BCML Lab @Heriot-Watt

Academic Publications

Selected Journal Publications

  1. Liu, H., Jiang, Z., Zhong, H., Li, J., Pang, W., Yuan, Y. (2026). OpenAqua: An automated multi-agent framework for early-stage water treatment train design with retrieval augmentation and critic-based refinement. Water Research, Accepted.

  2. Mao, Y., Li, H., Pang, W., Papanastasiou, G., Yang, G., Wang, C. (2026). SeLoRA: Self-expanding LoRA for high-quality and efficient medical image synthesis. Expert Systems with Applications, Vol 314, Article 131569. DOI ESWA

  3. Yang, Z., Xu, D., Pang, W., Yuan, Y. (2025). Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models. Transactions on Machine Learning Research. OpenReview arXiv TMLR

  4. 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, Vol 16, Issue 10, Article 101392. DOI CODE

  5. 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, Vol 319, Article 113518. DOI KBS

  6. Yu, X., Teng, L., Duang, Z., Zhang, D., Pang, W., Liang, M., Zheng, J., Qiu, L., Xu, Q. (2025). Bias-Variance Decomposition Knowledge Distillation for Medical Image Segmentation. Neurocomputing, Vol 638, Article 130230. DOI Neurocomputing

  7. 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, Vol 36, Issue 8. preprint DOI IEEE TNNLS

  8. 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, Vol 36, Issue 7, pp. 13402–13416. DOI arXiv IEEE TNNLS

  9. 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, Vol 18, Article 100433. arXiv DOI

  10. Wang, Y., Pang, W., Pedrycz, W. (2024). One-Shot Federated Clustering Based on Stable Distance Relationships. IEEE Transactions on Industrial Informatics, Vol 20, Issue 11, pp. 13262–13272. DOI IEEE TII

  11. 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, Vol 11, Issue 24, pp. 40166–40175. DOI IEEE IOT

  12. 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, Article 123704. DOI ESWA

  13. 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, Article 103981. DOI CVIU

  14. 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, Vol 10, pp. 111–128. DOI CAIS (Impact Factor: 5.8)

  15. Awad, A., Coghill, G.M., Pang, W. (2023). A novel Physarum-inspired competition algorithm for discrete multi-objective optimisation problems. Soft Computing, Vol 27, pp. 14699–14719. DOI (Impact Factor: 3.732)

  16. 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, Vol 19, No 4, e1010988. DOI (Impact Factor: 4.779)

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

  18. Wang, Y., Pang, W., Jiao, Z. (2023). An Adaptive Mutual K-nearest Neighbors Clustering Algorithm based on Maximizing Mutual Information. Pattern Recognition, Vol 137, Article 109273. DOI (Impact Factor: 8.5)

  19. Gao, X., Taylor, S., Pang, W., Hui, R., Lu, X., Oxford GI investigators, Braden, B. (2023). Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time. Information Fusion, Vol 92, pp. 64–79. DOI (Impact Factor: 17.56)

  20. Awad, A., Pang, W., Lusseau, D., Coghill, G. (2022). A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications. Artificial Intelligence Review, Vol 56, pp. 1–26. DOI AIR (Impact Factor: 9.588)

  21. Wang, Y., Pang, W., Zhou, J. (2022). An Improved Density Peak Clustering Algorithm Guided by Pseudo Labels. Knowledge-Based Systems, Vol 252, Article 109374. DOI KBS (Impact Factor: 8.038)

  22. 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, Article 108849. DOI KBS (Impact Factor: 8.038)

  23. 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, Vol 600, pp. 22–42. DOI (Impact Factor: 6.795)

  24. 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)

  25. Hassan, M., Wang, Y., Pang, W., Di, W., Li, D., Xu, D. (2021). GUV-Net for high fidelity shoeprint generation. Complex & Intelligent Systems, Vol 8, pp. 933–947. DOI CAIS (Impact Factor: 4.927)

  26. 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)

  27. 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, Vol 139, 105713. DOI Environ. Int. (Impact Factor: 7.943, top journal in Environmental Sciences)

  28. Wang, Y., Wang, D., Pang, W., Miao, C., Tan, A., Zhou, Y. (2020). A Systematic Density-based Clustering Method Using Anchor Points. Neurocomputing, Vol 400, pp. 352–370. DOI Neurocomputing (Impact Factor: 4.072)

  29. 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, Vol 32, pp. 13465–13478. DOI (Impact Factor: 4.664)

  30. Xue, Y., Tang, T., Pang, W., Liu, A.X. (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)

  31. Wang, W., Moreau, N.G., Yuan, Y., Race, P.R., 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)

  32. 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)

  33. 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)

  34. Wu, Z., Pang, W., Coghill, G.M. (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)

  35. Wu, Z., Pang, W., Coghill, G.M. (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)

  36. Pang, W., Coghill, G.M. (2015). Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast. BioSystems, Vol 131, pp. 40–50. DOI CODE (Impact Factor: 1.495)

  37. Pang, W., Coghill, G.M. (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)

  38. Pang, W., Coghill, G.M. (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)

  39. 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 KBS (Impact Factor: 4.104)

  40. 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, pp. 56–63. DOI Neurocomputing (Impact Factor: 1.634)

  41. 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 IEEE SPL (Impact Factor: 1.674)

  42. Pang, W., Coghill, G.M. (2011). An immune-inspired approach to qualitative system identification of biological pathways. Natural Computing, Vol 10, No 1, pp. 189–207. DOI

  43. Pang, W., Coghill, G.M. (2010). Learning Qualitative Differential Equation models: a survey of algorithms and applications. Knowledge Engineering Review, Vol 25, No 1, pp. 69–107. DOI KER (Impact Factor: 1.257)

Selected Conference Papers

  1. Liu, Y., Bi, H., Xia, T., Pang, W., Wang, C. KGRF-Seg: Knowledge-Guided One-Step Rectified Flow Model for Medical Image Segmentation. BMVC 2026, Accepted. BMVC 2026

  2. Song, J., Guasch, L., He, X., Yang, Z., Yuan, Y., Xie, W., Shen, L., Lin, H., Liu, S., Pang, W., Song, S. (2026). Conversational Human Audio-visual Talking Dialogue Generation. ECCV 2026 (main track). arXiv ECCV 2026

  3. Guan, R., Wan, Y., Guo, C., Cao, B., Giunchiglia, F., Pang, W., Liu, Y., Feng, X. (2026). Advancing Graph Few-Shot Learning via In-Context Learning. 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. DOI arXiv VIDEO KDD 2026

  4. Han, X., Chen, K., Dai, X., Liang, D., Peng, M., Pang, W., Giunchiglia, F., Feng, X., Liu, Y., Guan, R. (2026). TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning. 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. DOI arXiv KDD 2026

  5. Yang, Z., Xu, D., Zhang, Z., Chen, K., Wang, X., Xu, Y., Pang, W., Yuan, Y. (2026). Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models. ICML 2026. ICML Project CODE Dataset ICML 2026

  6. Yang, Z., Yang, Z., Zhan, S., Yue, T., Pang, W., Yuan, Y. (2026). SVAgent: Storyline-guided Long Video Understanding via Cross-modal Multi-agent Collaboration. CVPR 2026. CVF arXiv CVPR 2026

  7. Mao, Y., Li, H., Pan, Y., Papanastasiou, G., Qi, P., Yang, Y., Pang, W., Wang, C. (2026). Data-Efficient Medical Segmentation Through Active Knowledge Distillation from a SAM Teacher. IEEE 23rd International Symposium on Biomedical Imaging (ISBI). DOI ISBI

  8. Yang, Z., Pang, W., Yuan, Y. (2026). XR: Cross-Modal Agents for Composed Image Retrieval. The ACM Web Conference 2026. DOI arXiv WWW 2026

  9. Yang, Z., Yuan, Y., Jiang, X., An, B., Pang, W. (2026). InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration. AAAI 2026. arXiv DOI AAAI 2026

  10. Zhang, Y., Han, B., Wang, W., Li, X., Giunchiglia, F., Yang, B., Pang, W., Guan, R., Feng, X. (2026). HumanBBD: Human Motion Prediction with Brownian Bridge Diffusion. Neurocomputing, Vol 638, Article 131707. DOI Neurocomputing

  11. Zhang, Y., Han, B., Li, X., Pang, W., Giunchiglia, F., Feng, X., Guan, R. (2026). SPARD: Single-step Inference with Adaptive Sampling in Residual Diffusion for Human Motion Prediction. AAAI 2026. DOI AAAI 2026

  12. Jiang, Z., Yuan, Y., Hu, L., Pang, W. (2026). STProtein: predicting spatial protein expression from multi-omics data. AAAI 2026 Workshop SPARTA. OpenReview arXiv AAAI SPARTA

  13. Wang, T., Pang, W., Ma, X. (2025). Asymptotically Stable Quaternion-valued Hopfield-structured Neural Network with Periodic Projection-based Supervised Learning Rules. Thirty-Ninth Annual Conference on Neural Information Processing Systems. Proceedings OpenReview arXiv NeurIPS 2025

  14. Liu, Y., Wang, Y., Guo, Y., Pang, W., Li, X., Giunchiglia, F., Feng, X., Guan, R. (2025). Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration. Thirty-Ninth Annual Conference on Neural Information Processing Systems. Proceedings OpenReview arXiv NeurIPS 2025

  15. Yang, Z., Song, J., Luo, Z., Yang, Z., Xu, Y., Lan, J., Zhang, Y., Pang, W., Song, S., Yuan, Y. (2025). ReChar: Revitalising Characters with Structure Preserved and User-Specified Aesthetic Enhancements. SIGGRAPH Asia 2025. DOI

  16. Lihard, A., Pang, W. (2025). 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, pp. 1–5. DOI

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

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

  19. 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

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

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

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

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

  24. Yuan, Y., Wang, W., Li, X., Chen, K., Zhang, Y., Pang, W. (2024). Evolving Molecular Graph Neural Networks with Hierarchical Evaluation Strategy. GECCO 2024. DOI GECCO 2024

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

  26. Markovic, M., Naja, I., Edwards, P., Pang, W. (2021). The Accountability Fabric: A Suite of Semantic Tools For Managing AI System Accountability and Audit. 20th International Semantic Web Conference. PDF Demo ISWC 2021

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

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

  29. Yuan, Y., Wang, W., Pang, W. (2021). A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction. GECCO ‘21, pp. 386–394. DOI arXiv GECCO 2021

  30. Wang, W., Pang, W., Bingham, P., Mania, M., Chen, T., Perry, J. (2020). Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels. IEEE Congress on Evolutionary Computation, Glasgow, pp. 1–7. DOI CEC 2020

  31. Byla, E., Pang, W. (2019). DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence. 19th Annual UK Workshop on Computational Intelligence (UKCI 2019), Portsmouth. Best Paper Award among 45 papers. DOI arXiv CODE UKCI 2019 Best Paper Award among 45 papers UKCI 2019

  32. Awad, A., Usman, M., Lusseau, D., Coghill, G.M., Pang, W. (2019). A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems. GECCO ‘19 Companion, Prague. DOI GECCO 2019

  33. Usman, M., Awad, A., Pang, W., Coghill, G.M. (2019). Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. GECCO ‘19 Companion, Prague. DOI GECCO 2019

  34. Pang, W., Bruce, A.M., Coghill, G.M. (2018). Non-constructive interval simulation of dynamic systems. 31st International Workshop on Qualitative Reasoning (QR 2018, co-located at IJCAI’18), Stockholm. PDF QR 2018

  35. Ma, M., Pang, W., Huang, L., Wang, Z. (2017). A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks. PAKDD 2017, Jeju. DOI PAKDD 2017

  36. Mukhtar, N., Coghill, G.M., Pang, W. (2016). FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. GECCO ‘16 Companion. DOI GECCO 2016

  37. 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. ADMA 2016, Gold Coast. Best Paper Runner Up Award. DOI ADMA 2016 Best Paper Runner Up Award

  38. Jia, C., Pang, W., Fu, Y. (2015). Mode-Driven Volume Analysis Based on Correlation of Time Series. ECCV 2014 Workshops. DOI ECCV 2014 Workshop

  39. Lin, C., Liu, D., Pang, W., Apeh, E. (2015). Automatically Predicting Quiz Difficulty Level Using Similarity Measures. K-CAP 2015, New York. DOI K-CAP 2015

  40. Luo, C., Pang, W., Wang, Z., Lin, C. (2014). Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. 14th IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, pp. 917–922. DOI arXiv CODE ICDM 2014

  41. Luo, C., Pang, W., Wang, Z. (2014). Semi-supervised clustering on heterogeneous information networks. PAKDD 2014, Tainan. DOI PAKDD 2014

  42. Pang, W., Coghill, G.M. (2014). An immune network approach to learning qualitative models of biological pathways. 2014 IEEE Congress on Evolutionary Computation, pp. 1030–1037. DOI IEEE CEC 2014

  43. 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. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014), Belfast, pp. 480–483. DOI BIBM 2014

  44. Pang, W., Coghill, G.M. (2014). Fuzzy qualitative simulation with multivariate constraints. 2014 IEEE International Conference on Fuzzy Systems, pp. 575–582. DOI FUZZ-IEEE 2014

  45. Pang, W., Coghill, G.M. (2013). An Immune Network Approach to Qualitative System Identification of Biological Pathways. 27th International Workshop on Qualitative Reasoning (QR 2013), Bremen, pp. 77–84. QR 2013

BCML Lab @Heriot-Watt

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