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Jagannathan SarangapaniView profile

Professor

• Neural Networks
• Adaptive Control
• Cyber-Physical Systems
• +5 more

Jagannathan Sarangapani is the William A. Rutledge - Emerson Electric Company Distinguished Professor at the Missouri University of Science and Technology, where he holds a primary appointment in the Department of Electrical and Computer Engineering. He also holds courtesy appointments in Computer Science and Engineering Management and Systems Engineering. His research focuses on adaptive control, neural networks, cyber-physical systems, robotics, and big data prognostics. He has supervised over 36 doctoral and 32 M.S. students, with current research funded to the tune of $47M (shared credit: $13.8M). His awards include the IEEE Fellow, Fellow of the National Academy of Inventors, and multiple teaching excellence awards. Education: Ph.D. in Electrical Engineering (University of Texas at Arlington, 1994), M.S. (University of Saskatchewan, 1989), and B.E. (Anna University, India, 1986). Research interests include learning algorithms, neural network decision-making, networked control systems, and healthcare applications. Recent grants include projects on human-machine teaming, resilient deep learning control, and UAV formation control. He has authored/co-authored over 500 publications, including 21 patents and five books. Awards highlight his contributions: IEEE Control Systems Society’s Transition to Practice Award (2018), NSF CAREER Award (2001), and multiple teaching commendations. He directs the Embedded Control Systems and Networking Laboratory and served as Site Director for the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems.

Stephen K. RobinsonView profile

Professor

• Turbulence flow physics and modeling
• Experimental techniques
• Instrumentation
• +7 more

Stephen K. Robinson is a Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis. His work focuses on turbulence flow physics, experimental techniques, and aerospace applications, with recent emphasis on human-robot interaction, space systems, and autonomous technologies. Research interests include turbulence modeling, advanced instrumentation, 3D visualization, and safety engineering. His work integrates applied aerodynamics with cognitive psychology to address challenges in space exploration, such as haptic interfaces, VR training, and human performance under extreme conditions. Recent publications highlight innovations in spacecraft maintenance, robotic systems, and autonomous glider design. His projects often combine hardware development with simulation tools, such as digital twins and real-time performance metrics for manual spacecraft operations. Robinson collaborates on NASA-related initiatives including EVA training systems, habitat technology, and lunar construction dynamics. He has pioneered solutions for CO2 removal systems, spacesuit glove ergonomics, and radiation-resistant materials for extraterrestrial missions.

Kjell Gunnar RobbersmyrView profile

Professor

• Mechatronics
• Machine Design
• Condition Monitoring
• +5 more

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

• Industrial Artificial Intelligence
• Digital Twin Technology
• Condition Monitoring
• +7 more

Pradeep Kundu serves as an Assistant Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology (Bruges Campus), where he leads the M-Group Asset Performance Management subdivision. He is an active member of Leuven.AI Institute for Artificial Intelligence and holds governance roles in the Mechanical Engineering Department Council and Faculty Council. His research centers on Industrial Artificial Intelligence integrated with Digital Twin Technology, focusing on three core domains: rotating machinery condition monitoring (fault diagnosis/prognosis), manufacturing quality control (tool wear/surface monitoring), and production process optimization. Key methodological contributions address data scarcity through synthetic data generation (Digital Twin/Generative AI) and enhance model robustness via Physics-Informed Machine Learning, Hybrid Modeling, and advanced Statistical Regression. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on predictive maintenance for mechanical systems, utilizing diffusion models for damage imaging, entropy-based domain adaptation for bearing failure prediction, and sensor fusion techniques. His work consistently bridges physics-based modeling with deep learning across rotating machinery, structural health monitoring, and smart manufacturing applications. Dr. Kundu currently leads multiple funded projects including 'Intelligent Prognosis of Rotating Machines in Industry 4.0 using Generative AI' (2024-2025) and 'Digital Twin Framework for Fleet-Level Feed Drive Systems Health Assessment' (2023-2027), addressing critical challenges in data-limited industrial AI deployment. His research directly supports Industry 4.0 transformation through maintenance optimization and quality control innovations. He actively contributes to academic governance as member of the OC Smart Operations and Maintenance in Industry committee and the Mechanical Engineering Department Council, while his research group within the Mecha(tro)nic System Dynamics unit develops practical AI solutions for industrial asset performance management.

Keith Edgar BrownView profile

Associate Professor

• Underwater Acoustics
• Bio-inspired Sonar Systems
• Autonomous Underwater Vehicles
• +5 more

Keith Edgar Brown is an Associate Professor at Heriot-Watt University, affiliated with the School of Engineering & Physical Sciences and the Institute of Sensors, Signals & Systems. He also serves as Head of the Discipline of Electrical, Electronic and Computer Engineering. He holds a BSc (1984) and PhD (1988) in Electronic and Electrical Engineering from the University of Edinburgh, where his doctoral research focused on knowledge-based fault diagnosis in microwave radio relay equipment. His research centers on underwater acoustics, bio-inspired sonar systems, and intelligent diagnostics for complex systems, with applications in autonomous underwater vehicles and subsea infrastructure monitoring. His work contributes to UN Sustainable Development Goals related to ocean health and sustainable energy systems. Research interests include: Underwater acoustics and bio-inspired sonar design Condition-based monitoring of subsea power cables using machine learning Autonomous systems operation in hostile environments Acoustic signal processing and source localization Recent publications emphasize machine learning-driven solutions for underwater asset management, with notable contributions to IEEE journals and conferences. His work has been recognized through awards like the 2018 'Telling tails of Public engagement' prize. Brown collaborates extensively on projects involving subsea infrastructure diagnostics and robotics applications.

John BreslinView profile

Professor

• Data Science
• Artificial Intelligence
• Social Semantics
• +7 more

John Breslin is a Personal Professor in Electronic Engineering at the University of Galway, leading the TechInnovate/AgInnovate programs. His roles include Director of the Insight (Data Analytics) and VistaMilk (AgTech) research centers. He has authored 350+ publications and co-created the SIOC framework, widely used in web applications. His research spans Data Science, AI, Semantic Web, IoT, and Entrepreneurship. He has won multiple awards, including the Best Irish-Published Book and IIA Net Visionary Awards. His teaching spans 25 years, covering topics like Digital Control Systems and Innovation. He co-founded boards.ie, StreamGlider, and leads initiatives in Galway's innovation ecosystem. Research Interests: Data Science, AI, Social Semantics, IoT, Agricultural Technology, and Innovation. He is ranked top in Engineering and Computer Science citations at the University of Galway. His work contributes to UN SDGs related to Industry, Innovation, and Infrastructure. Education: Details not explicitly provided in text. Collaborations include Bosch, Cisco, IBM, and Harvard Medical School. Active in industry partnerships and startups, including the PorterShed Innovation District. Awards: Best Paper Awards, Entrepreneurship Recognition, and Academic Excellence. Supervised nearly 30,000 ECTS across modules, with a focus on graduate and postgraduate tech innovation programs.

• Mechanical Engineering Asset Management
• System Reliability Engineering
• Condition-Based Maintenance
• +8 more

Tieling Zhang is an Associate Professor at the University of Wollongong's School of Mechanical, Materials, Mechatronic and Biomedical Engineering. He holds a PhD from Tokyo University of Marine Science and Technology. His research focuses on system reliability engineering, machine learning applications, energy storage systems, and pipeline integrity management. He has published over 100 peer-reviewed articles and secured over AUD 7 million in research funding. Research Interests: Statistical Data Modelling, Big Data Analytics, Bayesian Inference, System Reliability Modelling, Degradation Modelling, Wind Turbine Maintenance, Battery Management, Smart Grids. Awards: Recipient of three Best Paper Awards from international conferences. He leads the Engineering Asset Management and Systems Engineering Research Group and serves on editorial boards of journals like Information and Machines . Teaching: Contributes to Master's programs in Engineering Asset Management and Engineering Management. Supervises PhD/Master’s students on topics like battery diagnostics, UAV wildfire management, and human-robot collaboration safety. Funding Highlights: Includes grants for battery health estimation, pipeline integrity, and railway asset management. Collaborates with industry partners like the Australasian Centre for Rail Innovation and Energy Pipeline Cooperative Research Centre.

Andrew MillsView profile

Professor

• Aerospace Systems
• Control Systems
• Health Monitoring
• +7 more

Professor Andrew Mills holds the position of Professor of Innovation in Aerospace Systems Monitoring and Control at the School of Electrical and Electronic Engineering, University of Sheffield. He also serves as Deputy Director of the Control, Monitoring and Systems University Technology Centre (UTC), focusing on industry and research council-funded projects. His work emphasizes applied research in aerospace, including sensing solutions, health monitoring, and control systems for gas turbines. He collaborates closely with Rolls-Royce and coordinates PhD/MSc projects to bridge academic and industrial research. Education : MEng (Engineering), Chartered Engineer (CEng) from the University of Sheffield. Research Interests : Gas Turbine Health Monitoring Embedded Systems in Extreme Environments Fleet Monitoring Algorithms Vision-Based Sensing Systems Thermal System Control Laws Prognostics and Health Management (PHM) Grants & Projects : Lead Investigator (PI) and Co-Investigator (CI) on grants such as: "Advanced Intelligent EHM" (TSB HITEA II, 2014–2017) "Wireless Sensing for Monitoring" (Rolls-Royce, PI, 2013–2015) "Autonomous Intelligence for Civil UAS" (Rolls-Royce, PI, 2012–2013) Labs & Teams : Leads the Control, Monitoring and Systems UTC, fostering collaboration between academia and industry for aerospace innovation.

• Actuators
• Automation and robotics
• Digital twin
• +6 more

Andrea De Martin is a fixed-term researcher at the Polytechnic University of Turin , affiliated with the Department of Mechanical and Aerospace Engineering (DIMEAS) and the Power Electronics Innovation Center (PEIC) . His work focuses on Prognostics and Health Management (PHM) for robotic and aerospace systems, particularly critical components like harmonic drives and flight control actuators. Key research areas: Actuators, Digital Twins, Flight Control Systems, PHM, Robotics He contributes to teaching courses like System Health Management and Servosystems at the PhD and Master’s levels, emphasizing aerospace and automotive applications. Recent publications highlight advancements in real-time electromechanical actuator modeling, fault detection in ball screws, and PHM systems for aircraft brakes. Scientific roles include guest editorships for journals like Aerospace and Actuators . He leads commercial research contracts with industry leaders such as Collins Aerospace and collaborates on national projects including PRIN and hybrid-electric aircraft systems development.

Jörn OstermannView profile

Professor

• Video Coding
• Computer Vision
• Machine Learning
• +9 more

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Jianwen MENGView profile

Assistant Professor

• Fault diagnosis
• Fault tolerant control
• Condition monitoring
• +5 more

Jianwen MENG serves as an Assistant Professor at ESTACA, where he has been an Enseignant-chercheur (Teacher-Researcher) since 2020. He is affiliated with ESTACA'Lab, the institution's research laboratory focused on embedded energy systems and transportation technologies. His academic foundation includes a PhD in Electrical Engineering from Université Paris-Saclay (2020), a Master's degree in Electronic Systems and Electrical Engineering from the University of Nantes (2017), and a Bachelor's degree in Electrical Engineering and Automation from Jimei University, China (2015). Dr. MENG specializes in fault diagnosis, fault tolerant control, and energy management systems for electric vehicles and embedded applications. His research integrates advanced control theory with machine learning to address critical challenges in battery and fuel cell technologies, particularly focusing on state estimation, degradation prediction, and real-time monitoring under operational stress. Analysis of his recent publications (2024-2025) reveals a strong interdisciplinary trajectory blending electrical engineering with artificial intelligence. Key trends include AI-enhanced battery state estimation under fast-charging conditions, reinforcement learning for energy management in hybrid vehicles, and novel fault diagnosis frameworks for electrochemical systems. His work consistently targets practical implementation in automotive applications while advancing theoretical control methodologies. He received the Best Paper Award at the IEEE Prognostics and System Health Management Conference (PHM-Paris) in 2019 for his contributions to lithium-ion battery monitoring. At ESTACA, Dr. MENG teaches multivariable systems, real-time control, rapid prototyping, and advanced simulation tools across multiple engineering program levels. His pedagogical approach emphasizes hands-on implementation of theoretical concepts in embedded systems. As a core member of ESTACA'Lab, he contributes to cutting-edge research in automotive electrification, particularly through projects involving battery management systems, fuel cell degradation modeling, and fault-tolerant control architectures for next-generation electric vehicles.

Afshin RahimiView profile

Professor

• Aerospace Engineering
• Control Systems
• Machine Learning
• +3 more

Dr. Afshin Rahimi is a Professor of Engineering at the University of Windsor's Faculty of Engineering, specializing in aerospace systems, machine learning applications, and fault diagnosis. His research focuses on enhancing reliability in satellite systems, renewable energy infrastructure, and smart manufacturing through advanced control systems and AI-driven solutions. He has won prestigious awards including gold medals at the Seoul International Invention Fair (2022) and the International Competition for Inventors (2021) for his work on helicopter engine diagnostics and satellite fault detection. Dr. Rahimi's research interests include: Machine learning for fault detection/prognosis in aerospace and energy systems Control systems design for satellites, UAVs, and offshore wind turbines Optimization of energy efficiency in cloud data centers and renewable systems Smart manufacturing applications using computer vision and object detection His recent work emphasizes applying AI to solve challenges in: Real-time satellite fault diagnosis using hybrid frameworks PV-integrated robotic systems for sustainable energy management Thermal regulation in smart buildings via LSTM networks Data scarcity mitigation in space systems using GANs Dr. Rahimi collaborates with industry partners and student teams like the WinSAT satellite design group. His research bridges theoretical advancements with practical implementation in critical infrastructure sectors.

• IoT
• Decision Support Systems
• Industry 4.0
• +4 more

Sylvain Kubler is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg's SerVal group. He holds a PhD from Université de Lorraine (2013) and previously served as an Associate Professor at the Research Center for Automatic Control of Nancy. His work focuses on IoT applications, decision support systems, and their integration into Industry 4.0, renewable energy, and smart cities. He has contributed to projects involving blockchain consensus mechanisms, maintenance scheduling, and energy system optimization. His research emphasizes sustainable technologies and distributed systems security. Research Interests: IoT and Blockchain Integration Decision Support Systems for Smart Cities and Industry Renewable Energy and Multi-Energy Networks AI-Driven Maintenance and Prognostics Privacy-Preserving Distributed Systems Recent Contributions: Sylvain’s recent work explores adaptive blockchain frameworks (e.g., DRAFTEE), reinforcement learning for maintenance scheduling, and explainable AI (XAI) in prognostics. His articles analyze trade-offs in federated learning architectures and multi-level energy system coordination. Affiliations and Labs: A core member of the SerVal group, he collaborates on initiatives like the IoT-based BIoTope ecosystem and the Sabine blockchain project. His work aligns with Luxembourg’s focus on secure, reliable, and sustainable technological solutions.

Mohammed Ali KhanView profile

Assistant Professor

• Renewable Energy
• Power Electronics
• Machine Learning
• +5 more

Mohammed Ali Khan is an Assistant Professor at the Centre for Industrial Electronics, University of Southern Denmark, specializing in Power Electronics and Renewable Energy systems. His research aligns with the European Green Deal’s climate neutrality goals, focusing on AI-driven grid optimization, fault diagnostics, and EV charging infrastructure resilience. Ph.D. in Power Electronics, Jamia Millia Islamia (2021) M.Tech in Power Systems, Amity University (2016) B.Tech in Electrical and Electronics Engineering, Karunya Institute (2013) His research interests span Renewable Energy Transition, Power Electronics Converters, AI/ML for Grid Stability, and Fault Detection in Photovoltaic Systems. Key scientific contributions include 6 major awards, such as the World’s Top 2% Scientists in Energy (2024) and Otto Mønsteds Fond conference grants. His recent publications emphasize AI sustainability, digital twins for grid harmonics, and EV charging reliability. He has supervised projects on electrolyzer converters, microgrids, and hydrogen integration, and delivered guest lectures on AI applications in Power Electronics. World's Top 2% Scientists in Energy (2024, 2023) Otto Mønsteds Fond - Conference Participation Abroad (2024, 2023) Excellent Oral Presentation Winner (2022) As a supervisor , he has guided students in EV converter design, drone-based mapping, and electronics projects. His projects include PowerGridOptimiser (2024–2027) and ComElCo (2022–2025). Collaborations extend to institutions like Jamia Millia Islamia and University of Technology, Brno.

Marielle StoelingaView profile

Professor

• Predictive Maintenance
• Formal Methods
• Fault Tree Analysis
• +7 more

Prof. Marielle Stoelinga is a Professor at the University of Twente, Netherlands, working within the Electrical Engineering, Mathematics and Computer Science faculty in the Formal Methods and Tools department. She leads significant research initiatives in formal methods with applications to safety, security, and reliability engineering. Her research spans predictive maintenance , fault tree analysis , attack tree modeling , and the integration of safety and security through formal methods. She focuses on applying big data analytics to predict system failures, with particular emphasis on critical infrastructure including railway systems, satellite missions, and nuclear reactors. Her work bridges theoretical formal methods with practical applications in asset management. Analysis of her recent publications reveals a strong trend toward integrating safety and security analysis through attack-fault-defense trees, developing formal frameworks for risk assessment, and applying model checking techniques to real-world maintenance problems. Her research increasingly addresses the human and organizational aspects of predictive maintenance systems while maintaining rigorous formal foundations. 5 million euros research grant from Dutch National Organization for Scientific Research (NWO) for PrimaVera project Prof. Stoelinga leads the PrimaVera research project ( Predictive maintenance for Very effective asset management ), which takes a holistic approach to predictive maintenance spanning sensor systems, data science, maintenance optimization, and human factors. Her research group actively contributes to formal methods applications in critical infrastructure sectors including energy, transportation, and aerospace.