Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)
Professor Eyad Elyan is a leading academic and researcher at Robert Gordon University's School of Computing, Engineering and Technology, where he serves as a Professor in Machine Learning and Computer Vision. He is the founder and head of the Machine Vision Research Group, driving innovative research in applied computer vision and deep learning with significant industry impact. Professor Elyan's research focuses on converting complex and unstructured data into knowledge and actionable insights, with particular emphasis on learning from images, videos, and other forms of unstructured data. His work spans engineering diagrams processing, remote inspection for oil and gas installations, intelligent condition monitoring of offshore assets, predictive maintenance, biometric applications, and medical datasets analysis. His expertise in ensemble-based learning and learning from unstructured and imbalanced datasets has been successfully implemented in various real-world applications. Professor Elyan was awarded the UK Knowledge Transfer Partnership Academic of the Year Award in 2023 for his transformative work in developing pioneering AI solutions for the oil and gas sector, and was a finalist for the Scottish Knowledge Exchange Award in 2024. These recognitions highlight his exceptional ability to bridge academic research with practical industry applications. His research has been supported by various public funding bodies including Innovate UK, the Data Lab Innovation Centre, Oil and Gas Innovation Centre (OGIC), NetZero Technology Centre (NTZ), and Historic Environment Scotland. Professor Elyan has supervised twelve PhD students to completion and examined more than fifteen others. He plays an active role in the academic community as a Fellow of the British Higher Education Academy and The International Neural Network Society, and serves as the Scotland Data Lab Innovation Centre Ambassador. Under Professor Elyan's leadership, the Machine Vision Research Group has developed innovative solutions including an end-to-end system for processing Piping and Instrumentation Diagrams (P&ID), AI-driven inspection systems for oil and gas assets, and defect recognition technologies. His work demonstrates a consistent commitment to translating cutting-edge research into practical tools that address real-world challenges, particularly in the energy sector.
Professor John McCall is a distinguished academic and researcher at Robert Gordon University's School of Computing, Engineering & Technology, where he previously served as Head of School. He currently serves as Director of the National Subsea Centre, leading initiatives to accelerate energy transition through smart technologies applied to industrial and environmental challenges in subsea and related marine sectors. With over 25 years of research experience in nature-inspired computing and artificial intelligence, Professor McCall has established himself as a leading expert in optimization algorithms and explainable AI. Professor McCall's research interests span data science, artificial intelligence, nature-inspired computing, and optimization, with significant applications in energy transition and subsea technologies. His work bridges theoretical foundations with practical implementations, having founded two spinout companies that deliver real-world optimization solutions to industry. He leads both the Complex Optimisation Research Group and the Computational Intelligence Research Group, where his team explores cutting-edge approaches to solving complex computational problems. Analysis of Professor McCall's recent publication record (2023-2025) reveals a strong focus on explainable AI, particularly in the context of evolutionary computation and metaheuristics. His research demonstrates an increasing emphasis on practical applications in energy systems, transportation, and subsea technologies, reflecting his commitment to addressing real-world challenges related to climate change and industrial transformation. The interdisciplinary nature of his work is evident in publications spanning computer science, operations research, renewable energy, and transportation planning. Lead of the Computational Intelligence Research Group ResearcherID: G-1423-2011 Scopus Author ID: 36797474900 ORCID: https://orcid.org/0000-0003-1738-7056 Professor McCall is actively involved in mentoring the next generation of researchers, currently supervising multiple PhD students across diverse topics including explainability of non-deterministic solvers, optimization of electrical machines, and computational intelligence applications in hydrocarbon systems. His research is supported by numerous grants from industry and government sources, with projects totaling millions of pounds focused on solving challenges in energy transition and smart technologies. At the National Subsea Centre, Professor McCall leads a multidisciplinary team working on digital twin technologies, subsea AI applications, and data-driven solutions for the energy sector. His work emphasizes collaboration between academia and industry to develop transformative solutions that address both current challenges and future opportunities in the subsea domain.
Dr. Tri M. Le serves as Associate Professor of Mathematics and Computer Science and Program Coordinator for the M.S. in Data Science at Mercer University's College of Professional Advancement, Department of Informatics and Mathematics. He joined Mercer in 2017 after working as a Predictive/Computational Statistician at the University of Nebraska-Lincoln, bringing expertise in statistical software (SAS, SPSS, R) and extensive teaching experience at undergraduate and graduate levels. His educational background includes: PhD and MA in Statistics, University of Missouri-Columbia (2014) MS in Probability and Statistics, Ho Chi Minh City University of Natural Sciences (2002) BS in Mathematics and Informatics, Ho Chi Minh City University of Natural Sciences (1999) Dr. Le's research spans Bayesian analysis, decision theory, spatio-temporal modeling, and machine learning. His work addresses fundamental questions in model uncertainty, prediction reliability, and the interpretability-performance trade-off in statistical learning. He has published in leading journals including Journal of Machine Learning Research and Bayesian Analysis, with recent focus on model averaging superiority over selection and theoretical foundations of ensemble methods. Analysis of his 2016-2022 publications reveals consistent focus on Bayesian predictive modeling, with increasing emphasis on interpretable machine learning. His work bridges theoretical statistics and practical applications in healthcare analytics and environmental systems, demonstrating interdisciplinary relevance through collaborations in geoscience and criminal justice research. Dr. Le actively contributes to the academic community as reviewer for Bayesian Analysis and International Conference on Fuzzy Systems and Data Mining, and as Session Chair for the Joint Statistical Meetings. His leadership extends to Mercer's Tenure and Promotion committee and the M.S. in Data Science program coordination. While no dedicated research lab is mentioned, Dr. Le's role as Program Coordinator provides structured opportunities for students through the M.S. in Data Science curriculum. His courses in Data Analytics and Healthcare Data Analytics offer practical training grounded in his research on predictive modeling and statistical inference.
Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors. Dr. Osman's research spans three interconnected domains: Machine Learning & Data Analytics : Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications Operations Research : Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics Supply Chain Management : Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors. Professional credentials include: IBM Mastery Certificate in Predictive Data Analytics Professional Engineer (PEng) license from Ontario Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.
Dr Jon Stammers serves as the Senior Theme Lead for Data, Connectivity and AI within the Integrated Manufacturing Group at the Advanced Manufacturing Research Centre (AMRC), University of Sheffield. He joined the AMRC in 2013, initially working in the Machining Group's Process Monitoring and Control team, and has since taken on leadership of the Data, Connectivity and AI theme. Additionally, he has lectured at the AMRC Training Centre and is an active member of the Centre for Machine Intelligence and the IET Manufacturing Technical Network. Stammers holds an MEng and PhD in Electronic Engineering. His doctoral research investigated automated identification of urban and natural audio signals using time-domain feature extraction and ensemble neural network classifiers. His primary research interests focus on leveraging data from connected manufacturing processes to enable Smart Factories. This includes developing open-source data architectures, data visualization techniques, computer vision applications, AI and machine learning algorithms, data security measures, and data science methodologies. He is particularly interested in how AI can serve as a practical tool to enhance daily manufacturing operations and the broader societal impacts of technology adoption in industrial settings. Analysis of his recent publications reveals a consistent emphasis on machine tool health monitoring, anomaly detection in machining processes, and the integration of AI for predictive maintenance. His work bridges theoretical advancements in signal processing and machine learning with practical industrial applications, contributing to more efficient and reliable manufacturing systems. No scientific awards, prizes, or fellowships were mentioned in the provided information. Stammers has previously lectured at the AMRC Training Centre, contributing to workforce development in advanced manufacturing. While no formal PhD or Master's advisees are listed in the provided information, his role in training is evident through his educational contributions. He has secured significant research funding as Principal Investigator and Co-Investigator on multiple projects, including the ATI-funded "Securing Aerospace Manufacture in the UK" (£974,000), Innovate UK's "Data-driven manufacturing" (£111,000), EPSRC's "Autonomous Method for Detecting Cutting Tool and Machine Tool Anomalies" (£1.02M), and several others totaling over £2.5 million. His current projects span AI for machining design, hydrogen storage, and geospatial AI for housing layouts. Stammers leads the Data, Connectivity and AI theme at the AMRC, which focuses on enabling Smart Factories through innovative data and AI solutions. He is part of the Integrated Manufacturing Group, a key research team within the AMRC dedicated to advancing manufacturing technologies.
