Dr Leandro L. Minku BSc, MSc, PhD

Dr Leandro L. Minku

School of Computer Science
Lecturer of Computer Science

Contact details

Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Leandro L. Minku is a Lecturer in Intelligent Systems.

He has over 70 publications in scientific venues, book chapters, columns for practitioners and articles to the general public. His research has been funded by EPSRC.

Leandro is enthusiastic about discussing and teaching artificial intelligence, and has strong research interests in the field of machine learning, including its intersections with other fields such as software engineering.

Leandro is a co-supervisor of ECOLE, an Innovative Training Network (ITN) for early stage researchers (ESRs) funded by the EU’s Horizon 2020 research and innovation program under grant agreement No.766186. It is based on novel synergies between nature inspired optimisation and machine learning. The training programme will be targeted at the automotive industry and ESRs employed on the program will be provided with the transferable skills necessary for thriving careers in emerging and rapidly developing industrial areas.

Please follow the link below to find out more about Leandro's work:

Dr Leandro L. Minku-personal web page

Qualifications

  • Fellow of the Higher Education Academy (2019)

  • PhD in Computer Science, University of Birmingham (2010)

  • MSc in Computer Science, Federal University of Pernambuco (2006)

  • BSc in Computer Science, Federal University of Parana (2003)

Biography

Leandro L. Minku is a Lecturer in Intelligent Systems at the School of Computer Science, University of Birmingham (UK). Prior to that, he was a Lecturer in Computer Science at the University of Leicester (UK). He received his PhD degree in Computer Science from the University of Birmingham (UK) in 2010.

Dr. Minku's main research interests are machine learning in non-stationary environments / data stream mining, online class imbalance learning, ensembles of learning machines and computational intelligence for software engineering. His work has been published in internationally renowned journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.

Among other roles, Dr. Minku is the general chair for the International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2019 and 2020), the co-chair for the Artifacts Evaluation Track at the International Conference on Software Engineering (ICSE 2020), an associate editor for the Journal of Systems and Software, an editorial board member for Neurocomputing and a conference correspondent for IEEE Software.

Teaching

  • MSc in Computer Science

Postgraduate supervision

  • Dr Rodolfo Cavalcante (completed in 2017), topic: time series forecast in non-stationary environments.

  • Ds Liyan Song, (completed in 2018), topic: software effort estimation using machine learning.

  • Mr Michael Chiu, topic: machine learning for non-stationary environments.

  • Mr Honghui Du, topic: machine learning for non-stationary environments.

  • Mr Gustavo Henrique Ferreira de Miranda Oliveira, topic: machine learning for non-stationary environments.

  • Mr Gan Ruan, topic: dynamic optimisation.

  • Ms Dalia Sobhi (completed in 2019), topic: software architectures.
  • Ms Sadia Tabassum, topic: machine learning for software engineering.

Research

  • Mining Data Streams, Online Learning and Concept Drift

  • Class Imbalanced Learning

  • Ensembles of Learning Machines

  • Evolutionary Algorithms (dynamic optimisation, multi-objective techniques, hyperheuristics)

  • Applications of the above to Software Engineering

Publications

Recent publications

Article

Minku, L 2019, 'A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation', Empirical Software Engineering, vol. 24, no. 5, pp. 3153-3204. https://doi.org/10.1007/s10664-019-09686-w

Idrees, MM, Minku, LL, Stahl, F & Badii, A 2019, 'A heterogeneous online learning ensemble for non-stationary environments', Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2019.104983

Song, L, Minku, LL & Yao, X 2019, 'Software effort interval prediction via Bayesian inference and synthetic Bootstrap resampling', ACM Transactions on Software Engineering and Methodology, vol. 28, no. 1, 5. https://doi.org/10.1145/3295700

Wang, S, Minku, LL & Yao, X 2018, 'A Systematic Study of Online Class Imbalance Learning With Concept Drift', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4802-4821. https://doi.org/10.1109/TNNLS.2017.2771290

Shen, X-N, Minku, LL, Marturi, N, Guo, Y-N & Han, Y 2018, 'A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling', Information Sciences, vol. 428, pp. 1-29. https://doi.org/10.1016/j.ins.2017.10.041

Chapter (peer-reviewed)

Minku, L 2019, Transfer Learning in Non-Stationary Environments. in M Sayed-Mouchaweh (ed.), Learning from Data Streams in Evolving Environments. Studies in Big Data, Springer, pp. 13-37.

Conference contribution

Saha, S, Rios, T, Minku, L, Yao, X, Xu, Z, Sendhoff, B & Menzel, S 2019, Optimal evolutionary optimization hyper-parameters to mimic human user behavior. in 2019 IEEE Symposium Series on Computational Intelligence (EEE SSCI 2019). IEEE Computer Society Press, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6/12/19.

Gomes Cabral, G, Minku, L, Shihab, E & Mujahid, S 2019, Class imbalance evolution and verification latency in just-in-time software defect prediction. in Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE 2019). IEEE Computer Society Press, pp. 666-676, 41st ACM/IEEE International Conference on Software Engineering (ICSE 2019), Montreal, Canada, 25/05/19. https://doi.org/10.1109/ICSE.2019.00076

Oliveira, GHFM, Minku, L & Oliveira, ALI 2019, GMM-VRD: a gaussian mixture model for dealing with virtual and real concept drifts. in Proceedings of the International Joint Conference on Neural Networks (IJCNN 2019). IEEE Computer Society, International Joint Conference on Neural Networks (IJCNN 2019), Budapest, Hungary, 14/07/19.

Du, H, Minku, LL & Zhou, H 2019, Multi-source transfer learning for non-stationary environments. in Proceedings of the International Joint Conference on Neural Networks (IJCNN 2019). IEEE Computer Society, International Joint Conference on Neural Networks (IJCNN 2019), Budapest, Hungary, 14/07/19.

Shehu, B, Heckel, R & Minku, L 2018, Reverse engineering the behaviour of Twitter bots. in 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS). Institute of Electrical and Electronics Engineers (IEEE), pp. 27 - 34, The Fith International Conference on Internet of Things:, Valencia, Spain, 15/10/18. https://doi.org/10.1109/SNAMS.2018.8554675

SONG, L, MINKU, LL & YAO, X 2018, A novel automated approach for software effort estimation based on data augmentation. in G T. Leavens, A Garcia & C S. Păsăreanu (eds), Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018). ACM/IEEE, New York, NY, pp. 468-479, The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018), Lake Buena Vista, United States, 4/11/18. https://doi.org/10.1145/3236024.3236052

Chiu, CW & Minku, LL 2018, Diversity-based pool of models for dealing with recurring concepts. in 2018 International Joint Conference on Neural Networks (IJCNN) . International Joint Conference on Neural Networks (IJCNN), vol. 2018, IEEE Computer Society, pp. 2759-2766, 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8489190

WALKINSHAW, N & Minku, L 2018, Are 20% of files responsible for 80% of defects? in Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '18 ). ACM/IEEE, New York, NY, pp. 2.1-2.10, 12th International Symposium on 
Empirical Software Engineering and Measurement, Oulu, Finland, 11/10/18. https://doi.org/10.1145/3239235.3239244

OLIVEIRA, GHFM, CAVALCANTE, RC, CABRAL, GG, MINKU, LL & OLIVEIRA, ALI 2018, Time series forecasting in the presence of concept drift: a PSO-based approach. in 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). International Conference on Tools with Artificial Intelligence (ICTAI), vol. Nov-2017, Institute of Electrical and Electronics Engineers (IEEE), pp. 239-246, IEEE ICTAI 2017 , Boston, United States, 6/11/17. https://doi.org/10.1109/ICTAI.2017.00046

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