Summary
I am a Research Fellow at the Department of Computing, Imperial College London. My 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). My current research interests also include:
- Automated Scientific Discovery and Data Science (e.g. automated discovery of food-webs using machine learning and text-mining)
- Computational Systems Biology and Bioinformatics (e.g. systems biology of bacterial glycomes)
- Machine Learning of Biological Networks (e.g. integrated machine learning of metabolic networks applied to predictive toxicology)
- Genetic and Evolutionary Computation (e.g. a genetic algorithm approach to inductive logic programming)
Please see my personal home page for more information and updated publications.
Selected Publications
Journal Articles
Bohan D, Dumbrell A, Raybould A, et al., 2017, Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks, Trends in Ecology and Evolution, Vol:32, ISSN:1872-8383, Pages:477-487
Muggleton SH, Lin D, Tamaddoni Nezhad A, 2015, Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited, Machine Learning, Vol:100, ISSN:1573-0565, Pages:49-73
Tamaddoni-Nezhad A, Muggleton S, 2009, The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause, Machine Learning, Vol:76, ISSN:0885-6125, Pages:37-72
Chapters
Vacher C, Tamaddoni-Nezhad A, Kamenova S, et al., 2016, Learning Ecological Networks from Next-Generation Sequencing Data, ECOSYSTEM SERVICES: FROM BIODIVERSITY TO SOCIETY, PT 2, Editor(s): Woodward, Bohan, ELSEVIER ACADEMIC PRESS INC, Pages:1-39, ISBN:978-0-08-100978-9
Tamaddoni-Nezhad A, Milani GA, Raybould A, et al., 2013, Construction and Validation of Food-webs using Logic-based Machine Learning and Text-mining, Pages:225-289