Samuel Simon Araya is an Associate Professor at Aalborg University's Faculty of Engineering and Science, specializing in Thermal Engineering. His research focuses on Proton-Exchange Membrane Fuel Cells (PEMFC), High-Temperature PEM Fuel Cells (HT-PEMFC), and Electrolysis technologies. He leads projects like 'Data-driven Diagnosis for High Temperature PEM Fuel Cells' and 'HySTrAm: Hydrogen Storage and Transport using Ammonia.' Key areas include fault diagnosis via Electrochemical Impedance Spectroscopy (EIS), CFD modeling of electrolysis cells, and sustainable energy systems. He supervises PhD students in fuel cell durability, power-to-fuel systems, and green ammonia production. His work aligns with UN SDGs for clean energy and climate action. Notable collaborations involve developing hydrogen storage solutions and optimizing PEM electrolyzers. His recent publications address multi-objective optimization of energy systems, CFD analysis of multiphase flows, and online fault detection methods. Araya has contributed to over 84 research outputs, including journal articles, conference papers, and reviews. His research bridges theoretical models with practical applications in green hydrogen production, fuel cell performance, and electrochemical systems.
Dr. Saeid Habibi is a Tier I Canada Research Chair and a full Professor in the Department of Mechanical Engineering at McMaster University. He leads the Centre for Mechatronics and Hybrid Technologies (CMHT), specializing in advanced automotive research, including battery management systems, autonomous driving, and electric vehicle technologies. His research focuses on control systems, state estimation, and fault detection/diagnosis, with applications in automotive, aerospace, and robotics sectors. Education: B.Sc. (Hons) in Control Engineering, University of Dundee, 1986 Ph.D. in Control Engineering, University of Cambridge, 1990 Research Interests: Dr. Habibi’s work spans control systems, hybrid vehicles, battery management, and mechatronics . He has pioneered the Smooth Variable Structure Filter (SVSF) for state estimation and fault detection. His interdisciplinary expertise bridges academia and industry, with collaborations in automotive electrification, fluid power, and actuation systems. Key projects include the development of prognostic tools for battery health and the creation of advanced test equipment commercialized globally. Key Contributions: Founder and CEO of EECOMOBILITY Inc., a startup focused on AI-driven vehicle diagnostics. Director of the CMHT lab, which houses cutting-edge automotive research facilities. Recipient of prestigious awards, including the ASME and CSME Fellowships, and multiple NSERC grants. Awards & Recognition: 2022 Dean’s Doctoral Mentoring Honour Roll 2015 ASME Fellowship 2012 IEEE Transportation Electrification Best Paper Prize Teaching & Mentorship: Dr. Habibi teaches advanced courses in mechatronics and hybrid vehicle design. He has mentored over 100 graduate students, many of whom now hold leadership roles in industry and academia. His NSERC CREATE initiative promotes interdisciplinary education in vehicle electrification. Labs & Partnerships: CMHT collaborates with industry leaders like Ford, Honda, and Bombardier, focusing on electrified powertrains, battery diagnostics, and autonomous systems. The lab supports large-scale projects funded by NSERC and Ontario’s Research Fund.
Professor Mathioudakis Konstantinos holds a prestigious position at the School of Mechanical Engineering, National Technical University of Athens (NTUA), leading the Laboratory of Thermal Turbomachines. His academic journey includes a Doctorate in Applied Sciences from the Catholic University of Leuven (Belgium) with highest distinction, alongside advanced studies from the Von Karman Institute and NTUA. He has over 35 years of professional experience in academia and industry, including roles as Secretary General for Energy (2009–2015) and professorships since 1990. His research focuses on gas turbine performance optimization, turbomachinery diagnostics, and energy systems, with notable contributions to fault detection algorithms, combustion chamber modeling, and alternative fuels. Key areas include aero-engine preliminary design, marine propulsion systems, and solar hybrid technologies. He has authored over 150 peer-reviewed papers and received multiple awards, including best paper honors from ASME and ImechE. Education: PhD in Applied Sciences (1985), Catholic University of Leuven Fluid Dynamics Diploma (1981), Von Karman Institute Mechanical Engineering (1980), NTUA Awards: ASME Best Paper Awards (2012, 2004, 2003, 2002) PE Publishing Award (2004) Outstanding Service Award (ASME, 2002) Professor Mathioudakis has pioneered diagnostic methodologies combining probabilistic reasoning and neural networks, enhancing fault localization accuracy. His work on transient modeling and steady-state diagnostics improves engine operability and maintenance strategies. He actively contributes to international committees, including leadership roles in ASME’s Controls and Diagnostics Committee. Current duties include coordinating Erasmus programs and advising on propulsion systems for next-generation aircraft. His lab develops tools for turbine disk design, contra-rotating propeller modeling, and solar hybrid gas turbines, bridging academic research with industrial applications.
Dr. Xianke Lin is an Associate Professor in the Department of Automotive and Mechatronics Engineering at Ontario Tech University. He holds a PhD from the University of Michigan-Ann Arbor (2014) and a BEng from Zhejiang University (2009). His research focuses on energy storage systems, hybrid vehicle control, and machine learning-based diagnostics for batteries. He serves as Associate Editor for IEEE Transactions on Transportation Electrification and Frontiers in Energy Research. Education: - PhD in Mechanical Engineering, University of Michigan (2014) - BEng in Mechanical Engineering, Zhejiang University (2009) Research Interests: - Electrified mobility systems - Battery modeling and control - Autonomous vehicle safety - Multiscale/multiphysics modeling - Power electronics optimization - Fault diagnosis and prognostics Publications: Dr. Lin's work spans 50+ peer-reviewed articles in top journals like Journal of Power Sources , Applied Energy , and IEEE Transactions . Recent trends show emphasis on: - Battery health-aware control strategies - Machine learning for prognostics - Connected/automated vehicle energy management Labs & Teams: Directs a research group focusing on electrified transportation systems. Offers graduate/undergraduate research opportunities for qualified candidates.
Dr. Yee Wei Law is a Senior Lecturer at the University of South Australia's (UniSA STEM) Mawson Lakes Campus. His research expertise spans cybersecurity, machine learning, and wireless sensor networks, with a focus on secure communication systems and autonomous vehicle localization. He serves as a Research Degree Supervisor and actively contributes to publications in IoT security, space systems, and privacy-preserving technologies. Research Trends : Recent work emphasizes domain adaptation for predictive maintenance, adversarial attack analysis in computer vision, and quantum key distribution for satellite-IoT networks. Key themes include security in smart grids, UAV-based gesture recognition, and collaborative privacy-preserving algorithms. Publications highlight interdisciplinary applications of machine learning in fault diagnostics, cloud detection via satellite, and secure networking protocols. His work bridges theoretical advancements with real-world implementations in hypersonic tracking and RFID security.
Fotis Kopsaftopoulos is an Assistant Professor at the Department of Mechanical, Aerospace, and Nuclear Engineering (MANE) at Rensselaer Polytechnic Institute (RPI) , where he has been since 2017. His research focuses on intelligent aerospace and mechanical systems , particularly structural health monitoring (SHM) , stochastic modeling , and machine learning applications for damage detection and fault diagnosis. Education: Ph.D. in Mechanical Engineering & Aeronautics (2012), University of Patras Diploma in Mechanical Engineering & Aeronautics (2004), University of Patras Research Interests: Kopsaftopoulos develops data-driven SHM frameworks using stochastic time series models , Gaussian process regression , and guided wave propagation for aerospace structures. His work addresses damage detection under varying loads , rotor fault diagnosis in multicopters, and health monitoring of batteries and adhesively bonded joints . Scientific Awards: 2021 Class of 1951 Outstanding Teaching Award at RPI 2015 Most Practical SHM Solution for Aerospace Award (sponsored by Airbus) 1998–1999 Academic Scholarship of Excellence from the State Scholarship Foundation of Greece Teaching & Leadership: He has taught courses such as System Identification , Aerospace Structures and Materials , and Engineering Dynamics at RPI. He served as co-Editor for Structural Health Monitoring proceedings, Associate Editor for the Structural Health Monitoring Journal , and Topic Editor for Sensors Journal . He has advised 6 PhD, 14 MEng/MS, and 46 BS projects.
Marco Munderloh is a research fellow at the Leibniz University Hannover under the Institute for Information Processing . His career spans advanced video coding, predictive maintenance, and photonic design, with a focus on low-bitrate aerial video compression and motion detection. Dipl.-Ing. in Computer Engineering (2004), Technical University of Ilmenau PhD (2015), Leibniz University Hannover His research interests include: Wave Field Synthesis (WFS) with patented applications in cinema sound systems Predictive maintenance using deep learning and Bayesian neural networks Inverse design for photonic integrated circuits and 3D nanostructures Region-of-interest (ROI) video coding and motion compensation Optical verification of construction materials Marco's recent publications (2025–2018) cover predictive maintenance, video compression, and photonic design. Key trends involve applying machine learning to industrial reliability, optimizing video codecs for aerial surveillance, and computational inverse design for optical devices. Scientific achievements include: Patent for WFS technology in sound reproduction Collaborations span institutions like IEEE, CIRP, EUSPEN, and journals such as APSIPA Transactions and Bautechnik . Contact: Marco.Munderloh@tnt.uni-hannover.de , Marco.Munderloh@web.de .
Wilson Q. Wang is a Professor at Lakehead University's Department of Mechanical and Mechatronics Engineering, where he serves as Lakehead Research Chair, Director of Mechatronics Engineering, and PhD Graduate Program Coordinator. He founded and directs both the Intelligent Mechatronics Systems Lab and Electric Vehicle Lab, supported by CFI/ORF grants. With cross-appointment in Electrical and Computer Engineering, he maintains an adjunct professorship at the University of Waterloo. PhD, Mechatronics Engineering, University of Waterloo MEng, Industrial Engineering, University of Toronto MSc, Mechanical Engineering, Northeastern University, China BASc, Electro-mechanical Engineering, SIT, China Dr. Wang's research focuses on the intersection of artificial intelligence and mechanical systems, specializing in signal processing techniques for condition monitoring, fault diagnosis, and prognostics. His work develops intelligent control systems that enhance machinery reliability through early fault detection and predictive maintenance strategies. The research spans both theoretical development of novel algorithms and practical implementation in industrial settings, with particular emphasis on mechatronic systems and electric vehicles. His publication record demonstrates consistent contributions to high-impact journals including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Instrumentation and Measurement, and IEEE/ASME Transactions on Mechatronics. The work shows an evolving trajectory from fundamental signal processing techniques toward increasingly sophisticated AI-driven prognostic systems, with growing emphasis on real-time implementation and industrial applications. CFI John Evens Leaders Grant Award (2022) Excellent Teaching Award, Lakehead University (2018) Distinguished Researcher Award, Lakehead University (2017) NSERC DG Accelerator Grant Award (2016) Innovation Award, Lakehead University (2015) Dr. Wang has supervised an extensive research team including 9 postdoctoral fellows, 15 PhD students, and nearly 40 MSc students. His research has been supported by multiple NSERC grants (DG, AG, CRD, DAS, EG, IG), CFI (JELF, NOF), ORF, and industry partners including Bombardier Transportation, Siemens Mahon Electric, and Techform Group. He serves as Associate Editor for several prestigious journals including IEEE/ASME Transactions on Mechatronics and IEEE Transactions on Instrumentation and Measurement. He directs two specialized research facilities: the Lab for Intelligent Mechatronic Systems (LIMS) and the Lab for Electric Vehicles (LEV), which support his team's work on smart sensors, fault diagnosis systems, and intelligent control applications across various mechanical and electromechanical systems.
Paolo Maggiore is a Full Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin. He has been actively involved in the Aerospace Engineering Doctoral College since the 19th cycle (2003/2004) through the current 40th cycle (2024/2025). He serves as a member of the PhotoNext Interdepartmental Center for Applied Photonics and the University Internship Commission. His research focuses on digital twin technology, prognostics and diagnostics of aerospace systems, and systems embedded sensors. His scientific work spans aerospace systems and plants within Industrial and Information Engineering, with particular emphasis on aerospace engineering (ERC Sector PE8_1). His research aligns with UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 9 (Industry, Innovation, and Infrastructure). Professor Maggiore's publication record shows a strong focus on aerospace systems engineering, with recent work examining lunar exploration technologies, prognostics and health management systems, digital twin applications, and advanced sensor technologies. His research often combines theoretical modeling with practical aerospace applications, demonstrating a strong connection between academic research and industry needs. Machine learning applications in aerospace systems Digital twin development for aerospace applications Prognostics and health monitoring of critical aerospace components Space exploration technologies and lunar outpost development Advanced sensor integration and diagnostics He has received research funding from various sources including competitive calls, private entities, regional research programs, and commercial contracts, demonstrating the applied nature of his work and strong industry connections. As an educator, Professor Maggiore supervises numerous doctoral students across multiple cycles of the Aerospace Engineering program and teaches courses including Contamination Control Engineering, Advanced Numerical Modeling, and Telemetry in Aerospace Engineering.
Gareth Tucker is a Professor of Railway Systems Engineering at the University of Huddersfield's School of Computing and Engineering, affiliated with the Institute of Railway Research (IRR). He holds an ERDF-funded Smart Rolling Stock Maintenance Research Facility (SRSMRF) and focuses on solving near-term railway engineering challenges such as derailment prevention, suspension design optimization, and robotics-driven maintenance. His expertise spans vehicle-track interaction, RCM, and AI-driven operational planning. Education: PhD in Railway Engineering from Imperial College London (2009), with research on reducing railway track lifecycle costs through management of tangential wheel-rail loading. Academic exchange at Tokyo Institute of Technology (2006-2007). Research Interests: Robotics in train maintenance, predictive maintenance scheduling, condition monitoring, rail safety (e.g., squats analysis), and European Standards development (CEN TC256 WG10 member). Recent work emphasizes ontology-based virtual depots and machine learning applications in maintenance optimization. Grants: European Regional Development Fund (ERDF) grant supporting SRSMRF facility establishment. Collaborations include industry partners across Network Rail, RSSB, and BSI standardization efforts. Labs: Manages the SRSMRF laboratory focusing on automation, robotics, and AI integration in maintenance workflows. Active in developing standards for vehicle-track interface safety through CEN/BSI committees.
Dr. Ann Smith is a Senior Lecturer in Data Science at the Department of Computer Science, University of Huddersfield. She is affiliated with the Centre for Efficiency and Performance Engineering and the Centre for Autonomous and Intelligent Systems. Her research focuses on statistical inference applied to industrial contexts, condition monitoring for predictive maintenance, and the reduction of input parameters in predictive classifiers. She is also engaged in promoting e-learning in mathematics and contributes to evidence-based healthcare diagnostics. Dr. Smith holds a PhD in Engineering, MSc in Applied Statistics, BSc in Mathematics, and PGCE(FE). She is a Knowledge Exchange Champion at the Isaac Newton Institute (University of Cambridge) and a Fellow of both the Institute of Mathematics and its Applications and the Higher Education Academy. Her educational background includes advanced degrees in mathematics and engineering, complemented by a PGCE in further education. Her international collaborations include visiting lecturer roles at Fuzhou Normal University (China) and Universität Greiswald (Germany). Key research areas include fault detection in mechanical systems, non-linear systems analysis, and autonomous abnormality assessment techniques. She actively participates in industry-focused initiatives like the Analysis for Innovators scheme and European Study Groups for Industry (ESGI). Dr. Smith’s scientific awards include Advanced Data Science Professional status and prestigious fellowships. Her work contributes to the UN Sustainable Development Goals, particularly in advancing quality education and industry innovation. She supervises PhD students and has published widely on topics ranging from coffee brewing optimization to genetic disorders linked to nasal polyps and bronchiectasis. Her recent articles highlight advancements in medical imaging techniques (e.g., PET/CT), fault detection in renewable energy systems, and mathematical modeling of industrial processes. She has delivered invited talks on predictive maintenance and participated in numerous international conferences, showcasing her interdisciplinary impact.
Dr Miguel Ángel Sanz Bobi is a Professor at the Institute for Research in Technology (IIT) of Comillas Pontifical University, Madrid, Spain. Since joining IIT in 1985 and becoming faculty on 1 September 1986, he has led over 60 research projects for utilities and industry and advised 20 PhD theses to completion, with four more ongoing. He currently holds the Endesa Chair for AI-driven maintenance. Education: PhD in Industrial Engineering, Universidad Politécnica de Madrid, 1992. Thesis: “Metodología de mantenimiento predictivo basada en análisis espectral y temporal de la historia de equipos industriales…” Research Interests: His work integrates artificial intelligence with power and industrial engineering . Key themes include: Condition monitoring and predictive maintenance of complex industrial assets (power plants, wind turbines, trains). AI techniques: expert systems, fuzzy logic, machine learning, reinforcement learning, generative adversarial networks. Digital twins for anomaly detection and prognosis. Reliability, risk assessment and asset management in power systems. Publication Trends: Recent publications (2020-2025) focus on deep reinforcement learning for microgrids, machine-learning-based health indicators for batteries and insulators, ensemble methods for gas-turbine diagnostics, and open-source asset-management toolkits. Earlier work covers neural networks, multi-agent systems, and probabilistic models for maintenance scheduling. Scientific Awards & Fellowships: No specific awards are listed in the supplied text. Grants & Projects: Dr Sanz-Bobi has coordinated or participated in more than 60 projects, including: Horizon 2020: REDREAM (€957837), ATTEST (€864298) Endesa Chair: 2023-2028 and 2026-2028 cycles on AI-driven maintenance EU/UIC: Guidelines for data-driven railway maintenance (2024-2027) Industry: Iberdrola, Gas y Electricidad Generación, SATE s.r.l., Verescence, Repsol, Ford, Robert Bosch, Abengoa, Alstom, Union Fenosa, Canal de Isabel II, etc. Laboratories & Teams: He heads the Intelligent Systems research area at IIT and leads the Endesa Chair laboratory, which develops AI solutions for predictive maintenance and asset management in power generation, transmission and distribution networks.
Gianluca D'Elia is a Researcher at the University of Modena and Reggio Emilia (UNIMORE), affiliated with the Department of Engineering Sciences and Methods. His research focuses on mechanical systems, vibration analysis, fault diagnosis, and condition monitoring of rotating machinery. He teaches courses in mechatronics, including kinematics/dynamics of machines and diagnostics of mechatronic systems. His work emphasizes non-stationary systems, bearing and gearbox fault detection, and data-driven approaches for predictive maintenance. Research interests include: vibration-based diagnostics, signal processing for machine health monitoring, and development of algorithms for fault localization and prognostics. Key contributions involve the MOIRA-UNIMORE dataset for independent cart systems and advancements in blind deconvolution techniques. His methodologies integrate cyclostationary analysis, machine learning, and experimental modal analysis. Publications highlight innovations in bearing fault detection via harmonic-percussive source separation, prognostics using Hidden Markov Models, and multi-axial vibration testing for engine components. Collaborations include developing the UniVibe software for automated gear diagnostics, and experimental studies on compressor stall/surge phenomena. Education: Not explicitly stated in provided texts. Grants: Implied through experimental setups (e.g., compressor test rig modifications). Labs/Teams: Involved in UNIMORE's mechanical engineering labs, collaborating on projects like wet compression compressor analysis and bearing endurance testing.
MME Yufei GONG is an academic affiliated with the Université Technologique de Troyes (UTT), where she holds a position in the Department of Automatic Control and Systems Engineering. Her research focuses on advanced control systems engineering, with particular emphasis on degradation modeling, prognostics, and stochastic processes in feedback control systems. She is actively involved in supervising PhD students and contributes to the university's research directory. Her work integrates machine learning techniques with traditional control theory to address challenges in system reliability and fault tolerance. Key research areas include stochastic degradation indices, remaining useful life (RUL) estimation, multi-agent systems, and fractional-order control. Dr. Gong has published extensively in top-tier journals since 2019, with notable contributions to predictive maintenance and resilient control system design. Her academic profile reflects a strong background in both theoretical and applied aspects of automatic control systems engineering.
Suresh Perinpanayagam is Professor of Engineering at the University of York, where he leads transformative research in digital/data-centric engineering, digital twins, and AI. His work aims to revolutionize system design by leveraging data and high-performance computing to provide a more realistic and synergistic approach to complex future systems. He is affiliated with the School of Physics, Engineering and Technology at the University of York, where he has established the Data-Centric Engineering and Digital Twinning Synergy (DACEDITS) research group. Professor Perinpanayagam holds a Bachelor's and Master's degree in Aeronautical Engineering from Imperial College, London, and a PhD in Mechanical Engineering from Imperial College, London (Rolls-Royce Vibration University Technology Centre). His research focuses on harnessing digital technologies to revolutionize engineering design, control, development, and through-life supportability within aerospace, transport, energy and built infrastructure domains. Digital twins form a cornerstone of his work, creating virtual replicas of physical systems that are continually updated with real-time data for remote monitoring and predictive analytics. His team combines advanced modeling and simulation with data analytics and machine learning algorithms to gain actionable intelligence from real-time data, facilitating predictive maintenance and fault detection. Key application areas include fusion energy systems, electric/hydrogen aircraft, and autonomous transport vehicles, where the goal is to minimize extensive testing and validation while addressing global challenges in energy, electrification, circular economy practices, and net-zero emissions goals. Analysis of Professor Perinpanayagam's recent publications reveals a strong focus on applying digital twin technology and machine learning to critical engineering systems. His research spans aerospace applications (particularly for more electric aircraft), railway systems, and power electronics reliability. A notable trend is the increasing emphasis on explainable AI for safety-critical systems in aerospace, addressing certification challenges while maintaining high reliability standards. His work consistently bridges theoretical advancements with practical industrial applications, particularly in collaboration with major aerospace companies. Professor Perinpanayagam has secured research grants exceeding £5 million throughout his career. He has cultivated extensive industrial collaborations with leading companies including Boeing, Rolls-Royce, BAE Systems, Thales, Airbus Group, Safran, Meggitt, UKAEA, Heathrow Airport, Assystem, Awaretag and Chitendai Ltd. He has served as Principal Investigator for significant projects such as the Future Landing Gear Phase 2 project (£2 million) and the LAND One project with Airbus, as well as a £1 million project from Safran/ATI for the OLLGA project. As an educator and mentor, Professor Perinpanayagam has been the principal supervisor for seven PhD candidates and one Master's by Research student, all of whom have successfully completed their degrees. He has also supervised Individual Research Projects for thirty-five Master's students. His teaching encompasses data-centric engineering for intelligent systems, covering machine learning, digital twin technology, intelligent transport systems, IoT/sensory systems, predictive analytics, asset management, resilience engineering, project management, and system availability and maintainability. Professor Perinpanayagam leads the Data-Centric Engineering and Digital Twinning Synergy (DACEDITS) research group at the University of York, which pioneers the integration of digital and data technologies to revolutionize engineering design and support. His team brings together cross-disciplinary expertise in advanced modeling and simulation, data analytics, and artificial intelligence to develop next-generation engineering systems that are highly efficient, reliable, and economically viable. The group maintains strong industry partnerships that facilitate the translation of research into practical applications.
