Mathematical Research for Blockchain Economy: 3rd International Conference Marble 2022, Vilamoura, Portugal (Lecture Notes in Operations Research) [Hardcover] [Feb 19, 2023] Pardalos, Panos; Kotsireas, Ilias; Guo, Yike and Knottenbelt, William

This book presents the best papers from the 3rd International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2022, held in Vilamoura, Portugal. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focuses on the mathematics behind blockchain to bridge the gap between practice and theory.

Blockchain Technology has been considered as the most fundamental and revolutionising invention since the Internet. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy and security, and showcase solutions and developments to overcome them.

Panos Pardalos is an Emeritus Distinguished Professor of Industrial and Systems Engineering at the University of Florida, USA. Additionally, he is the Paul and Heidi Brown Preeminent Professor of Industrial and Systems Engineering. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Professor Pardalos is a world-leading expert in global and combinatorial optimization.  He is also a member of several  Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, and Computational Management Science. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London, UK. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine. He received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington. He founded InforSense, a software company for life science and health care data analysis, and served as CEO for several years before the company's merger with IDBS, a global advanced R&D software provider, in 2009. He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining and large-scale data management. He is a Senior Member of the IEEE and is a Fellow of the British Computer Society.

William Knottenbelt is a Professor of Applied Quantitative Analysis in the Department of Computing at Imperial College, London, UK. His broad area of research interest is the application of mathematical modelling techniques to real life systems. Specific areas of interest include but are not limited to, modelling and optimisation in parallel queuing systems, modelling of storage systems, stochastic modelling of sport, stochastic modelling of healthcare systems, resource allocation and control in cloud-computing environments, numerical solution of Markov-models and specification techniques for SLA specification, compliance prediction and monitoring.