Dr Leandro L. Minku BSc, MSc, PhD

Dr Leandro L. Minku

School of Computer Science
Senior Lecturer of Computer Science

Contact details

Email
l.l.minku@bham.ac.uk
View my research portal
Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Leandro L. Minku is a Senior 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 Senior 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

Tong, H, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2022, 'A novel generalised meta-heuristic framework for dynamic capacitated arc routing problems', IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2022.3147509

Song, L & Minku, L 2022, 'A procedure to continuously evaluate predictive performance of just-in-time software defect prediction models during software development', IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2022.3158831

Shi, X, Minku, L & Yao, X 2022, 'Adaptive Memory-enhanced Time Delay Reservoir and Its Memristive Implementation', IEEE Transactions on Computers.

Sobhy, D, Minku, L, Bahsoon, R & Kazman, R 2022, 'Continuous and proactive software architecture evaluation: an IoT case', ACM Transactions on Software Engineering and Methodology, vol. 31, no. 3, 46. https://doi.org/10.1145/3492762

Tabassum, S, Minku, L & Feng, D 2022, 'Cross-project online just-in-time software defect prediction', IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2022.3150153

Hassan, S, Bahsoon, R, Minku, L & Nour, A 2022, 'Dynamic evaluation of microservice granularity adaptation', ACM Transactions on Autonomous and Adaptive Systems , vol. 16, no. 2, 4. https://doi.org/10.1145/3502724

Sobhy, D, Bahsoon, R, Minku, L & Kazman, R 2021, 'Evaluation of software architectures under uncertainty: a systematic literature review', ACM Transactions on Software Engineering and Methodology, vol. 30, no. 4, 51. https://doi.org/10.1145/3464305

Soares, R & Minku, L 2021, 'OSNN: an online semisupervised neural network for nonstationary data streams', IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3132584

Tong, H, Huang, C, Minku, L & Yao, X 2021, 'Surrogate models in evolutionary single-objective optimization: a new taxonomy and experimental study', Information Sciences. https://doi.org/10.1016/j.ins.2021.03.002

Oliveira, GHFM, Minku, L & Oliveira, ALI 2021, 'Tackling virtual and real concept drifts: an adaptive Gaussian mixture model approach', IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3099690

Conference contribution

Saha, S, Rios, T, Minku, L, Vas Stein, B, Wollstadt, P, Yao, X, Back, T, Sendhoff, B & Menzel, S 2022, Exploiting generative models for performance predictions of 3D car designs. in 2021 IEEE Symposium Series on Computational Intelligence (SSCI)., 9660034, IEEE Symposium Series on Computational Intelligence, IEEE, IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 4/12/21. https://doi.org/10.1109/SSCI50451.2021.9660034

Shi, X, Gao, J, Minku, L, Yu, J & Yao, X 2022, Second-order time delay reservoir computing for nonlinear time series problems. in 2021 IEEE Symposium Series on Computational Intelligence (SSCI)., 9659913, IEEE Symposium Series on Computational Intelligence, IEEE, IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 4/12/21. https://doi.org/10.1109/SSCI50451.2021.9659913

Tong, H, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2021, A hybrid local search framework for the dynamic capacitated arc routing problem. in F Chicano (ed.), GECCO '21: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Genetic and Evolutionary Computation Conference (GECCO), Association for Computing Machinery (ACM), New York, pp. 139-140, Genetic and Evolutionary Computation Conference, 10/07/21. https://doi.org/10.1145/3449726.3459450

Saha, S, Minku, LL, Yao, X, Senhoff, B & Menzel, S 2021, Exploiting linear interpolation of variational autoencoders for satisfying preferences in evolutionary design optimization. in 2021 IEEE Congress on Evolutionary Computation (CEC)., 9504772, Congress on Evolutionary Computation, IEEE, pp. 1767-1776, 2021 IEEE Congress on Evolutionary Computation (CEC), 28/06/21. https://doi.org/10.1109/CEC45853.2021.9504772

Minku, L 2021, Multi-stream online transfer learning for software effort estimation: is it necessary? in S McIntosh, X Xia & S Amasaki (eds), PROMISE 2021: Proceedings of the 17th ACM International Conference on Predictive Models and Data Analytics in Software Engineering. PROMISE: Predictor Models in Software Engineering, Association for Computing Machinery (ACM), pp. 11-20, The 17th International Conference on Predictive Models and Data Analytics in Software Engineering, Athens, Greece, 19/08/21. https://doi.org/10.1145/3475960.3475988

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