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

Agrawal, A, Menzies, T, Minku, LL, Wagner, M & Yu, Z 2020, 'Better software analytics via “DUO”: data mining algorithms using/used-by optimizers', Empirical Software Engineering, vol. 25, no. 3, pp. 2099–2136. https://doi.org/10.1007/s10664-020-09808-9

Zhang, O, Minku, LL & Gonem, S 2020, 'Detecting asthma exacerbations using daily home monitoring and machine learning', Journal of Asthma. https://doi.org/10.1080/02770903.2020.1802746

Dallora, AL, Minku, L, Mendes, E, Rennemark, M, Anderberg, P & Sanmartin Berglund, J 2020, 'Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia', International Journal of Environmental Research and Public Health, vol. 17, no. 18. https://doi.org/10.3390/ijerph17186674

Sobhy, D, Minku, L, Bahsoon, R, Chen, T & Kazman, R 2020, 'Run-time evaluation of architectures: a case study of diversification in IoT', Journal of Systems and Software, vol. 159, 110428. https://doi.org/10.1016/j.jss.2019.110428

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

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

Conference article

Saha, S, Menzel, S, Minku, L & Yao, X 2020, 'Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds', IEEE Spectrum.

Conference contribution

Wang, S & Minku, L 2020, AUC estimation and concept drift detection for imbalanced data streams with multiple classes. in Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020., 9207377, Proceedings of International Joint Conference on Neural Networks, IEEE Computer Society Press, IEEE International Joint Conference on Neural Networks (IJCNN), 2020 , Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207377

Ruan, G, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2020, Computational study on effectiveness of knowledge transfer in dynamic multi-objective optimisation. in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE Computer Society Press, pp. 1-8, 2020 IEEE Congress on Evolutionary Computation (IEE CEC 2020), Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/CEC48606.2020.9185907

Du, H, Minku, L & Zhou, H 2020, MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments. in 20th IEEE International Conference on Data Mining (ICDM, 2020). IEEE Computer Society Press, 20th IEEE International Conference on Data Mining (ICDM), 2020 , Sorrento, Italy, 17/11/20.

Saha, S, Rios, T, Minku, L, Yao, X, Xu, Z, Sendhoff, B & Menzel, S 2020, Optimal evolutionary optimization hyper-parameters to mimic human user behavior. in 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019., 9002958, IEEE Symposium Series on Computational Intelligence (SSCI), IEEE Computer Society Press, pp. 858-866, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6/12/19. https://doi.org/10.1109/SSCI44817.2019.9002958

Tong, H, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2020, Towards Novel Meta-heuristic Algorithms for Dynamic Capacitated Arc Routing Problems. in Parallel Problem Solving from Nature (PPSN XVI), Sixteenth International Conference, Proceedings . Lecture Notes in Computer Science, Springer, Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020., Leiden, Netherlands, 5/09/20.

Tabassum, S, Minku, LL, Feng, D, Cabral, GG & Song, L 2019, An Investigation of Cross-Project Learning in Online Just-In-Time Software Defect Prediction. in 42nd International Conference on Software Engineering (ICSE 2020). IEEE Computer Society Press, 42nd International Conference on Software Engineering (ICSE 2020), Seoul, Korea, Republic of, 23/05/20.

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 2019 International Joint Conference on Neural Networks (IJCNN)., 8852097, IEEE Computer Society, pp. 1-8, International Joint Conference on Neural Networks (IJCNN 2019), Budapest, Hungary, 14/07/19. https://doi.org/10.1109/IJCNN.2019.8852097

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