Explore video presentations, research project demonstrations, and conference talks from members and collaborators of the Bio-inspired Computing and Machine Learning (BCML) Lab.
KDD 2026: Advancing Graph Few-Shot Learning via In-Context Learning
Conference presentation at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026) presenting novel in-context learning strategies for graph few-shot learning (R. Guan, Y. Wan, C. Guo, B. Cao, F. Giunchiglia, W. Pang, Y. Liu, X. Feng).
Watch on YouTube →KDD 2025: Diffusion-based Non-Autoregressive Solver for TSP
Conference presentation at ACM SIGKDD 2025 introducing an efficient diffusion-based non-autoregressive solver for the Traveling Salesman Problem (TSP) and complex combinatorial optimization problems (M. Wang, Y. Zhou, Z. Cao, Y. Xiao, X. Wu, W. Pang, et al.).
Watch on YouTube →Exploring Public Attention in the Circular Economy (Podcast)
An insightful podcast discussion featuring Prof. Bing Xu and Prof. Wei Pang from Heriot-Watt University exploring public perception, data-driven analysis of public attention, systemic change, and the transition toward a sustainable circular economy (highlighting timestamp t=558s for key research insights).
Watch on YouTube →Public Attention in the Circular Economy (AI Podcast)
A short AI-generated podcast voiced by Prof. Bing Xu and Dr. Wei Pang from Heriot-Watt University, discussing public perception, data-driven analysis of public attention, and systemic change toward a sustainable circular economy.
Watch on YouTube →AI3SD Seminar: Hyperparameter Optimisation of GNNs for Molecular Property Prediction
Presentation delivered at the AI3SD Autumn Seminar VIII ("Molecules, Graphs & Networks"). Dr. Yingfang Yuan presents research supervised by Dr. Wei Pang, Prof. Mike Chantler, and Prof. George M. Coghill, proposing hierarchical genetic algorithms and tree-structured mutation for hyperparameter optimization of graph neural networks (GNNs) in molecular property prediction.
Watch on YouTube →The Accountability Fabric: ISWC 2021 Demo Presentation
Interactive demonstration prepared for the 20th International Semantic Web Conference (ISWC 2021), developed as part of the EPSRC-funded RAINS project (M. Markovic, I. Naja, P. Edwards, & W. Pang). It features The Accountability Fabric, a suite of semantic tools for managing AI system auditability, workflow tracking, and model card generation.
Explore Demo & Video Page →MLap: Machine Learning Auditability Pipeline (RAInS Project Internship)
Presentation of Junhao Song's research internship project for the EPSRC-funded RAInS (Realising Accountable Intelligent Systems) project, introducing MLap (Machine Learning Auditability Pipeline) for tracking model cards, data provenance, and auditability in AI workflows.
KEPS 2021: Plan Verbalisation for Robots Acting in Dynamic Environments
Presentation delivered by Dr. Konstantinos Gavriilidis at the KEPS 2021 workshop (ICAPS 2021) on "Plan Verbalisation for Robots Acting in Dynamic Environments" (co-authored with Yaniel Carreno, Andrea Munafo, Wei Pang, Ron Petrick, and Helen Hastie), introducing natural language explanation and plan verbalisation techniques for autonomous robots.
Manufacturing Immortality: Machine Learning for Self-Healing Materials
As part of the EPSRC-funded Manufacturing Immortality consortium, researchers at Heriot-Watt University apply machine learning algorithms to assist in the discovery, design, and development of novel self-healing materials (27 July 2021).
Watch on YouTube →Accelerated Design of Escherichia coli Genomes with Reduced Size
Presentation by Ioana Gherman detailing the accelerated design of Escherichia coli genomes with reduced size using a mechanistic whole-cell model and machine learning surrogate algorithms.
Watch on YouTube →Hyperparameter Optimisation for Graph Neural Networks
Presentation by Dr. Yingfang (James) Yuan delivered at the Laboratory for AI and Visualisation (LAIV) seminar series in May 2021. The talk explores hyperparameter optimization techniques for Graph Neural Networks (GNNs), addressing structural model selection, parameter tuning, and efficient graph learning methodologies.
Watch on YouTube →SICSA XAI 2021: Are Contrastive Explanations Useful?
Presentation delivered by Dr. James Forrest at the SICSA Workshop on eXplainable Artificial Intelligence (XAI 2021) titled "Are Contrastive Explanations Useful?", exploring natural language generation, contrastive explanation frameworks, and user evaluation in interpretable AI decision systems.
Watch on YouTube →SICSA XAI 2021: Challenges and Future Directions for Accountable Machine Learning
Presentation delivered by Agne Zainyte (co-authored with Prof. Wei Pang) at the SICSA Workshop on eXplainable Artificial Intelligence (XAI 2021) titled "Challenges and Future Directions for Accountable Machine Learning", discussing key challenges, auditability, transparency requirements, and future research directions for accountable machine learning.
Watch on YouTube →SICSA XAI 2021: On Evidence Capture for Accountable AI Systems
Presentation delivered by Prof. Wei Pang at the SICSA Workshop on eXplainable Artificial Intelligence (XAI 2021) titled "On Evidence Capture for Accountable AI Systems", detailing mechanisms for evidence logging, provenance tracking, and auditability in accountable artificial intelligence systems.
Watch on YouTube →SICSA XAI 2021: Towards Accountability Driven Development for Machine Learning Systems
Presentation delivered by Danny Fung at the SICSA Workshop on eXplainable Artificial Intelligence (XAI 2021) titled "Towards Accountability Driven Development for Machine Learning Systems", proposing software engineering methodologies and accountability-driven frameworks for AI model lifecycle management.
Watch on YouTube →Manufacturing Immortality: Self-Healing Materials in a Circular Economy
A presentation produced as part of the EPSRC-funded Manufacturing Immortality project. It explores the design, feasibility, bio-inspired algorithms, and circular economy implications of self-healing materials.
Watch on YouTube →Data Lab MSc Project Competition Presentation
Presentation of Dr. Rachana Patel's award-winning MSc project (supervised by Dr. Wei Pang). Her project won the runner-up award in the prestigious Data Lab MSc project competition across 12 Scottish universities (competing among over 150 students), as well as the Alison Cawsey Memorial Prize for being the most deserving student in her cohort.
Watch on YouTube →Dr. Abubakr Awad's Research Channel
Official YouTube channel of BCML alumnus Dr. Abubakr Awad. The channel features video recordings, technical presentations, and demonstration videos covering machine learning models, graph representations, and intelligent systems.
Visit YouTube Channel →
