Educational Qualifications

  • 2017                    
    Ph.D. (Chemical Engineering), University of Cambridge, United Kingdom
  • 2010                    
    B.Eng. (Chemical Engineering), University of Adelaide, Australia
  • 2010                    
    B.Fin., University of Adelaide, Australia

Academic and Professional Experience

  • 2024 - Present      
    Senior Lecturer, School of Business, Singapore University of Social Sciences
  • 2023 - 2024          
    Senior Scientist, Singapore Institute of Manufacturing Technology
  • 2019 - 2023          
    Scientist, Singapore Institute of Manufacturing Technology
  • 2017 - 2019          
    Research Fellow, Singapore Institute of Manufacturing Technology
  • 2017 - 2017          
    Part-time Lecturer, Nanyang Technological University
  • 2016 - 2017          
    Project Officer, Nanyang Technological University and Cambridge Centre for Advanced Research and Education in Singapore

Selected Publications

Refeered Journal Articles:

  • W. Weng, M. Pratama, J. Zhang, C. Chen, E. K. Y. Yapp, R. Savitha. Cross-domain continual learning via CLAMP. Information Sciences, 676:120813, 2024. doi:10.1016/j.ins.2024.120813.
  • E. K. Y. Yapp, A. Gupta, X. Li. A layer-wise neural network for multi-item single-output quality estimation. Journal of Intelligent Manufacturing, 34:3131—3141, 2023. doi:10.1007/s10845-022-01995-0.2.
  • M. de Carvalho, M. Pratama, J. Zhang, E. K. Y. Yapp. ACDC: Online unsupervised cross-domain adaptation. Knowledge-Based Systems, 253:109486, 2022. doi:10.1016/j.knosys.2022.109486.
  • W. Weng, M. Pratama, C. Za’in, M. de Carvalho, R. Appan, A. Ashfahani, E. K. Y. Yapp. Autonomous cross domain adaptation under extreme label scarcity. IEEE Transactions on Neural Networks and Learning Systems, 2022. doi:10.1109/TNNLS.2022.3183356.
  • F. Mao, W. Weng, M. Pratama, E. K. Y. Yapp. Continual learning via inter-task synaptic mapping. Knowledge-Based Systems, 222:106947, 2021. doi:10.1016/j.knosys.2021.106947.
  • W. Weng, M. Pratama, A. Ashfahani, E. K. Y. Yapp. Online Semi supervised Learning Approach for Quality Monitoring of Complex Manufacturing Process. Complexity, 2021:1–16, 2021. doi:10.1155/2021/3005276.
  • E. K. Y. Yapp, X. Li, W. F. Lu, P. S. Tan. Comparison of base classifiers for multi-label learning. Neurocomputing, 394:51–60, 2020. doi:10.1016/j.neucom.2020.01.102.
  • N. Q. K. Le, D. T. Do, F.-Y. Chiu, E. K. Y. Yapp, H.-Y. Yeh, C.-Y Chen. XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma. Journal of Personalized Medicine, 10:128, 2020. doi:10.3390/jpm10030128.
  • N. Q. K. Le, Q.-T. Ho, E. K. Y. Yapp, H.-Y. Yeh, Y.-Y. Ou. DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain’s complexes. Neurocomputing, 375:71–79, 2020. doi:10.1016/j.neucom.2019.09.070.
  • J. N. Sua, S. Y. Lim, M. H. Yulius, X. Su, E. K. Y. Yapp, N. Q. K. Le, H.-Y. Yeh. Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine. Chemometrics and Intelligent Laboratory Systems, 206:104171, 2020. doi:10.1016/j.chemolab.2020.104171.
  • N. Q. K. Le, E. K. Y. Yapp, N. Nagasundaram, H.-Y. Yeh. Classifying promoters by interpreting the hidden information of DNA sequences via deep learning and combination of continuous FastText N-grams, Frontiers in bioengineering and biotechnology, 7:305, 2019. doi:10.3389/fbioe.2019.00305.
  • 10. N. Q. K. Le, E. K. Y. Yapp, N. Nagasundaram, M. C. H. Chua, H.-Y. Yeh. Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture. Computational and Structural Biotechnology Journal, 17:1245–1254, 2019. doi:10.1016/j.csbj.2019.09.005.
  • N. Q. K. Le, T.-T. Huynh, E. K. Y. Yapp, H.-Y. Yeh. Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. Computer Methods and Programs in Biomedicine, 177:81–88, 2019. doi:10.1016/j.cmpb.2019.05.016.
  • N. Q. K. Le, E. K. Y. Yapp, Q.-T. Ho, N. Nagasundaram, Y.-Y. Ou, H.-Y. Yeh. iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou’s 5-step rule and word embedding. Analytical Biochemistry, 571:53–61, 2019. doi:10.1016/j.ab.2019.02.017.
  • N. Q. K. Le, E. K. Y. Yapp, Y.-Y. Ou, H.-Y. Yeh. iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou’s 5-step rule. Analytical Biochemistry, 575:17–26, 2019. doi:10.1016/j.ab.2019.03.017.
  • N. Q. K. Le, E. K. Y. Yapp, H.-Y. Yeh. ET-GRU: Incorporating multi-layer gated recurrent units and position specific scoring matrices to identify electron transport proteins. BMC Bioinformatics, 20:377, 2019. doi:10.1186/s12859-019-2972-5.
  • C. S. Lindberg, M. Y. Manuputty, E. K. Yapp, J. Akroyd, R. Xu, M. Kraft. A detailed particle model for polydisperse titanium dioxide aggregates. Journal of Computational Physics, 397:108799, 2019. doi:10.1016/j.jcp.2019.06.074
  • N. Nagasundaram, E. K. Y. Yapp, N. Q. K. Le, B. Kamaraj, A. M. Al-Subaie, H.-Y. Yeh. Application of computational biology and artificial intelligence technologies in cancer precision drug discovery. BioMed Research International, 2019. doi:10.1155/2019/8427042.
  • N. Nagasundaram, E. K. Y. Yapp, N. Q. K. Le, H.-Y. Yeh. In silico screening of sorbitol derivatives to inhibit viral matrix protein VP40 of Ebola virus. Molecular Biology Reports, 46: 3315–3324, 2019. doi:10.1007/s11033-019-04792-w.
  • J. W. Martin, R. I. Slavchov, E. K. Y. Yapp, J. Akroyd, S. Mosbach, M. Kraft. The Polarization of Polycyclic Aromatic Hydrocarbons Curved by Pentagon Incorporation: The Role of the Flexoelectric Dipole. Journal of Physical Chemistry C, 121:27154–27163, 2017. doi:10.1021/acs.jpcc.7b09044.
  • S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft. Modelling of soot formation in a diesel engine with the moment projection method. Energy Procedia, 142:4092–4097, 2017. doi:10.1016/j.egypro.2017.12.330.
  • S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft. Extension of moment projection method to the fragmentation process. Journal of Computational Physics, 335:516–534, 2017. doi:10.1016/j.jcp.2017.01.045.
  • S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft. A moment projection method for population balance dynamics with a shrinkage term. Journal of Computational Physics, 330:960–980, 2017. doi:10.1016/j.jcp.2016.10.030.
  • E. K. Y. Yapp, C. G. Wells, J. Akroyd, S. Mosbach, and M. Kraft. Modelling PAH curvature in laminar premixed flames using a detailed population balance model. Combustion Flame, 34:1861–1868, 2017. doi:10.1016/j.combustflame.2016.10.004.
  • E. K. Y. Yapp, R. I. A. Patterson, J. Akroyd, S. Mosbach, E. M. Adkins, J. H. Miller, and M. Kraft. Numerical simulation and parametric sensitivity study of optical band gap in a laminar co-flow ethylene diffusion flame. Combustion Flame, 167:2569–2581, 2016. doi:10.1016/j.combustflame.2016.01.033.
  • E. K. Y. Yapp, D. Chen, J. Akroyd, S. Mosbach, M. Kraft, J. Camacho, and H. Wang. Numerical simulation and parametric sensitivity study of particle size distributions in a burner-stabilised stagnation flame. Combustion Flame, 162:2569–2581, 2015. doi:10.1016/j.combustflame.2015.03.006.

 

Book chapter:

  • E. K. Y. Yapp and M. Kraft. Modelling soot formation: model of particle formation. In F. Battin-Leclerc, J. M. Simmie, E. Blurock (Eds.), Cleaner Combustion—Developing Detailed Chemical Kinetic Models (pp. (389–407)). London: Springer, 2013. doi: 10.1007/978-1-4471-5307-8_15.

 

Refeered Conference Papers:

  • E. K. Y. Yapp, N. C. N. Nam. Anomaly detection on MVTec AD using VQ-VAE-2. 2024 57th CIRP Conference on Manufacturing Systems (CMS). 2024, accepted.
  • S. Pan, H. Luo, M. C. H. Chua, K. Pugalenthi, E. K. Y. Yapp. A review of similarity-based few-shot learning methods for time series classification in manufacturing. 2024 6th International Conference on Industrial Artificial Intelligence (IAI). 2024, accepted.
  • M. de Carvalho, M. Pratama, J. Zhang, H. Chua, E. K. Y. Yapp. Towards cross-domain continual learning. 2024 IEEE 40th International Conference on Data Engineering (ICDE), 1131–1142, 2024. doi:10.1109/ICDE60146.2024.00092.
  • A. Ashfahani, M. Pratama, E. Lughofer, E. Y. K. Yee. Autonomous Deep Quality Monitoring in Streaming Environments. 2021 International Joint Conference on Neural Networks (IJCNN), 1–8, 2021. doi:10.1109/IJCNN52387.2021.9534461.
  • D. N. C. Nam, T. Van Tung and E. Y. K. Yee, Quality monitoring for injection moulding process using a semi-supervised learning approach. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6, 2021. doi:10.1109/IECON48115.2021.9589593.
  • N. J. Punnoose, P. Vadakkepat, A.-P. Loh, E. K. Y. Yap. Data-driven quality estimation for production processes with lot-level quality control. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6, 2021. doi:10.1109/IECON48115.2021.9589245.
  • K. J. Lee, E. K. Y. Yapp, X. Li. Unsupervised probability matching for quality estimation with partial information in a many-to-one input-output scenario. 2020 The 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1432–1437, 2020. doi:10.1109/ICIEA48937.2020.9248430.
  • J. Camacho, A. V. Singh, W. Wang, R. Shan, E. K. Y. Yapp, D. Chen, M. Kraft, H. Wang. Soot particle size distributions in premixed stretch-stabilized flat ethylene–oxygen–argon flames. Proceedings of the Combustion Institute, 36:1001–1009, 2017. doi:10.1016/j.proci.2016.06.170.
  • D. Chen, Z. Zainuddin, E. Yapp, J. Akroyd, S. Mosbach, and M. Kraft. A fully coupled simulation of PAH and soot growth with a population balance model. Proceedings of the Combustion Institute, 34:1827–1835, 2013. doi:10.1016/j.proci.2012.06.089.

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