Educational Qualifications

2016                     
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

Awards and Honours

2026   
Excellent Paper Award, 2026 IET International Conference on Engineering Technology and Applications (IET ICETA), for “What helps PCB defect classification: crop scale, not generative augmentation”

2026   
Certificate of Presentation Award, 2026 IEEE International Conference on Consumer Electronics – Taiwan (ICCE-Taiwan), for “Generative AI for defect generation in PCBs”

2021                    
Best Research Project Award, Team Member (Work Package 3.4 Lead), Cyber-Physical Production System - Towards Contextual and Intelligent Response, Singapore Institute of Manufacturing Technology

2011                    
Gates Cambridge Scholarship, University of Cambridge

2010                    
University Medal, University of Adelaide

2005                    
International Scholarship, University of Adelaide

Selected Publications

Note on author name: Edward Yapp Kien Yee publishes as E. K. Y. Yapp. In some publications the name was misspelt or the given names and surname were reordered. These entries are reproduced as published and marked with an asterisk (*).

Refereed Journal Articles:

  • E. K. Y. Yapp, W. Zhuang, C. Chen, X. Wang, Y. Wen, H.-Y. Yeh. Multi-view dynastic classification and visual interpretation of blue-and-white porcelain. Social Sciences and Humanities Open, 13:102768, 2026. doi:10.1016/j.ssaho.2026.102768.
  • C. Chen, A. Y. X. Lee, E. K. Y. Yapp, H.-Y. Yeh. Night soil fertilizer use and parasitic infections in ancient China: a preliminary study of agricultural practices, economic factors and disease ecology in the Song dynasty. Parasitology, 1–9, 2026. doi:10.1017/S0031182026101784.
  • E. K. Y. Yapp, H.-Y. Yeh. An extensive experimental comparison of machine and deep learning methods for credit and bank fraud detection. Finance Research Letters, 88:109190, 2026. doi:10.1016/j.frl.2025.109190.
  • W. Weng, M. Pratama, J. Zhang, C. Chen, E. Y. K. Yie*, 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.
  • W. Weng, M. Pratama, C. Za’in, M. de Carvalho, R. Appan, A. Ashfahani, E. Y. K. Yee*. Autonomous cross domain adaptation under extreme label scarcity. IEEE Transactions on Neural Networks and Learning Systems, 34:6839–6850, 2023. doi:10.1109/TNNLS.2022.3183356.
  • M. de Carvalho, M. Pratama, J. Zhang, E. Y. K. Yee*. ACDC: Online unsupervised cross-domain adaptation. Knowledge-Based Systems, 253:109486, 2022. doi:10.1016/j.knosys.2022.109486.
  • F. Mao, W. Weng, M. Pratama, E. Y. K. Yee*. 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 semisupervised learning approach for quality monitoring of complex manufacturing process. Complexity, 2021:3005276, 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, Y.-Y. Ou, H.-Y. Yeh. 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, M. C. H. Chua. Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites. 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.
  • 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: using multi-layer gated recurrent units 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. Y. Yapp, J. Akroyd, R. Xu, M. Kraft. A detailed particle model for polydisperse aggregate particles. Journal of Computational Physics, 397:108799, 2019. doi:10.1016/j.jcp.2019.06.074.
  • N. Nagarajan, 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, 2019:8427042, 2019. doi:10.1155/2019/8427042.
  • N. Nagarajan, E. K. Y. Yapp, N. Q. K. Le, H.-Y. Yeh. In silico screening of sugar alcohol compounds 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, R. Xu, M. Kraft. Modelling PAH curvature in laminar premixed flames using a detailed population balance model. Combustion and Flame, 176:172–180, 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, M. Kraft. Numerical simulation and parametric sensitivity study of optical band gap in a laminar co-flow ethylene diffusion flame. Combustion and Flame, 167:320–334, 2016. doi:10.1016/j.combustflame.2016.01.033.
  • E. K. Y. Yapp, D. Chen, J. Akroyd, S. Mosbach, M. Kraft, J. Camacho, H. Wang. Numerical simulation and parametric sensitivity study of particle size distributions in a burner-stabilised stagnation flame. Combustion and Flame, 162:2569–2581, 2015. doi:10.1016/j.combustflame.2015.03.006.

 

Book Chapter:

  • E. K. Y. Yapp, 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.

 

Refereed Conference Papers:

  • E. K. Y. Yapp. What helps PCB defect classification: crop scale, not generative augmentation. 2026 IET International Conference on Engineering Technology and Applications (IET ICETA), 2026, accepted.
  • E. K. Y. Yapp, H.-Y. Yeh. Generative AI for defect generation in PCBs. 2026 IEEE International Conference on Consumer Electronics – Taiwan (ICCE-Taiwan), 923–924, 2026. doi:10.1109/ICCE-Taiwan71481.2026.11652788.
  • E. K. Y. Yapp, N. C. N. Doan. Anomaly detection on MVTec AD using VQ-VAE-2. 57th CIRP Conference on Manufacturing Systems (CMS), Procedia CIRP, 130:1809–1814, 2024. doi:10.1016/j.procir.2024.10.320.
  • M. de Carvalho, M. Pratama, J. Zhang, H. Chua, E. Yapp*. Towards cross-domain continual learning. 2024 IEEE 40th International Conference on Data Engineering (ICDE), 1131–1142, 2024. doi:10.1109/ICDE60146.2024.00092.
  • 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), 1–6, 2024. doi:10.1109/IAI63275.2024.10729912.
  • H. Ge, J. Zhuang, M. T. T. Nguyen, J. Fong, E. K. Y. Yapp, X. Li, N. Doan. Correlation analysis and predictive modeling for quality prediction in injection moulding process of learning factory. 13th Conference on Learning Factories (CLF 2023), 1–6, 2023. doi:10.2139/ssrn.4470006.
  • 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. V. Tung, 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 multiple-instances, single-output scenario. 2020 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, 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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