BCML Lab @Heriot-Watt

Academic Publications

Selected Journal Publications

  1. Gherman, M., Sharma, K., Rees-Garbutt, J., Pang, W., Abdallah, Z., Marucci, L., 2025, Accelerated design of Escherichia coli reduced genomes using a whole-cell model and machine learning, Cell Systems, DOI, CODE

  2. Hu, Y., Rao, Y., Yu, H., Wang, G., Fan, H., Pang, W., Dong, J., 2025, Out-of-Distribution Monocular Depth Estimation with Local Invariant Regression, Knowledge Based Systems, In Press.

  3. Yu, X., Teng, L., Duang, Z., Zhang, D.,Pang, W., Liang, M., Zheng, J., Qiu, L., Xu, Q., Bias-Variance Decomposition Knowledge Distillation for Medical Image Segmentation, 2025, Neurocomputing, DOI

  4. Wang, Y., Pang, W., Wang, D., Pedrycz, W., 2025, One-shot Federated K-means Clustering based on Density Cores, IEEE Transactions on Neural Networks and Learning Systems In Press, preprint

  5. Xiao, Y.,Wang, D., Li,B., Chen, H., Pang, W., Wu, X., Li, H., Xu, D., Liang, Y., Zhou, Y., 2024, Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems, IEEE Transactions on Neural Networks and Learning Systems In Press, DOI Arxiv

  6. Song, J. Yuan, Y., Chang, K., Xu, B., Xuan, J., Pang, W.,2024, Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation, Energy and AI, In Press, Arxiv

  7. Wang, Y., Pang, W., Witold Pedrycz, 2024,One-Shot Federated Clustering Based on Stable Distance Relationships, IEEE Transactions on Industrial Informatics DOI

  8. Hu, R., Wang X., Ding X., Zhang, Y., Xin, X., Pang, W., Yu, S., 2024, Unsupervised Domain Adaptation for Skeleton Recognition with Fourier Analysis, IEEE Internet of Things Journal, DOI

  9. Hassan, M., Wang, Y., Wang, D.,Pang, W., Li, D., Zhou, Y, Xu, D, et al, 2024, “Deep learning model for human-intuitive shoeprint reconstruction”, Expert Systems with Applications, Vol 249, DOI.

  10. Huang,L., Bai, X., Zeng, J., Yua, M., Pang, W., Wang, K., 2024,FAM: Improving Columnar Vision Transformer with Feature Attention Mechanism. Computer Vision and Image Understanding, Vol 242,DOI

  11. Hou, W., Wang, Y., Zhao, Z., Cong Y., Pang, W, Tian, Y., 2023, Hierarchical Graph Neural Network with Subgraph Perturbations for Key Gene Cluster Discovery in Cancer Staging, Complex & Intelligent Systems, DOI(Impact Factor: 5.8).

  12. Awad, A., Coghill, G.M. & Pang, W., 2023, A novel Physarum-inspired competition algorithm for discrete multi-objective optimisation problems. Soft Computing DOI(Impact Factor: 3.732)

  13. Gherman,I.,Abdallah, Z., Pang, W, Gorochowski, T., Grierson, C., Marucci, L., 2023, Bridging the gap between mechanistic biological models and machine learning surrogates, PLOS Computational Biology, DOI(Impact Factor: 4.779)

  14. Huang L., Sun S., Zeng J., Wang W., Pang, W, Wang K., 2023, U-DARTS: Uniform-space differentiable architecture search,Information Sciences, Vol 628, Pages 339-349, DOI(Impact Factor: 8.233)

  15. Wang, Y., Pang, W, Jiao Z., 2023, An Adaptive Mutual K-nearest Neighbors Clustering Algorithm based on Maximizing Mutual Information, Pattern Recognition DOI (Impact Factor: 8.5).

  16. Gao, X., Taylor S. Pang, W. Hui, R., Lu, X., 2023, Oxford GI investigators, Braden, B., Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time, Information Fusion, Vol 92, pp. 64-29 DOI (Impact Factor: 17.56)

  17. Awad, A., Pang, W., Lusseau, D., Coghill G., 2022, A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications. Artificial Intelligence Review, DOI. (Impact Factor: 9.588)

  18. Wang Y.,Pang, W., Zhou, J., 2022, An Improved Density Peak Clustering Algorithm Guided by Pseudo Labels, Knowledge-Based Systems, DOI (Impact Factor: 8.038){:target=”_blank”}

  19. Liu Q., Li J., Ren H., Pang, W., 2022, All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing, Knowledge-Based Systems, Vol 249, 108849. DOI. (Impact Factor: 8.038)

  20. Hassan M., Wang. Y., Wang. D., Pang, W., Wang, K, Li, D., Zhou, Y., Xu D., 2022, Restorable-Inpainting: A Novel Deep Learning Approach for Shoeprint Restoration, Information Sciences, DOI (Impact Factor: 6.795)

  21. Wang, X., Liu, X., Pang, W., Jiang, A. 2022, Multiscale Increment Entropy: An approach for quantifying the physiological complexity of biomedical time series, Information Sciences, vol. 586, pp. 279-293. DOI (Impact Factor: 6.795)

  22. Hassan, M., Wang, Y., Pang, W., Di, W., Li, D., Xu, D., 2021 GUV-Net for high fidelity shoeprint generation. Complex & Intelligent Systems(2021). DOI (Impact Factor: 4.927)

  23. Usman, M.,Pang, W & Coghill, G. M, 2020 Inferring Structure and Parameters of Dynamic System Models using Swarm Intelligence, Memetic Computing, vol. 12, pp. 267-282, DOI (Impact Factor: 2.674)

  24. Liu, X, Wang X, Zhou, L, Xia, J, Pang, W, 2020 “Spatial Imputation for Air Pollutants Data Sets Via Low Rank Matrix Completion Algorithm”, Environment International, DOI (Impact Factor: 7.943, top journal in Environmental Sciences)

  25. Wang, Y, Wang, D, Pang, W, Miao, C, Tan, A, Zhou, Y, 2020, “A Systematic Density-based Clustering Method Using Anchor Points”,Neurocomputing, DOI (Impact Factor: 4.072)

  26. Wang, Y, Wang, D, Zhang, X, Pang，W, Miao, C, Tan, A & Zhou, Y, 2020, ‘McDPC: Multi-center Density Peak Clustering’, Neural Computing and Applications DOI (Impact Factor: 4.664)

  27. Xue, Y, Tang, T, Pang, W & Liu, AX 2020, ‘Self-adaptive Parameter and Strategy based Particle Swarm Optimization for Large-scale Feature Selection Problems with Multiple Classifiers’, Applied Soft Computing, Vol. 88, 106031. DOI (Impact Factor: 4.873)

  28. Wang, W, Moreau, NG, Yuan, Y, Race, PR & Pang, W 2019, 'Towards machine learning approaches for predicting the self-healing efficiency of materials' Computational Materials Science, vol. 168, pp. 180-187. DOI (Impact Factor: 4.098)

  29. Tian, X, Pang, W , Wang, Y, Guo, K & Zhou, Y 2019, 'LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models ' BioSystems, vol. 182, pp. 8 16. DOI (Impact Factor: 1.623)

  30. Hu, X, Huang, L, Wang, Y & Pang, W 2019, 'Explosion gravitation field algorithm with dust sampling for unconstrained optimization' Applied Soft Computing, vol. 81, 105500. DOI (Impact Factor: 4.873)

  31. Wu, Z, Pang, W & Coghill, GM 2015, 'An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing' Cognitive Computation, vol. 7, no. 6, pp. 637-651. DOI (Impact Factor: 1.933)

  32. Wu, Z, Pang, W & Coghill, GM 2015, 'An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems' Soft Computing, vol. 19, no. 6, pp. 1595-1610. DOI (Impact Factor: 1.63)

  33. Pang, W & Coghill, GM 2015, 'Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast' BioSystems, vol. 131, pp. 40-50. DOI [CODE]JMorven (Impact Factor: 1.495)

  34. Pang, W & Coghill, GM 2015, 'QML-AiNet: an immune network approach to learning qualitative differential equation models' Applied Soft Computing, vol. 27, pp. 148-157. DOI (Impact Factor: 4.38)

  35. Pang, W & Coghill, GM 2014, 'QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches' Journal of Computational Science, vol. 5, no. 5, pp. 795–808. DOI (Impact Factor: 1.231)

  36. Ji, J, Pang, W , Han, X, Zhou, C & Wang, Z 2012, 'A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data' Knowledge-Based Systems, vol. 30, pp. 129-135. DOI (Impact Factor: 4.104)

  37. Jia, C-C, Wang, S-J, Peng, X-J, Pang, W , Zhang, C-Y, Zhou, C & Yu, Z-Z 2012, 'Incremental multi-linear discriminant analysis using canonical correlations for action recognition' Neurocomputing, vol. 83, no. -, pp. 56-63. DOI (Impact Factor: 1.634)

  38. Yu, Z-Z, Jia, C-C, Pang, W & Zhang, C-Y 2012, 'Tensor Discriminant Analysis with Multi-Scale Features for Action Modeling and Categorization' IEEE Signal Processing Letters, vol. 19, no. 2, pp. 95-98. DOI (Impact Factor: 1.674)

  39. Pang, W & Coghill, GM 2011, 'An immune-inspired approach to qualitative system identification of biological pathways' Natural Computing, vol. 10, no. 1, pp. 189-207. DOI

  40. Pang, W & Coghill, GM 2010, 'Learning Qualitative Differential Equation models: a survey of algorithms and applications' Knowledge Engineering Review, vol. 25, no. 1, pp. 69-107. DOI (Impact Factor: 1.257)

Selected Conference Papers

  1. Wang, T., Pang, W., Ma, X., Asymptotically Stable Quaternion-valued Hopfield-structured Neural Network with Periodic Projection-based Supervised Learning Rules, The Thirty-Ninth Annual Conference on Neural Information Processing Systems, accepted. NeurIPS 2025

  2. Liu, Y., Wang, Y., Guo, Y., Pang, W., Li, X., Giunchiglia, F., Feng, X., Guan, R., Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration, accepted, NeurIPS 2025

  3. Lihard, A. and Pang, W., Stabilise Power Grid Systems from Fluctuating Renewable Energy Sources Production with Artificial Immune System, 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), Trondheim, Norway: IEEE, Mar. 2025, pp. 1–5. DOI

  4. Wang, Y., Zhang, S., Yang, M., Zhang, T., Wang, J., Zhao, Y., Pang, W., IO-K-means: Iterative Optimization for Centroids in K-means, The 21st International Conference on Advanced Data Mining and Applications, ADMA 2025

  5. MERMAID: Multi-perspective Self-reflective Agents with Generative Augmentation for Emotion Recognition, Yang, Z., Song, Z., Song, S., Pang, W., Yuan, Y., Empirical Methods in Natural Language Processing, EMNLP Main 2025, Accepted. EMNLP 2025

  6. Li,B., Li, S., Pang, W., 2025, TS-Net: An Emotion Recognition Network Based on Temporal-Spatial Features of EEG Signals, International Conference on Intelligent Computing, DOI ICIC 2025

  7. Zhao, P., Cao, Z., Wang, D., Song, W., Pang, W., Zhou, Y.. Jiang, Y., Visual-Enhanced Multimodal Framework for Flexible Job Shop Scheduling Problem, ACM Multimedia 2025, accepted.     ACM MM 2025

  8. Yuan, Y., Chen, K., Rizvi, M., Baillie, L., Pang, W., Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness, 2025 International Joint Conference on Neural Networks, accepted. preprint.     IJCNN 2025

  9. Liu, Y., Li, M., Pang, W., Giunchiglia, F., Huang,L., Feng, X., Guan, R., Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning, The 39th Annual AAAI Conference on Artificial Intelligence, Accepted. preprint DOI     AAAI 2025

  10. Wang, M., Zhou, Y., Cao, Z., Xiao,Y., Wu, X., Pang, W., Jiang, Y., Yang, H., Zhao, P., An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Accepted. preprint DOI     KDD 2025

  11. Yuan, Y., Wang, W., Li, X, Chen,X., Zhang, Y.,Pang, W.,. Evolving Molecular Graph Neural Networks with Hierarchical Evaluation Strategy, GECCO 2024, Accepted, DOI     GECCO 2024

  12. Yang, F., Li, X., Wang, M., Zang, H, Pang, W., Wang M., WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series, AAAI’23: Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 10754-10761. DOI     AAAI 2023

  13. Markovic, M, Naja, I., Edwards, P. & Pang , W 2021 , The Accountability Fabric : A Suite of Semantic Tools For Managing AI System Accountability and Audit, In Proceedings of The 20th International Semantic Web Conference (ISWC 2021). URL. Demo Video and code     ISWC 2021

  14. Yuan, Y., Wang, W. & Pang, W, 6 Apr 2021, ‘A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks’, 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp. 482-489, DOI, Open Access, arXiv     CEC 2021

  15. Frachon, L., Pang, W & Coghill, G., 2021 ‘An Immune-Inspired Approach to Macro-Level Neural Ensemble Search’ 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp. 2491-2498, DOI, arXiv     CEC 2021

  16. Yuan, Y., Wang, W. & Pang, W, 2021, ‘A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction’, GECCO ‘21: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 386–394 DOI open access arXiv     GECCO 2021

  17. Wang, W, Pang, W, Bingham, P, Mania, M, Chen T, Perry, J. ‘Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels’, 2020, IEEE Congress on Evolutionary Computation (CEC 2000), Glasgow, United Kingdom. pp. 1-7, DOI.     CEC 2000

  18. Byla, E & Pang, W 2019, 'DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence' Paper presented at 19th Annual UK Workshop on Computational Intelligence, Portsmouth, United Kingdom, 4/09/19-6/09/19. CODE DOI Best Paper Award among 45 papers UKCI 2019

  19. Awad, A, Usman, M, Lusseau, D, Coghill, GM & Pang, W 2019, A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems. in Genetic and Evolutionary Computation Conference Companion (GECCO '19 Companion), July 13–17, 2019, Prague, Czech Republic. ACM, New York, USA, The Genetic and Evolutionary Computation Conference GECCO 2019, Prague, Czech Republic, 13/07/19. DOI     GECCO 2019

  20. Usman, M, Awad, A, Pang, W & Coghill, GM 2019, Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. in Genetic and Evolutionary Computation Conference Companion (GECCO '19 Companion), July 13–17, 2019, Prague, Czech Republic. ACM, New York, USA, The Genetic and Evolutionary Computation Conference GECCO 2019, Prague, Czech Republic, 13/07/19. DOI     GECCO 2019

  21. Pang, W , Bruce, AM & Coghill, GM 2018, Non-constructive interval simulation of dynamic systems. in Z Falomir, GM Coghill & W Pang (eds), Proceeding of the 31st International Workshop on Qualitative Reasoning (co-located at IJCAI'18). pp. 70-77, 31st International Workshop on Qualitative Reasoning (IJCAI'18), Stockholm, Sweden, 13/07/18. URL     IJCAI QR 2018

  22. Ma, M, Pang, W , Huang, L & Wang, Z 2017, A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks. in J Kim, K Shim, L Cao, JG Lee, X Lin & YS Moon (eds), The Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2017. Lecture Notes in Computer Science, vol. 10234, Springer , Cham, pp. 750-761, The Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, Korea, Republic of, 23/05/16. DOI     PAKDD 2017

  23. Mukhtar, N, Coghill, GM & Pang, W 2016, FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. in T Friedrich, F Neumann & AM Sutton (eds), GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM, pp. 1007-1010. DOI     GECCO 2016

  24. Wang, Y, Du, W, Liang, Y, Chen, X, Zhang, C, Pang, W & Xu, Y 2016, PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. in J Li, X Li, S Wang, J Li & QZ Sheng (eds), Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. Lecture Notes in Artificial Intelligence (LNAI), Springer International Publishing, pp. 714-725, ADMA 2016, Gold Coast, Australia, 12/12/16. DOI Best Paper Runner Up Award

  25. Jia, C, Pang, W & Fu, Y 2015, Mode-Driven Volume Analysis Based on Correlation of Time Series. in L Agapito, MM Bronstein & C Rother (eds), Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I. Lecture Notes in Computing Science, vol. 8925, Springer , Zurich, pp. 818-833. DOI     ECCV 2014 Workshop

  26. Lin, C, Liu, D, Pang, W & Apeh, E 2015, Automatically Predicting Quiz Difficulty Level Using Similarity Measures. in Proceedings of The 8th International Conference on Knowledge Capture (K-Cap)., 1, ACM, pp. 1-8, K-CAP 2015 - The 8th International Conference on Knowledge Capture, New York, United States, 7/10/15. DOI     K-CAP 2015

  27. Luo, C, Pang, W , Wang, Z & Lin, C 2014, Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. in R Kumar, H Toivonen, J Pei, JZ Huang & X Wu (eds), 2014 IEEE International Conference on Data Mining (ICDM 2014). IEEE proceedings, IEEE Explore, pp. 917-922, 14th IEEE International Conference on Data Mining, Shenzhen, China, 14/12/14. DOI,[CODE]Hete-CF ` ICDM 2014 top data mining conference`

  28. Luo, C, Pang, W & Wang, Z 2014, Semi-supervised clustering on heterogeneous information networks. in VS Tseng, T Bao Ho, Z-H Zhou, ALP Chen & H-Y Kao (eds), Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II. Lecture Notes in Computer Science, vol. 8444, Springer , pp. 548-559. DOI     PAKDD 2014

  29. Pang, W & Coghill, GM 2014, An immune network approach to learning qualitative models of biological pathways. in 2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014). IEEE Press, pp. 1030-1037. DOI     CEC 2014

  30. Jiang, Y, Wang, Y, Pang, W , Chen, L, Sun, H, Liang, Y & Blanzieri , E 2014, Essential Protein Identification based on Essential Protein-Protein Interaction Prediction by Integrated Edge Weights. in _The IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014)._IEEE Press, Belfast, UK, pp. 480-483. DOI     BIBM 2014

  31. Pang, W & Coghill, GM 2014, Fuzzy qualitative simulation with multivariate constraints. in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014). IEEE Press, pp. 575-582. DOI     FUZZ-IEEE 2014

  32. Pang, W & Coghill, GM 2013, An Immune Network Approach to Qualitative System Identification of Biological Pathways. in M Bhatt, P Struss & C Freksa (eds), 27th International Workshop on Qualitative Reasoning (QR 2013). Universität Bremen / Universität Freiburg, Bremen, Germany, pp. 77-84, 27th International Workshop on Qualitative Reasoning, Bremen, United Kingdom, 27/08/13.     QR 2013

BCML Lab @Heriot-Watt

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