine learning techniques in order to obtain explainable model predictions as well as to be able to apply them in applications where it is important to understand the machine's output. In particular, I apply this theoretical work to clinical decision support systems in personalised cancer medicine as well as in education to personalised tutoring systems.

Broadly, my research is in the areas of artificial intelligence, human-like computation, machine learning, automated reasoning, diagrammatic reasoning, knowledge representation, theorem proving, cognitive science, human-computer interaction.

I am a member of the Artificial Intelligence research group and also of the Programming, Logic, and Semantics Group.

Publications

Book chapters

  • Hemker, K., Shams, Z. and Jamnik, M., 2023. CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs
    Doi: 10.1007/978-3-031-39539-0_6
  • Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Liò, P., 2023. Concept Distillation in Graph Neural Networks
    Doi: 10.1007/978-3-031-44070-0_12
  • Cheng, PCH., Stockdill, A., Garcia Garcia, G., Raggi, D. and Jamnik, M., 2022. Representational Interpretive Structure: Theory and Notation
    Doi: 10.1007/978-3-031-15146-0_4
  • McGrath, S., Blake, A., Stapleton, G., Touloumis, A., Chapman, P., Jamnik, M. and Shams, Z., 2022. Evaluating Colour in Concept Diagrams
    Doi: 10.1007/978-3-031-15146-0_14
  • Jamnik, M., 2021. Logical Reasoning with Diagrams
    Doi: 10.7551/mitpress/11252.003.0078
  • Jamnik, M. and Cheng, P., 2021. Endowing Machines with the Expert Human Ability to Select Representations: Why and How
    Doi: http://doi.org/10.1093/oso/9780198862536.003.0018
  • Jamnik, M., 2021. Logical reasoning with diagrams
  • Jamnik, M., 2021. Human-Like Computational Reasoning: Diagrams and Other Representations
    Doi: 10.1007/978-3-030-77879-8_7
  • Jamnik, M., Bundy, A. and Green, I., 2002. On Automating Diagrammatic Proofs of Arithmetic Arguments
    Doi: 10.1007/978-1-4471-0109-3_18
  • Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG. and Cheng, PCH., Considerations in Representation Selection for Problem Solving: A Review
    Doi: 10.1007/978-3-030-86062-2_4
  • Cheng, PCH., Garcia, GG., Raggi, D., Stockdill, A. and Jamnik, M., Cognitive Properties of Representations: A Framework
    Doi: 10.1007/978-3-030-86062-2_43
  • Colarusso, F., Cheng, PCH., Garcia, GG., Raggi, D. and Jamnik, M., Observing Strategies of Drawing Data Representations
    Doi: 10.1007/978-3-030-86062-2_55
  • Journal articles

  • Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Lio, P., 2022. Encoding Concepts in Graph Neural Networks
  • Wijaya, MA., Kazhdan, D., Dimanov, B. and Jamnik, M., 2021. Failing Conceptually: Concept-Based Explanations of Dataset Shift
  • Kazhdan, D., Dimanov, B., Jamnik, M. and Liò, P., 2020. MEME: Generating RNN Model Explanations via Model Extraction
  • Margeloiu, A., Simidjievski, N., Jamnik, M. and Weller, A., 2020. Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
  • Kiat, NQW., Wang, D. and Jamnik, M., 2020. Pairwise Relations Discriminator for Unsupervised Raven's Progressive Matrices
  • Scherer, P., Trȩbacz, M., Simidjievski, N., Shams, Z., Terre, HA., Liò, P. and Jamnik, M., 2020. Incorporating network based protein complex discovery into automated model construction
  • Zhao, Y., Wang, D., Bates, D., Mullins, R., Jamnik, M. and Lio, P., 2020. Learned Low Precision Graph Neural Networks
  • Wang, D., Jamnik, M. and Lio, P., 2020. ABSTRACT DIAGRAMMATIC REASONING WITH MULTIPLEX GRAPH NETWORKS 8th International Conference on Learning Representations, ICLR 2020,
  • Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P. and Weller, A., 2020. Now You See Me (CME): Concept-based Model Extraction. CoRR, v. abs/2010.13233
  • Wang, D., Jamnik, M. and Lio, P., 2019. Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat
  • Słowik, A., Mangla, C., Jamnik, M., Holden, SB. and Paulson, LC., 2019. Bayesian Optimisation with Gaussian Processes for Premise Selection
  • Scherer, P., Andres-Terre, H., Lio, P. and Jamnik, M., 2019. Decoupling feature propagation from the design of graph auto-encoders
  • Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M. and Liò, P., 2019. Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice. Front Genet, v. 10
    Doi: 10.3389/fgene.2019.01205
  • Linker, S., Burton, J. and Jamnik, M., 2017. Tactical diagrammatic reasoning Electronic Proceedings in Theoretical Computer Science, EPTCS, v. 239
    Doi: http://doi.org/10.4204/EPTCS.239.3
  • Stapleton, G., Jamnik, M. and Shimojima, A., 2017. What Makes an Effective Representation of Information: A Formal Account of Observational Advantages Journal of Logic, Language and Information, v. 26
    Doi: 10.1007/s10849-017-9250-6
  • Urbas, M., Jamnik, M. and Stapleton, G., 2015. Speedith: A Reasoner for Spider Diagrams Journal of Logic, Language and Information, v. 24
    Doi: 10.1007/s10849-015-9229-0
  • Sarkar, A., Blackwell, AF., Jamnik, M. and Spott, M., 2014. Teach and try: A simple interaction technique for exploratory data modelling by end users Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC,
    Doi: http://doi.org/10.1109/VLHCC.2014.6883022
  • Stapleton, G., Jamnik, M. and Urbas, M., 2013. Designing inference rules for spider diagrams Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC,
    Doi: http://doi.org/10.1109/VLHCC.2013.6645238
  • Benzmüller, C., Sorge, V., Jamnik, M. and Kerber, M., 2008. Combined reasoning by automated cooperation Journal of Applied Logic, v. 6
    Doi: http://doi.org/10.1016/j.jal.2007.06.003
  • Jamnik, M., 2008. How can machines reason? AISB 2008 Convention: Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Logic and the Simulation of Interaction and Reasoning,
  • Dennis, LA., Jamnik, M. and Pollet, M., 2006. On the comparison of proof planning systems: γcLAM Ωmega and IsaPlanner Electronic Notes in Theoretical Computer Science, v. 151
    Doi: http://doi.org/10.1016/j.entcs.2005.11.025
  • Jamnik, M. and Bundy, A., 2005. Psychological validity of schematic proofs LECT NOTES ARTIF INT, v. 2605
  • Bundy, A., Jamnik, M. and Fugard, A., 2005. What is a proof? PHILOS T ROY SOC A, v. 363
    Doi: http://doi.org/10.1098/rsta.2005.1651
  • Jamnik, M. and Janičić, P., 2003. Learning strategies for mechanised building of decision procedures Electronic Notes in Theoretical Computer Science, v. 86
    Doi: http://doi.org/10.1016/S1571-0661(04)80662-5
  • Jamnik, M., Kerber, M., Pollet, M. and Benzmuller, C., 2003. Automatic learning of proof methods in proof planning LOGIC J IGPL, v. 11
    Doi: http://doi.org/10.1093/jigpal/11.6.647
  • Jamnik, M., Bundy, A. and Green, I., 1999. On automating diagrammatic proofs of arithmetic arguments Journal of Logic, Language and Information, v. 8
    Doi: http://doi.org/10.1023/A:1008323427489
  • Benzmüller, C., Jamnik, M., Kerber, M. and Sorge, V., 1999. Agent based mathematical reasoning Electronic Notes in Theoretical Computer Science, v. 23
    Doi: http://doi.org/10.1016/S1571-0661(05)82522-8
  • Bundy, A. and Jamnik, M., A Common Type of Rigorous Proof that Resists Hilbert’s Programme Proof Technology in Mathematics Research and Teaching,
  • Trębacz, M., Shams, Z., Jamnik, M., Scherer, P., Simidjievski, N., Terre, HA. and Liò, P., Using ontology embeddings for structural inductive bias in gene expression data analysis arxiv,
  • Sato, Y., Stapleton, G., Jamnik, M. and Shams, Z., Human inference beyond syllogisms: an approach using external graphical representations Cognitive Processing,
  • Scherer, P., Trębacz, M., Simidjievski, N., Viñas, R., Shams, Z., Terre, HA., Jamnik, M. and Liò, P., Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics,
    Doi: 10.1093/bioinformatics/btab830
  • Oliver, I., Howse, J., Gem, S., Shams, Z. and Jamnik, M., Exploring and Conceptualising Attestation International Conference on Conceptual Structures ICCS2019,
  • Conference proceedings

  • Raggi, D., Stapleton, G., Jamnik, M., Stockdill, A., Garcia, GG. and Cheng, PCH., 2022. Oruga: An Avatar of Representational Systems Theory CEUR Workshop Proceedings, v. 3227
  • Stockdill, A., Garcia, GG., Cheng, PCH., Raggi, D. and Jamnik, M., 2022. Cognitive Analysis for Representation Change CEUR Workshop Proceedings, v. 3227
  • Stockdill, A., Stapleton, G., Raggi, D., Jamnik, M., Garcia, GG. and Cheng, PCH., 2022. Examining Experts' Recommendations of Representational Systems for Problem Solving Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2022-September
    Doi: http://doi.org/10.1109/VL/HCC53370.2022.9833141
  • Apperly, I., Bundy, A., Cohn, A., Colton, S., Cussens, J., D'Avila Garcez, A., Hahn, U., Jamnik, M., Jay, C., Mareschal, D., Sammut, C., Schmid, U., Seed, A., Stahl, BC., Steedman, M. and Tamaddoni-Nezhad, A., 2022. Preface CEUR Workshop Proceedings, v. 3227
  • Słowik, A., Bottou, L., Holden, SB. and Jamnik, M., 2022. On the Relation between Distributionally Robust Optimization and Data Curation (Student Abstract) Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, v. 36
  • Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, M., Jamnik, M. and Lio, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations CEUR Workshop Proceedings, v. 3318
  • Deslis, D., Stylianides, A. and Jamnik, M., 2021. Primary school teachers’ mathematical knowledge for Lakatos-style proof instruction Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education, v. 2
  • Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
  • Ayers, EW., Jamnik, M. and Gowers, WT., 2021. A graphical user interface framework for formal verification Leibniz International Proceedings in Informatics, LIPIcs, v. 193
    Doi: http://doi.org/10.4230/LIPIcs.ITP.2021.4
  • Dimanov, B., Bhatt, U., Jamnik, M. and Weller, A., 2020. You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods. Frontiers in Artificial Intelligence and Applications: ECAI 2020,
    Doi: 10.3233/FAIA200380
  • Wang, D., Jamnik, M. and Lio, P., 2020. Abstract Diagrammatic Reasoning with Multiplex Graph Networks
  • Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG., Sutherland, HEA., Cheng, PCH. and Sarkar, A., 2020. Cross-domain correspondences for explainable recommendations CEUR Workshop Proceedings, v. 2582
  • Raggi, D., Stockdill, A., Jamnik, M., Garcia Garcia, G., Sutherland, HEA. and Cheng, PCH., 2020. Dissecting Representations Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12169 LNAI
    Doi: 10.1007/978-3-030-54249-8_11
  • Slowik, A., Mangla, C., Jamnik, M., Holden, SB. and Paulson, LC., 2020. Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), v. 34
    Doi: 10.1609/aaai.v34i10.7232
  • Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG., Sutherland, HEA., Cheng, PCH. and Sarkar, A., 2020. Correspondence-based analogies for choosing problem representations Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2020-August
    Doi: 10.1109/VL/HCC50065.2020.9127258
  • Cheng, PC-H., Garcia, GG., Sutherland, HEA., Raggi, D., Stockdill, A. and Jamnik, M., 2019. Elucidating the Cognitive Anatomy of Representation Systems. CogSci,
  • Raggi, D., Stockdill, A., Jamnik, M., Garcia Garcia, G., Sutherland, HEA. and Cheng, PCH., 2019. Inspection and Selection of Representations Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11617 LNAI
    Doi: 10.1007/978-3-030-23250-4_16
  • Ayers, EW., Gowers, WT. and Jamnik, M., 2019. A Human-Oriented Term Rewriting System Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11793 LNAI
    Doi: http://doi.org/10.1007/978-3-030-30179-8_6
  • Sato, Y., Stapleton, G., Jamnik, M. and Shams, Z., 2018. Deductive reasoning about expressive statements using external graphical representations. CogSci,
  • Sato, Y., Stapleton, G., Jamnik, M., Shams, Z. and Blake, A., 2017. How network-based and set-based visualizations aid consistency checking in ontologies ACM International Conference Proceeding Series, v. Part F130152
    Doi: http://doi.org/10.1145/3105971.3105988
  • Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., 2017. Reasoning with concept diagrams about antipatterns in ontologies Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10383 LNAI
    Doi: http://doi.org/10.1007/978-3-319-62075-6_18
  • 2016. Diagrammatic Representation and Inference - 9th International Conference, Diagrams 2016, Philadelphia, PA, USA, August 7-10, 2016, Proceedings Diagrams, v. 9781
  • Stapleton, G., Jamnik, M. and Shimojima, A., 2016. Effective representation of information: Generalizing free rides Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9781
    Doi: 10.1007/978-3-319-42333-3_28
  • Sarkar, A., Spott, M., Blackwell, AF. and Jamnik, M., 2016. Visual discovery and model-driven explanation of time series patterns Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2016-November
    Doi: http://doi.org/10.1109/VLHCC.2016.7739668
  • Sarkar, A., Jamnik, M., Blackwell, AF. and Spott, M., 2015. Interactive visual machine learning in spreadsheets Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2015-December
    Doi: 10.1109/VLHCC.2015.7357211
  • Urbas, M. and Jamnik, M., 2014. A framework for heterogeneous reasoning in formal and informal domains Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8578 LNAI
    Doi: http://doi.org/10.1007/978-3-662-44043-8_28
  • Urbas, M. and Jamnik, M., 2012. Diabelli: A Heterogeneous Proof System. IJCAR, v. 7364
  • Urbas, M., Jamnik, M., Stapleton, G. and Flower, J., 2012. Speedith: A Diagrammatic Reasoner for Spider Diagrams. Diagrams, v. 7352
  • Urbas, M. and Jamnik, M., 2011. Heterogeneous proofs: Spider diagrams meet higher-order provers Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6898 LNCS
    Doi: http://doi.org/10.1007/978-3-642-22863-6_29
  • Urbas, M. and Jamnik, M., 2010. Heterogeneous Reasoning in Real Arithmetic DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 6170
  • Ridsdale, M., Jamnik, M., Benton, N. and Berdine, J., 2008. Diagrammatic reasoning in separation logic Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5223 LNAI
    Doi: http://doi.org/10.1007/978-3-540-87730-1_50
  • Benzmuller, C., Sorge, V., Jamnik, M. and Kerber, M., 2005. Can a higher-order and a first-order theorem prover cooperate? LOGIC FOR PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND REASONING, PROCEEDINGS, v. 3452
  • Winterstein, D., Bundy, A., Gurr, C. and Jamnik, M., 2004. An experimental comparison of diagrammatic and algebraic logics DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 2980
  • Winterstein, D., Bundy, A. and Jamnik, M., 2004. On differences between the real and physical plane DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 2980
  • Jamnik, M., Kerber, M. and Pollet, M., 2002. Learn Omega-matic: System Description. CADE, v. 2392
  • Jamnik, M., Kerber, M. and Pollet, M., 2002. Automatic learning in proof planning ECAI 2002: 15TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, v. 77
  • Winterstein, D., Bundy, A., Gurr, C. and Jamnik, M., 2002. Using animation in diagrammatic theorem proving DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 2317
  • Jamnik, M., Kerber, M. and Benzmuller, C., 2001. Towards learning new methods in proof planning SYMBOLIC COMPUTATION AND AUTOMATED REASONING,
  • Benzmüller, C., Jamnik, M., Kerber, M. and Sorge, V., 2001. Experiments with an Agent-Oriented Reasoning System. KI/ÖGAI, v. 2174
  • Benzmuller, C., Jamnik, M., Kerber, M. and Sorge, V., 2001. Resource guided concurrent deduction SYMBOLIC COMPUTATION AND AUTOMATED REASONING,
  • Winterstein, D., Bundy, A. and Jamnik, M., 2000. A proposal for automating diagrammatic reasoning in continuous domains THEORY AND APPLICATION OF DIAGRAMS, PROCEEDINGS, v. 1889
  • Jamnik, M., 1997. Automation of Diagrammatic Proofs in Mathematics. IJCAI,
  • Jamnik, M., Bundy, A. and Green, P., 1997. Automation of diagrammatic reasoning IJCAI-97 - PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2,
  • Wang, D., Jamnik, M. and Lio, P., Investigating diagrammatic reasoning with deep neural networks
    Doi: 10.1007/978-3-319-91376-6_36
  • Słowik, A., Mangla, C., Jamnik, M., Holden, S. and Paulson, L., Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving EPiC Series in Computing,
    Doi: 10.29007/q91g
  • Collins, K., Human Uncertainty in Concept-Based AI Systems Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society,
    Doi: http://doi.org/10.1145/3600211.3604692
  • Yang, M., Stylianides, A. and Jamnik, M., Chinese teachers’ professional noticing of students’ reasoning in the context of Lakatos-style proving activity.
  • Colarusso, F., Cheng, P., Garcia Garcia, G., Stockdill, A., Raggi, D. and Jamnik, M., A novel interaction for competence assessment using micro-behaviors: Extending CACHET to graphs and charts
    Doi: http://doi.org/10.1145/3544548.3581519
  • Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., iCon: A Diagrammatic Theorem Prover for Ontologies Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference (KR2018),
  • Stockdill, A., Stapleton, G., Raggi, D., Jamnik, M., Garcia Garcia, G. and Cheng, P., Examining experts’ recommendations of representational systems for problem solving
  • Cheng, P., Stockdill, A., Garcia Garcia, G., Raggi, D. and Jamnik, M., Representational interpretive structure: Theory and notation International Conference on Theory and Application of Diagrams,
  • McGrath, S., Blake, A., Stapleton, G., Touloumis, A., Chapman, P., Jamnik, M. and Shams, Z., Evaluating colour in concept diagrams Diagrammatic Representation and Inference, 13th International Conference, Diagrams 2022, v. LNAI 13462
    Doi: http://doi.org/10.1007/978-3-031-15146-0_14
  • Deslis, D., Stylianides, A. and Jamnik, M., Two Primary School Teachers’ Mathematical Knowledge of Content, Students, and Teaching Practices relevant to Lakatos-style Investigation of Proof Tasks
  • Scherer, P., Lio, P. and Jamnik, M., Distributed representations of graphs for drug pair scoring Proceedings of the First Learning on Graphs Conference (LoG 2022), v. PMLR 198
  • Wu, Y., Jiang, Q., Li, W., Rabe, M., Staats, C., Jamnik, M. and Szegedy, C., Autoformalization with large language models https://openreview.net/pdf?id=IUikebJ1Bf0,
  • Jiang, Q., Li, W., Tworkowski, S., Czechowski, K., Odrzygózdz, T., Miłos ́, P., Wu, Y. and Jamnik, M., Thor: Wielding hammers to integrate language models and automated theorem provers Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2022,
  • Espinosa Zarlenga, M., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P. and Jamnik, M., Concept embedding models: Beyond the Accuracy-Explainability Trade-Off
  • Jiang, Q., Welleck, S., Zhou, JP., Li, W., Liu, J., Jamnik, M., Lacroix, T., Lample, G. and Wu, Y., Draft, sketch, and prove: Guiding formal theorem provers with informal proofs https://openreview.net/pdf?id=SMa9EAovKMC,
  • Margeloiu, A., Ashman, M., Bhatt, U., Chen, Y., Jamnik, M. and Weller, A., Do Concept Bottleneck Models Learn as Intended?
  • Espinosa Zarlenga, M., Barbiero, P., Shams, Z., Kazhdan, D., Bhatt, U., Weller, A. and Jamnik, M., Towards robust metrics for concept representation evaluation AAAI Press,
  • Margeloiu, A., Simidjievski, N., Lio, P. and Jamnik, M., Weight predictor network with feature selection for small sample tabular biomedical data
  • Sarkar, A., Blackwell, A., Jamnik, M. and Spott, M., Interaction with uncertainty in visualisations
  • Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., Reasoning with Concept Diagrams about Antipatterns Kalpa Publications in Computing,
    Doi: 10.29007/4ckv
  • Shams, Y., Jamnik, M., Stapleton, G. and Sato, Y., Reasoning with concept diagrams about antipatterns
    Doi: http://doi.org/10.1007/978-3-319-62075-6_18
  • Kazhdan, D., Dimanov, B., Jamnik, M., Lio, P. and Weller, A., Now You See Me (CME): Concept-based Model Extraction
  • Zarlenga, ME., Shams, Z. and Jamnik, M., Efficient Decompositional Rule Extraction for Deep Neural Networks Advances in Neural Information Processing Systems,
  • Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., Accessible Reasoning with Diagrams: from Cognition to Automation
  • Raggi, D., Stapleton, G., Stockdill, A., Jamnik, M., Garcia Garcia, G. and Cheng, P., How to (Re)represent it?
  • Stapleton, G., Shimojima, A. and Jamnik, M., The Observational Advantages of Euler Diagrams with Existential Import
  • Slowik, A., Mangla, C., Jamnik, M., Holden, S. and Paulson, L., Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract) Proceedings of the AAAI Conference on Artificial Intelligence,
  • Theses / dissertations

  • Ayers, E., 2022. A Tool for Producing Verified, Explainable Proofs
  • Stockdill, A., 2022. Automating representation change across domains for reasoning
  • Dimanov, B., Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
  • Wang, D., Neural Diagrammatic Reasoning
  • Internet publications

  • Słowik, A., Gupta, A., Hamilton, WL., Jamnik, M. and Holden, SB., 2020. Towards Graph Representation Learning in Emergent Communication
  • Słowik, A., Gupta, A., Hamilton, WL., Jamnik, M., Holden, SB. and Pal, C., 2020. Structural Inductive Biases in Emergent Communication
  • Zhao, Y., Wang, D., Gao, X., Mullins, R., Lio, P. and Jamnik, M., 2020. Probabilistic Dual Network Architecture Search on Graphs
  • Jamnik, M., Home Page
  • Books

  • Jamnik, M., Uesaka, Y. and Schwartz, SE., 2016. Preface
  • 2012. Diagrammatic Representation and Inference
    Doi: 10.1007/978-3-642-31223-6
  • 2012. Automated Reasoning
    Doi: 10.1007/978-3-642-31365-3
  • Jamnik, M., Goel, AK. and Narayanan, NH., 2010. Preface
  • 2010. Diagrammatic Representation and Inference
    Doi: 10.1007/978-3-642-14600-8
  • Jamnik, M., Goel, A. and Narayanan, H., 2010. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
  • 2010. Diagrammatic Representation and Inference, 6th International Conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010. Proceedings
  • Contact Details

    Room: 
    FC18
    Office phone: 
    (01223) 7-63587
    Email: 

    mateja.jamnik@cl.cam.ac.uk