Short bio 

I completed my PhD in Data Science in 2011 at Birkbeck, University of London. I also hold undergraduate and master's degrees related to computer science, as well as a graduate diploma and a master's in pure mathematics. I'm originally Brazilian (well, as much as possible... for someone who doesn't know anything about football), and I moved to the UK in 2003 (the original plan was to stay in the UK for a year... that was over 20 years ago).

I have a mix of academic and commercial experience. Currently, I am a Senior Lecturer in Computer Science and AI at the University of Essex. Previously I held positions at the University of Hertfordshire, Birkbeck University of London, and I worked in software development using mainly C++, VB and VBA.

Research

I am particularly interested in feature weighting as well as unsupervised and semi-supervised learning. I have published various papers with applications in fields such as security, biosignal processing (EEG) and data mining.

If you are interested in pursuing a PhD in data science, and in particular if your topic of choice relates to unsupervised learning, do feel free to contact me. PhD studentships are usually advertised at KDnuggets.

Grants and awards

• 2024-2027 AI and automation of complex quotes
  MEL Aviation and Innovate UK. GBP 257,091 (PI).

• 2019-2022 Develop AI methods to optimise interactions with customers.
  BT and Innovate UK. GBP 247,874 (PI).

• 2018-2023 Anomaly detection for fraud prevention within the Brazilian Governmental Public Key Infrastructure.
  Royal Society, GBP 63,130 (CO-PI).

• 2018-2021 Artificial Intelligence Triage System for the MSK Service.
  Provide CIC and Innovate UK. GBP 578,252 (CO-PI).

• 2017-2018 Effortless Accountability.
  ESRC IAA Challenge lab: Essex County Council. GBP 10,000 (CO-I).

• 2017 Chikio Hayashi Award.
  International Federation of Classification Societies (IFCS), USD$ 1,000.

• 2017-2018 Beyond clustering with a single distance.
  Microsoft Azure Research Award. USD$ 5,000.00 (PI).

• 2013/14 Excellence in Teaching Award.
  Glyndwr University (nominated).

Editorial work
I am an Associate Editor for the following journals:

• Journal of Classification (Springer)

• Array (Elsevier)

• PLoS ONE

I have also been elected Secretary of the British Data Science Society (formerly, the British Classification Society).

Selected works
Internet profiles: Google scholar, ResearchGate.

• Amorim, R.C., Makarenkov, V., On k-means iterations and Gaussian clusters, Neurocomputing, Elsevier, vol. 553 (7), 2023.

• Amorim, R.C., On sum-free subsets of abelian groups, Axioms, vol. 12(8), 2023.

• Chowdhury, S., Helian, N., Amorim, R.C., Feature weighting in DBSCAN using reverse nearest neighbours, Pattern Recognition, Elsevier, vol. 137, 2023.

• Harris, D., Amorim, R.C., An extensive empirical comparison of k-means initialisation algorithms, IEEE Access, IEEE, vol. 10, pp. 58752-58768, 2022. 

• Alamos, A.J.B., Hashempour, R., Rumble, D., Jameel, S., Amorim, R.C., Unified Transformer Multi-Task Learning for Intent Classification With Entity Recognition, IEEE Access, IEEE, vol. 9, pp. 147306-147314, 2021.

• Amorim, R.C., Makarenkov, V., Improving cluster recovery with feature rescaling factors, Applied Intelligence, Springer, vol. 51, pp. 5759–5774, 2021. 

• Amorim, R.C., Ruiz, C.D.L., Identifying meaningful clusters in malware data, Expert Systems with Applications, Elsevier, vol. 177, 2021.

• Amorim, R.C., Makarenkov, V., Mirkin, B., Core clustering as a tool for tackling noise in cluster labels. Journal of Classification, Springer, vol. 37, pp. 143–157, 2020

• Amorim, R.C., Unsupervised feature selection for large data sets. Pattern Recognition Letters, Elsevier, vol. 128, pp. 183-189, 2019.

• Chowdhury, S., Amorim, R.C., An efficient density-based clustering algorithm using reverse nearest neighbour. Proceedings of the Computing Conference, 2019, London, UK.

• Panday, D., Amorim, R.C., Lane, P., Feature weighting as a tool for unsupervised feature selection. Information Processing Letters, Elsevier, vol. 129, pp. 44-52, 2018.

• Amorim, R.C., Tahiri, N., Mirkin, B., Makarenkov, V., A Median-Based Consensus Rule for Distance Exponent Selection in the Framework of Intelligent and Weighted Minkowski Clustering, In: Palumbo F., Montanari A., Vichi M. (eds) Data Science. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, pp. 97-110, 2017.

• Amorim, R.C., Shestakov, A., Makarenkov, V., Mirkin, B., The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning, Pattern Recognition, Elsevier, vol. 67, pp. 62-72, 2017.

• Amorim, R.C., Makarenkov, V., Mirkin, B., A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation, Information Sciences, Elsevier, Vol. 370-371, pp. 343-354, 2016.

• Amorim, R.C., A survey on feature weighting based K-Means algorithms, Journal of Classification, Springer, 33(2), pp. 210-242, 2016.

• Amorim, R.C., Makarenkov, V., Applying subclustering and Lp distance in Weighted K-Means with distributed centroids, Neurocomputing, Elsevier, Vol. 173(3), pp.700-707, 2016.

• Amorim, R.C., Hennig, C., Recovering the number of clusters in data sets with noise features using feature rescaling factors, Information Sciences, Elsevier, vol. 324, pp. 126-145, 2015.

• Amorim, R.C., Feature relevance in Ward's hierarchical clustering using the Lp norm, Journal of Classification, Springer, vol. 32(1), pp. 46-62, 2015.

• Amorim, R.C., and Mirkin B., A clustering based approach to reduce feature redundancy. In: Skulimowski, A.M.J., Kacprzyk, J. (Eds). Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing. Springer.

• Puttaroo, M., Komisarczuk, P., Amorim, R.C., Challenges in developing Capture-HPC exclusion lists, Proceedings of the 7th International Conference on Security of Information and Networks, 2014, Glasgow, UK.

• Zampieri, M., and Amorim, R.C., Between Sound and Spelling: Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery. Proceedings of the 9th International Conference on Natural Language Processing, 2014, Warsaw, Poland.

• Amorim, R.C. and Komisarczuk, P., Towards effective malware clustering: reducing false negatives through feature weighting and the Lp metric. In: Issac, B. and Israr, N. (Eds) Case Studies in Secure Computing - Achievements and Trends. CRC Press, 2014.

• Amorim, R.C. and Komisarczuk, P., Partitional Clustering of Malware using K-Means. In: Blackwell, C. and Zhu, H. (Eds) Cyberpatterns: Unifying Design Patterns with Security, Attack and Forensic Patterns. Springer, pp. 223-233, 2014.

• Amorim, R.C. and Mirkin, B., Removing redundant features via clustering: preliminary results in mental task separation. Proceedings of the 8th International Conference on Knowledge, Information and Creativity Support Systems, 7-9 November 2013, Krakow, Poland.

• Amorim, R.C. and Zampieri, M., Effective Spell Checking Methods Using Clustering Algorithms. Recent Advances in Natural Language Processing, 7-13 September 2013, Hissar, Bulgaria.

• Amorim, R.C. and Mirkin, B., Selecting the Minkowski exponent for intelligent K-Means with feature weighting. In: Pardalos, P., Goldengorin, B., Aleskerov, F. (Eds), Clusters, orders, trees: methods and applications, Springer, 2013.

• Puttaaroo, M., Komisarczuk, P., Amorim, R.C., On Drive-by-Download Attacks and Malware Classification. Fifth International Conference on Internet Technologies & Applications (ITA), Wrexham, Wales, 10 to 13 September 2013.

• Austin, A., Amorim, R.C., Griffin, A., Targeted tutorials and the use of ASSIST to support student learning. Education, Learning, Styles, Individual differences Network (ELSIN), Billund, Denmark, 18 to 20 June 2013.

• Amorim, R.C., An Empirical Evaluation of Different Initializations on the Number of K-means Iterations. MICAI - Lecture Notes in Computer Sciences, 7629, pp. 15-26, 2013.

• Amorim, R.C., Constrained Clustering with Minkowski Weighted K-Means. 13th IEEE International Symposium on Computational Intelligence and Informatics, pp. 14-17, Budapest, Hungary, 20-22 November 2012.

• Amorim R.C. and Fenner, T., Weighting Features for Partition Around Medoids using the Minkowski Metric. IDA - Lecture Notes in Computer Science, 7619, pp. 35-44, 2012.

• Amorim, R.C. and Komisarczuk P., On Initializations for the Minkowski Weighted K-Means. IDA - Lecture Notes in Computer Science, 7619, pp.45-55, 2012.

• Amorim, R.C., Mirkin B., Gan J.Q., Anomalous Pattern based Clustering of Mental Tasks with Subject Independent Learning: Some Preliminary Results, Artificial Intelligence Research, 1(1), pp. 46-54, 2012.

• Amorim, R.C. and Komisarczuk P., On Partitional Clustering of Malware, CyberPatterns 2012, Oxford Brookes, Oxford, 9-10 July 2012.

• Amorim, R.C. and Mirkin, B., Minkowski Metric, Feature Weighting and Anomalous Cluster Initialisation in K-Means Clustering, Pattern Recognition, vol. 45(3), pp. 1061-1075, 2012.

• Amorim, R.C. and Komisarczuk, P., On the Future of Capture-HPC: A Malware Survey, Technical Report 01/2012, University of West London, 2012.

• Amorim, R.C. Feature Weighting for Clustering Using K-Means and the Minkowski Metric, Lambert Academic Publishing, 2012.

• Amorim, R.C. and Mirkin, B., Minkowski Metric for Feature Weighting, Proceedings of the International Classification Conference, University of St. Andrews, Scotland, 11-15 July, 2011.

• Amorim, R. C., Mirkin, B. and Gan, J. Q., A method for classifying mental tasks in the space of EEG transforms, UKCI, University of Nottingham, 7-9 September, 2009.

• Amorim, R. C., Computational Methods of Feature Selection - Book Review, Information Processing & Management, Elsevier, 2009.

• Amorim, R. C., An Adaptive Spell Checker Based on PS3M: Improving the Clusters of Replacement Words, The Sixth International Conference on Computer Recognition Systems, Advances in Intelligent and Soft Computing, Springer-Verlag, 2009.

• Amorim, R. C., Matrix Methods in Data Mining and Pattern Recognition, Book Review, Cognitive Systems Research, Elsevier, 2009.

• Amorim, R. C., Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge, The 2nd International Conference on Advanced Engineering Computing and Applications in Sciences, IEEE Computer Society Press, Spain, 2008.

• Amorim, R. C., Successes and New Directions in Data Mining - Book Review, Information Retrieval, Springer, 2008.