Miguel Vieira is an Assistant Professor at the Faculty of Engineering , Lusófona University, Lisbon, and a Researcher at RCM2+. He also holds Visiting Researcher positions at Instituto Superior Técnico and Universidade de Coimbra. PhD in Leaders for Technical Industries (2017) from Instituto Superior Técnico (part of MIT Portugal Program) Focus on industrial engineering challenges, including optimization methods, simulation models, and machine learning for supply chain and production systems Developed mathematical programming models (LP, IP, MILP, MINLP) and decomposition methods for complex manufacturing problems His research spans deterministic and stochastic scenarios, with a focus on Industry 4.0, human-robot interaction, and decision-support systems. He has published 8 journal articles, 11 book chapters, and contributed to 7 events, including organizing 7 conferences. Recent publications emphasize data-driven optimization , risk assessment , and sustainable strategies in biomass and fuel distribution supply chains, alongside machine learning integration in manufacturing. His work includes a Q-Learning algorithm for assembly lines and stochastic programming applications. Awards & Honors : 6 scientific awards (specifics not listed) Miguel has supervised 3 MSc dissertations and collaborated with 53 researchers. His projects include risk assessment in process design, dynamic scheduling for Industry 4.0, and enhancing industrial optimization via deep learning.
Stefano Marchesiello is a Full Professor of Applied Mechanics at the Polytechnic University of Turin, Department of Mechanical and Aerospace Engineering (DIMEAS), a position he has held since 2019. His academic work spans theoretical studies, numerical applications, and experimental tests within the field of Applied Mechanics. He maintains active roles in doctoral education, serving on mechanical engineering doctoral colleges from 2013/2014 through 2024/2025, and teaches courses including Dynamics and Identification of Nonlinear Systems, Dynamics of Mechanical Systems, Vibration Mechanics, and Machine Mechanics for Aerospace Engineering. Marchesiello's research focuses on modal analysis and identification, damage diagnosis in structures and construction materials, damping systems, mechanical vibrations, and nonlinear dynamics. His primary research lines include vehicle-bridge dynamic interaction, dynamic identification techniques in linear and nonlinear fields, damage identification, vibrations of continuous systems with non-proportional damping, innovative vibration damping devices, diagnostics and monitoring of rotating systems, and pantograph-catenary dynamic interaction. His work bridges theoretical mechanics with practical engineering applications, particularly in transportation infrastructure and mechanical systems. His recent publications demonstrate a strong focus on nonlinear system identification, structural health monitoring, and vibration analysis across various mechanical and aerospace applications. Marchesiello's research shows increasing integration of machine learning techniques with traditional mechanical engineering approaches, particularly in system identification and damage detection. His work spans from fundamental nonlinear dynamics to practical applications in railway systems, rotating machinery, and structural components. Certificate of reviewing awarded by Journal of Sound and Vibration - Elsevier, Netherlands (2013) Certificate of Excellence in Reviewing - Mechanical Systems and Signal Processing 2013 awarded by Elsevier, Netherlands (2013) Marchesiello serves as Scientific Director for multiple commercial research contracts, particularly with Officina Fratelli Bertolotti SpA, focusing on vibration damping systems for railway catenaries and rotor dynamics modeling. He has led research projects from 2008 through 2023, demonstrating sustained research leadership and industry collaboration. His editorial work includes membership on the Editorial Board of SHOCK AND VIBRATION since 2018, and he has served on program committees for the International Conference on Damage Assessment of Structures (DAMAS) across multiple years. He is actively involved with the Dynamics of Mechanical Systems and Identification research group (DIMEAS), which focuses on developing advanced methods for analyzing and identifying mechanical systems with both linear and nonlinear behaviors. His research integrates computational modeling, experimental validation, and practical applications across multiple engineering domains.
Brian P. Anderson is a Professor at the Wyant College of Optical Sciences of the University of Arizona , where he serves as Interim Dean. His research focuses on quantum fluid dynamics and Bose-Einstein condensates (BECs), utilizing laser cooling and optical trapping techniques to study superfluid behavior, quantized vortices, and quantum turbulence. Anderson has held faculty appointments in both the College of Optical Sciences and the Department of Physics since 2001. Ph.D., Stanford University (1999) M.S., Stanford University (1995) B.A., Rice University (1992) His work explores the generation, manipulation, and dynamics of quantized vortices in BECs, connecting to broader phenomena like phase transitions and turbulence in quantum systems. He has pioneered methods for controlled vortex studies using tailored optical potentials and contributes to understanding two-dimensional quantum turbulence. Key trends in his publications include investigations into vortex dynamics in BECs, quantum turbulence, phase manipulation, and the application of optical techniques to study superfluidity. His research bridges experimental, numerical, and theoretical approaches. Scientific Awards American Physical Society Fellow (2013) American Physical Society Outstanding Referee (2011) Army Research Office Young Investigator Award (2004) National Research Council Postdoctoral Associateship (1999-2001) Presidential Early Career Award for Scientists and Engineers (2004) Anderson leads the Bose-Einstein Condensation Lab at the University of Arizona. His teaching includes graduate courses in optical sciences, such as OPTI 570A and OPTI 571L.
Charles Bienvenue is an Assistant Professor in the Department of Mechanical Engineering at Polytechnique Montréal. His academic credentials include a PhD in Biomedical Engineering and a Bachelor's degree in Engineering Physics, both from Polytechnique Montréal. His research expertise spans multiple domains: Nuclear engineering and reactor design/operation Applied mathematics and mathematical physics Mathematical modeling of physical systems Nuclear and particle physics Medical physics applications Professor Bienvenue's primary research interests focus on nuclear reactor physics, applications of ionizing radiation in medical physics, and modeling the transport of neutral and charged particles using finite element methods. His work has significant implications for radiation therapy planning and nuclear reactor safety analysis. His research aligns with Polytechnique Montréal's centers of excellence in Modeling and Artificial Intelligence, as well as Energy, Water and Resources, and Human Health. As an educator, he teaches ENE6101: Static Physics of Reactors, drawing on his extensive knowledge of nuclear engineering principles. His active publication record demonstrates ongoing research productivity with multiple publications in high-impact journals including Nuclear Science and Engineering, Journal of Computational Physics, and Physical Review Applied. Professor Bienvenue's research methodology emphasizes deterministic algorithms for high-accuracy particle transport simulations, with particular attention to coupled photon-electron-positron systems. His work bridges theoretical nuclear physics with practical medical applications, particularly in radiation therapy planning.
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. Ciprian Zavoianu is an academic researcher at Robert Gordon University (RGU) in the School of Computing, Engineering & Technology. He leads the Net Zero Operations research programme at the National Subsea Centre and is affiliated with the Complex Optimisation Research Group. His work focuses on applying artificial intelligence, particularly evolutionary computation algorithms, to solve complex real-world optimization problems with practical engineering applications. Dr. Zavoianu earned his academic qualifications from West University of Timisoara, Romania (BSc and MSc in Computer Science) and Johannes Kepler University Linz, Austria (PhD in Computer Science, 2015). His doctoral research focused on enhancing multi-objective evolutionary algorithms for computationally-intensive optimization problems. His primary research interests include: Evolutionary Computation Multi-Objective Optimization Data Mining & Machine Learning (particularly for surrogate modeling) Timetabling and Rostering Parallel/Distributed Computing Dr. Zavoianu's recent publications (2021-2025) demonstrate a strong focus on applying optimization techniques to transportation systems, electrical machine design, and sustainable energy solutions. His work consistently addresses the challenge of computationally expensive optimization through innovative surrogate modeling approaches, enabling practical applications of evolutionary algorithms to real-world engineering problems. He has secured multiple research grants including 'Data For Net Zero' and 'Ferry Passenger and Freight Modelling for Shetland,' demonstrating the practical relevance and industry applicability of his research. Dr. Zavoianu actively supervises five PhD/EngD students across diverse topics including predictive analytics for subsea installations, optimization of electrical machines, and operations optimization for harbor operations. His supervision approach emphasizes bridging theoretical algorithm development with practical implementation in engineering contexts. His laboratory work is centered around the National Subsea Centre, where he leads the Net Zero Operations research programme, focusing on sustainable solutions for the energy transition through advanced computational methods.
