Dr Alireza Tamaddoni-Nezhad
About
Biography
Alireza joined the Department of Computer Science in 2017 as Lecturer in Machine Learning and Computational Intelligence. Prior to this appointment, he was a Research Fellow at the Department of Computing, Imperial College London. He completed his PhD in Computer Science (Machine Learning) at Imperial College where he also worked as a Research Associate on several research projects applying Machine Learning to real-world problems from Biology, Medicine and Ecology. His principal research interests are in the areas of Artificial Intelligence and Machine Learning, in particular the theory, implementations and real-world applications of Relational and Logic-based Machine Learning (also known as Inductive Logic Programming). His current research interests also include: Third Wave AI and Human-Like Computing (e.g. Human-Machine Learning), Explainable AI (e.g. Comprehensible Machine Learning), Data Science, Automated Scientific Discovery, Computational Systems Biology and Bioinformatics, Machine Learning of Biological Networks, and Genetic and Evolutionary Computation.
He was Co-Director of the Surrey Institute for People-Centred AI and currently leading the 'Trustworthy AI' and 'Sustainable AI' research themes at the AI Institute. He is also Co-Investigator and Technical Director of the EPSRC NetworkPlus Grant on Human-Like Computing (HLC) to support the development of a UK-wide multi-disciplinary community of researchers within the EPSRC priority area of Human-Like Computing.
Alireza has been collaborating with researchers within academia and industry and played a central role in developing several Machine Learning approaches for a range of applications in Data Science and Systems Biology (from genetic & metabolic networks to ecosystems). For example, his machine learning and text-mining approach for automated discovery & validation of probabilistic interaction networks from ecological data is regarded as the first successful application of machine learning from large-scale agricultural data. The results include novel food-web hypotheses, some confirmed by subsequent experimental studies (e.g. DNA analysis). Similarly, his work on machine learning of metabolic networks was regarded as the first successful application of machine learning for modeling inhibition in metabolic networks and the method developed in this project was used in studies by other researchers. The results of his research and the above mentioned collaborations have been published in Computer Science journals and proceedings (e.g. Machine Learning Journal) as well as high impact Biological and Ecological Journals (e.g. Advances in Ecological Research, IF=6.3; Trends in Ecology and Evolution, IF=16.7; and Nature Ecology & Evolution).
He is currently Lead Guest Editor for Machine Learning Journal, Special Issue on Learning and Reasoning
I am looking for PhD students and postdocs with interest and background in AI and Machine Learning. Please contact me for details.
Publications
[NEW Book chapter] A. Tamaddoni-Nezhad, D.A. Bohan, G. A. Milani, A. Raybould, S. Muggleton, Human-Machine Scientific Discovery. In Human-Like Machine Intelligence, Oxford University Press, 279-315, 2021.
Journal articles
[1] D. A. Ma, X. Lu, C. Gray, A. Raybould, A. Tamaddoni-Nezhad, G. Woodward, D. Bohan. Ecological networks reveal resilience of agro-ecosystems to changes in farming management. Nature Ecology & Evolution, 3:260-264, 2019
[2] S.H. Muggleton, W-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen and Z-H. Zhou. Meta-interpretive learning from noisy images. Machine Learning, 107:1097-1118, 2018.
[3] S.H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold. Ultra-strong machine learning - comprehensibility of programs learned with ILP. Machine Learning, 107:1119-1140, 2018
[4] D. Bohan, C. Vacher, A. Tamaddoni-Nezhad, A. Raybould, A. Dumbrell and G. Woodward, Next- Generation Global Biomonitoring – Large-scale, automated reconstruction of ecological networks. Trends in Ecology and Evolution, 32(7), 477-487, 2017
[5] C. Vacher, A. Tamaddoni-Nezhad, S. Kamenova, N. Peyrard, Y. Moalic, R. Sabbadin, L. Schwaller, J. Chiquet, M. Smith, J. Vallance, V. Fievet, B. Jakuschkin, D. A. Bohan, Learning Ecological Networks from Next-Generation Sequencing Data. Advances in Ecological Research, 54, 1-39, 2016.
[6] M. Pocock, D. Evans, C. Fontaine, M. Harvey, R. Julliard, Ó. McLaughlin, J. Silvertown, A. Tamaddoni-Nezhad, P. White, and D. Bohan, The visualisation of ecological networks, and their use as a tool for engagement, advocacy and management, Advances in Ecological Research, 54, 1-39, 2016.
[7] QUINTESSENCE Consortium. Networking our way to better Ecosystem Service provision. Trends in Ecology and Evolution, 31(2):105-115, 2016.
[8] S.H. Muggleton, D. Lin and A. Tamaddoni-Nezhad. Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited, Machine Learning, 100(1):49-73, 2015.
[9] S.H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad. Meta-Interpretive Learning: application to Grammatical Inference. Machine Learning, 94:25-49, 2014
[10] A. Tamaddoni-Nezhad, G. Afroozi Milani, A. Raybould, S. Muggleton and D.Bohan, Construction and Validation of Agricultural Food-webs using Logic-based Machine Learning and Text-mining, Advances in Ecological Research, Vol 49, pages 224-290, Academic Press, Amsterdam, 2013
[11] D. Bohan, A. Raybould, C. Mulder, G. Woodward, A. Tamaddoni-Nezhad, N. Bluthgen, M. Pocock, S. Muggleton, D. Evans, J. Astegiano, F. Massol, N. Loeuille, S. Petit and S. Macfadyen. Networking agroecology: Integrating the diversity of agroecosystem interactions Advances in Ecological Research, Vol 49, pages 2-67. Academic Press, Amsterdam, 2013
[12] M. Sternberg, A. Tamaddoni-Nezhad, V. Lesk, E. Kay, P. Hitchen, A. Cootes, L. Alphen, M. Lamoureux, H. Jarrell, C. Rawlings, E. Soo, C. Szymanski, A. Dell, B. Wren, S. Muggleton. Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach. Journal of Molecular Biology, 425(1):186-197, 2013
[13] D. A. Bohan, G. Caron-Lormier, S. Muggleton, A. Raybould and A. Tamaddoni-Nezhad. Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning. PloS One, vol. 6, pp. e29028, 2011.
[14] E Kay, V Lesk, A. Tamaddoni-Nezhad, P Hitchen, A Dell, M. Sternberg, S. Muggleton, B Wren. Systems analysis of bacterial glycomes. Biochemical Society Transactions, volume 38, issue 5, pp.1290–1293, 2010.
[15] A. Tamaddoni-Nezhad and S.H. Muggleton. The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause. Machine Learning, 76(1):37-72, 2009.
[16] A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, M. Sternberg, J. Nicholson, and S. Muggleton. Modeling the effects of toxins in metabolic networks. IEEE Engineering in Medicine and Biology, 26:37-46, 2007.
[17] S.H. Muggleton and A.Tamaddoni-Nezhad. QG/GA: A stochastic search approach for Progol. Machine Learning, 70 (2–3):123–133, 2007. (Best Paper Award)
[18] A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, and S.H. Muggleton. Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning, 64:209–230, 2006.
Book chapter
[19] A. Tamaddoni-Nezhad, D. Lin, H. Watanabe, J. Chen and S. Muggleton, Machine Learning of Biological Networks using Abductive ILP, In Eds. L. Cerro and K. Inoue, Logical Modeling of Biological Systems, pp 363-401, ISTE-Wesley, 2014
Edited proceedings
[20] S.H. Muggleton , A. Tamaddoni-Nezhad & F.A. Lisi (Eds.) Inductive Logic Programming, 21st International Conference, ILP 2011, Revised Selected Papers, Series: Lecture Notes in Computer Science, Vol. 7207, Springer, 2012
[21] S.H. Muggleton, R. Otero, and A. Tamadonni-Nezhad (Eds.) Inductive Logic Programming, 16th International Conference, ILP 2006, Revised Selected Papers, Series: Lecture Notes in Computer Science, Vol. 4455, Springer, 2007.
Refereed conference and workshop papers
[22] W-Z Dai, S.H. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z-H. Zhou. Logical vision: One-shot meta-interpretive learning from real images. In Proceedings of the 27th International Conference on Inductive Logic Programming, Springer-Verlag, pages 46-62, 2018.
[23] U. Schmid, C. Zeller, T. Besold, A. Tamaddoni-Nezhad, and S.H. Muggleton. How does predicate invention affect human comprehensibility? In Proceedings of the 26th International Conference on Inductive Logic Programming, Springer-Verlag, pages 52-67, 2017.
[24] W. Dai , S. Muggleton , A. Tamaddoni-Nezhad and Z. Zhou, Logical vision: meta-interpretive learning for human-like vision, Human-Like Computing Machine Intelligence Workshop (MI20- HLC), 2016
[25] A. Cropper, A. Tamaddoni-Nezhad, and S. Muggleton. Meta-interpretive learning of data transformation programs. In Proceedings of the 25th International Conference on Inductive Logic Programming. Springer-Verlag, pages 46-59, 2016.
[26] A. Tamaddoni-Nezhad, D. Bohan, A. Raybould and S. Muggleton. Towards machine learning of predictive models from ecological data. In Proceedings of the 24th International Conference on Inductive Logic Programming, Springer-Verlag, pages 154-167, 2015.
[27] S.H. Muggleton, D. Lin, J. Chen, and A. Tamaddoni-Nezhad. Metabayes: Bayesian meta-interpretative learning using higher-order stochastic refinement. In Proceedings of the 23rd International Conference on Inductive Logic Programming, LNAI 8812, pages 1-17, 2014
[28] A. Tamaddoni-Nezhad, G. Afroozi Milani, D. Bohan, S. Dunbar, A. Raybould, and S.H. Muggleton. Towards automatic construction and corroboration of food webs. In Proceedings of the European Conference on Machine Learning, Workshop on Learning and Discovery in Symbolic Systems Biology (ECML/LDSSB 2012), pages 95-102, 2012
[29] G. Afroozi Milani, D. Bohan, S. Dunbar, A. Raybould, S.H. Muggleton and A. Tamaddoni-Nezhad, Machine learning and text mining of trophic links. In Proceedings of the 11th International Conference on Machine Learning and Applications Special Session on Learning on the Web (ICMLA/LW 2012), pages 410-415, IEEE, 2012
[30] A. Tamaddoni-Nezhad, D. Bohan, A. Raybould and S. Muggleton. Machine learning a probabilistic network of ecological interactions. In Proceedings of the 21st International Conference on Inductive Logic Programming, LNAI 7207, pages 332-346, 2012. (Best Paper Award)
[31] S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad. MC-Toplog: Complete multi-clause learning guided by a top theory. In Proceedings of the 21st International Conference on Inductive Logic Programming, LNAI 7207, pages 238-254, 2012.
[32] A. Tamaddoni-Nezhad and S.H. Muggleton. Stochastic Refinement. In Proceedings of the 20th International Conference on Inductive Logic Programming, pages 222-237, 2011.
[33] S.H. Muggleton, J. Santos, and A. Tamaddoni-Nezhad. ProGolem: a system based on relative minimal generalisation. In Proceedings of the 19th International Conference on Inductive Logic Programming, LNCS 5989, pages 131-148. Springer-Verlag, 2010.
[34] S.H. Muggleton, J. Santos, and A. Tamaddoni-Nezhad. TopLog: ILP using a logic program declarative bias. In Proceedings of the International Conference on Logic Programming, LNCS 5366, pages 687-692. Springer-Verlag, 2010.
[35] A. Tamaddoni-Nezhad, R. Barton, P. Hitchen, E. Kay, V. Lesk, F. Turner, A. Dell, C. Rawlings, M. Sternberg, B. Wren, S. Muggleton, A logic-based approach for modeling genotype-phenotype relations in Campylobacter, In Proceedings of the 9th International Conference on Systems Biology (ICSB-2008), 2008.
[36] J. Santos, A. Tamaddoni-Nezhad, and S.H. Muggleton. An ILP system for learning head output connected predicates. In Proceedings of the 14th Portuguese Conference on Artificial Intelligence, LNAI 5816, pages 150-159. Springer-Verlag, 2009.
[37] A. Tamaddoni-Nezhad and S.H. Muggleton. A note on refinement operators for IE-based ILP systems. In Proceedings of the 18th International Conference on Inductive Logic Programming, LNAI 5194, pages 297-314. Springer-Verlag, 2008.
[38] S.H. Muggleton and A. Tamaddoni-Nezhad. QG/GA: A stochastic search approach for Progol. In Proceedings of the 16th International Conference on Inductive Logic Programming, LNAI 4455, pages 37-39. Springer-Verlag, 2006.
[39] A. Tamaddoni-Nezhad, R. Greaves, and S.H. Muggleton. Large-scale online learning using analogical prediction. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.
[40] A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, S.H. Muggleton. Abduction and induction for learning models of inhibition in metabolic networks. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA’05), pages 233-238, IEEE. 2005.
[41] G. Afroozi Milani, K. Ziarati, and A. Tamaddoni-Nezhad. Virtual characteristics measurement using factor analysis. In Proceedings of the 2nd International Conference on E-Business and Telecommunication Networks (ICETE'05), pages 102-108, 2005.
[42] A. Tamaddoni-Nezhad, A. Kakas, S.H. Muggleton, and F. Pazos. Modelling inhibition in metabolic pathways through abduction and induction . In Proceedings of the 14th International Conference on Inductive Logic Programming, pages 305-322. Springer-Verlag, 2004.
[43] S.H. Muggleton, A. Tamaddoni-Nezhad, and H. Watanabe. Induction of enzyme classes from biological databases. In Proceedings of the 13th International Conference on Inductive Logic Programming, pages 269-280. Springer-Verlag, 2003.
[44] A. Tamaddoni-Nezhad, S. Muggleton, and J. Bang. A Bayesian model for metabolic pathways. In International Joint Conference on Artificial Intelligence (IJCAI03) Workshop on Learning Statistical Models from Relational Data, pages 50-57. IJCAI, 2003.
[45] A. Tamaddoni-Nezhad and S.H. Muggleton. A genetic algorithms approach to ILP. In Proceedings of the 12th International Conference on Inductive Logic Programming, pages 285-300. Springer-Verlag, 2002.
[46] A. Tamaddoni-Nezhad and S.H. Muggleton. Using genetic algorithms for learning clauses in first-order logic. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pages 639-646, Morgan Kaufmann Publishers, 2001.
[47] A. Tamaddoni-Nezhad and S.H. Muggleton. Searching the subsumption lattice by a genetic algorithm. In Proceedings of the 10th International Conference on Inductive Logic Programming, pages 243-252. Springer-Verlag, 2000.