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Junyu QiView profile

Researcher

• Condition Monitoring
• Predictive Maintenance
• Rotating Machinery Diagnostics
• +6 more

Dr.-Ing. Junyu Qi serves as a Scientific Researcher at the Reutlingen Research Institute within the Faculty of Technology at Reutlingen University, Germany. His primary affiliation is with the Drive Technology research group under the Electronics & Drives division, where he conducts advanced research in condition monitoring and predictive maintenance for industrial machinery. Education: B.Sc. in Mechatronics from Shanghai Maritime University (2008-2012), M.Sc. in Mechatronics from Friedrich-Alexander-Universität Erlangen-Nürnberg (2013-2016) Professional Experience: Researcher at KU Leuven's Department of Mechanical Engineering (2017-2023), Bosch Rexroth engineer (2015-2016) Qi's research centers on AI-driven predictive maintenance systems, specializing in vibration analysis and cyclostationary signal processing for rotating machinery. His work bridges statistical modeling with deep learning to develop robust diagnostic frameworks for wind turbine drivetrains, roller chain systems, and planetary gearboxes. Key innovations include sensorless monitoring techniques using motor current data and multi-sensor fusion architectures for remaining useful life prediction. His publication portfolio shows a clear trajectory toward integrating physics-informed AI with industrial applications, with recent work emphasizing few-shot learning for data-scarce scenarios and federated learning for distributed industrial systems. Over 70% of his publications focus on wind turbine diagnostics and bearing prognostics. Best Paper Award, American Society of Mechanical Engineers (ASME) 2020 for wind turbine gearbox research Regular reviewer for Mechanical Systems and Signal Processing, Reliability Engineering and System Safety, IEEE/ASME Transactions on Mechatronics Qi actively contributes to the academic community through peer review and conference participation, with no formal teaching responsibilities documented. His current research leverages the Reutlingen University's specialized laboratories for drive technology and vibration analysis to develop deployable industrial solutions.

• Subsea Engineering
• Controls Engineering
• Systems Engineering
• +4 more

Rafik Borji serves as Lecturer of Subsea Controls and Systems Engineering within the Department of Mechanical and Aerospace Engineering at the University of Houston's College of Engineering. He functions as the Subsea Engineering Academic Advisor, with his office located in Engineering Building 2, Room W228 on the Houston campus. His academic contact email is subsea-advisor@me.uh.edu. His research spans Subsea Engineering and Biomedical Engineering, focusing on model-based approaches for transient two-phase flow in pipelines and tissue characterization using optical coherence tomography/ultrasound techniques. Key areas include cardiovascular diagnostics (arterial plaque analysis, VAD prognostics) and subsea flow assurance, demonstrating interdisciplinary integration of mechanical systems modeling with biomedical applications. Publications from 2009-2016 reveal dual research trajectories: subsea systems work emphasizes low-dimensional modeling for pipeline flow dynamics, while biomedical research develops quantitative methods for tissue property assessment and cardiovascular device monitoring. Both domains utilize advanced modeling techniques for system diagnostics and prognostics. No scientific awards or honors are documented in the available information. As Subsea Engineering Academic Advisor, Borji provides program guidance though specific student advisees and research grants are not detailed in the source material. Documentation does not reference laboratories, research teams, or specialized facilities associated with his work.

John PearsonView profile

Visiting Professor

• Systems Engineering
• Aerospace Engineering
• Control Systems
• +6 more

John Pearson is a Visiting Professor at Loughborough University and an Honorary Associate Professor at Nottingham University. He holds a PhD from Loughborough University in the field of Active Control of Helicopter Structural Response. His career spans over 25 years in the aerospace and defense industry, including roles at Rolls-Royce, BAE Systems, and Westland Helicopters. He has been a Technology Lead for Innovation at Rolls-Royce and led the Prognostics and Health Management Laboratory at BAE Systems' Systems Engineering Innovation Centre (SEIC). Education: PhD in Active Control of Helicopter Structural Response (Loughborough University), BSc (details not explicitly stated). Research Interests: Focuses on advanced control systems, IVHM, autonomous systems, thermal management, and UAV technologies. Collaborated on EPSRC-funded projects involving bio-mimetic actuation, fault tolerance, and avionics thermal management. His work includes pioneering experimental implementations of active stability systems and integrated system architectures using Kalman filters. Grants & Collaborations: Involved in EPSRC projects and the ASTRAEA programme (UK-industry/Government funded UAV initiative). Led IVHM campaigns across BAE platforms, including Typhoon jets and UAV projects. Labs & Teams: Founded the Prognostics and Health Management Laboratory at BAE Systems. Active in academic-industry partnerships through the SEIC at Loughborough University. Additional Roles: Served on the National Technical Committee for Health Management and the Boeing IVHM Centre of Excellence board. Reviewer for international journals and conference organiser in systems engineering.

Sara KohtzView profile

Assistant Professor

• Data Science
• Physics-Informed Machine Learning
• Reliability Engineering
• +3 more

Sara Kohtz is an Assistant Professor in the School of Systems Science and Industrial Engineering at Binghamton University. She holds a BS (2016) and MS (2017) in Industrial Engineering from Binghamton University and a PhD in Industrial Engineering from the University of Illinois at Urbana-Champaign (2024). Her research focuses on machine learning theory and applications in high-impact engineered systems, particularly physics-informed machine learning for energy systems, reliability engineering, and fault diagnosis in electrified systems. Education Background: PhD: Industrial Engineering – University of Illinois at Urbana-Champaign MS: Industrial Engineering – Binghamton University BS: Industrial Engineering – Binghamton University Her work bridges data science and engineering, addressing challenges in battery management, sensor placement, and optimal system design. Recent publications emphasize machine learning methodologies for prognostics, energy systems optimization, and physics-driven models. Professional memberships include ASME, IISE, and the Society of Women Engineers (SWE). No awards or grants are explicitly listed in the provided information.

Francesco AmigoniView profile

Researcher

• Robotics
• Multi-Agent Systems
• Machine Learning
• +3 more

Francesco Amigoni is affiliated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. His research focuses on robotics, multi-agent systems, and machine learning, with applications in autonomous systems, anomaly detection, and energy systems. He has contributed to advancements in multi-agent coordination, path planning, and predictive maintenance through interdisciplinary approaches. Key research areas include: Multi-Agent Systems: Task allocation, cooperative robotics, and deadlock prevention in dynamic environments Machine Learning: Anomaly detection, transfer learning, and neural networks for battery lifecycle analysis Autonomous Robotics: Exploration strategies, SLAM algorithms, and map completeness estimation Data Mining: NLP-driven classification of road accidents and healthcare system analysis His work bridges theoretical foundations with real-world applications, addressing challenges in energy systems, environmental monitoring, and healthcare management. Recent studies highlight innovations in lithium-ion battery prognostics and swarm robotics fault detection.

• Reliability Engineering
• Optimization
• Machine Learning
• +3 more

Jianchao Zeng is affiliated with North University of China's Department of Computer Science and Control Engineering, and also associated with Taiyuan University of Science and Technology's Complex System and Computational Intelligence Laboratory. His research focuses on reliability engineering, optimization, machine learning, and image processing. He has collaborated extensively on projects involving maintenance modeling, evolutionary algorithms, and medical imaging analysis. Affiliations: North University of China, Taiyuan University of Science and Technology Key research interests include reliability engineering (predictive maintenance, degradation modeling), optimization techniques (surrogate-assisted algorithms, swarm intelligence), and medical image processing (radiomics, segmentation). His recent work emphasizes interdisciplinary applications in reliability systems, remote sensing, and healthcare. Publications highlight contributions to maintenance optimization frameworks, deep learning models for image analysis, and hybrid algorithms combining evolutionary computation with machine learning. Notably, his work on remaining useful life prediction and radiomics-enhanced segmentation addresses critical challenges in industrial and medical domains.

Seung-Kyum ChoiView profile

Associate Professor

• Additive Manufacturing
• Uncertainty Quantification
• Robust Design Optimization
• +4 more

Dr. Seung-Kyum Choi is an Associate Professor at the School of Mechanical Engineering, Georgia Institute of Technology. He joined Georgia Tech in 2006 and currently directs the Center for Additive Manufacturing Systems (CAMS), focusing on advancing additive manufacturing technologies and educational programs. His research emphasizes robust design optimization, uncertainty quantification, and probabilistic mechanics applied to engineered systems, including additive manufacturing, metamaterials, and aerospace components. Education: Ph.D., Mechanical Engineering, Wright State University (2006) M.S., Mechanical Engineering, Ajou University, South Korea (2001) B.S., Mechanical Engineering, Ajou University, South Korea (1996) Research interests span additive manufacturing, structural reliability, and multidisciplinary design optimization. He develops simulation tools for uncertainty management in complex systems, such as 3D-printed lattice structures and aerospace components. His work integrates machine learning, meta-learning, and surrogate modeling to enhance decision-making in engineering design. Notable awards include the 2009 Lockheed Martin Aeronautics Dean’s Award for Teaching Excellence. He contributes to journals like International Journal of Materials and Product Technology and Structure & Infrastructure Engineering , and actively mentors students through CAMS and his research labs.

Aldo A. FerriView profile

Professor

• Acoustics
• Structural Dynamics
• Nonlinear Dynamics
• +5 more

Aldo A. Ferri is a Professor and Director of Assessment and Student Success in the School of Mechanical Engineering at Georgia Tech. He holds a Ph.D. (1985), M.A. (1983) from Princeton University, and B.S. (1981) from Lehigh University. His research focuses on acoustics, structural dynamics, nonlinear dynamics, and control systems, with emphasis on vibration damping, friction-based systems, and fluid-structure interaction. He pioneered semi-active friction devices for shock isolation and dither-based friction oscillation cancellation. Ferri has received numerous teaching awards, including the Class of 1940 W. Howard Ector Outstanding Teacher Award (2016) and ASME Fellow status (2008). He has also contributed to educational innovations like blended learning and mobile hands-on experiments. His work is funded by NSF, NASA, and industry partners like Honeywell and General Motors. Ferri teaches courses in dynamics, control systems, and numerical methods, and has advised on projects ranging from wheelchair inertial properties to carbon nanotube mechanics. His lab develops experimental platforms for educational purposes and advanced engineering applications. Education: Ph.D. in Mechanical Engineering, Princeton University, 1985 M.A. in Mechanical Engineering, Princeton University, 1983 B.S. in Mechanical Engineering, Lehigh University, 1981 Research Interests: Structural vibrations and damping mechanisms Semi-active control systems and friction dynamics Fluid-structure interaction modeling Diagnostic/prognostic methods for mechanical systems Acoustic engineering and noise control Educational technology and hands-on learning modules Key Contributions: Developed vibration isolation mounts for submarine platforms Advanced dither-based cancellation of brake squeal Created experimental platforms using guitar strings for teaching signals/systems Improved planetary gear fault diagnosis techniques Extended Ritz formulations for acousto-elasticity problems Awards & Recognition: 2016 ASEE Best Paper Award Woodruff School Faculty Fellow (1993-1998) SAE Ralph R. Teetor Educational Award (1991) Grants & Partnerships: Honeywell, Inc. National Science Foundation NASA Air Force Office of Scientific Research Office of Naval Research General Motors Corporation Labs & Teams: Leads research groups focused on mechanical systems control, experimental education tools, and nanotechnology applications. Collaborates with interdisciplinary teams in biomedical engineering (wheelchair efficiency studies) and materials science (carbon nanotube mechanics).

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Power Electronics
• Electrical Engineering
• Energy Systems
• +3 more

Heath Hofmann is a Professor in the Department of Electrical and Computer Engineering. His research focuses on advanced energy systems, including electric machines, power electronics, and hybrid energy storage solutions. He emphasizes robust control strategies, battery management systems, and model predictive control applications. Hofmann's work often addresses challenges in electric vehicle powertrains, shipboard power systems, and renewable energy integration. Research interests include: Electric machine design and control Battery degradation and state-of-health estimation Hybrid energy storage systems Thermal management of power electronics Model predictive control for multi-energy systems His advising style prioritizes weekly one-on-one meetings and lab collaboration, with a focus on student-driven research progression. Hofmann encourages internships and conference participation (e.g., IEEE ECCE, APEC) to advance technical expertise. Key research contributions span topics like nonlinear magnetics modeling, fault detection in battery systems, and adaptive control strategies for high-power applications. Labs/Teams: Active in electric propulsion system design, energy storage optimization, and control algorithm development for industrial and maritime applications.

• Automatic Control
• Fault Detection
• Water Distribution Networks
• +3 more

Joaquim Blesa Izquierdo is a Professor at the Department of Automatic Control, Universitat Politècnica de Catalunya. His research focuses on automatic control systems with emphasis on fault detection, water distribution networks, and data-driven methodologies. He leads the MASHED project on energy system management and has advised two PhD students in fault diagnosis and leak localization. His work integrates artificial intelligence techniques like LSTM neural networks with traditional control engineering approaches. Research interests include: Fault diagnosis/prognosis in complex systems Smart infrastructure monitoring (water networks, energy grids) Data-driven approaches for system analysis Hybrid energy storage systems Publications demonstrate strong focus on: Leak localization techniques for water distribution networks Fault detection mechanisms using LPV observers and ANFIS networks Multi-layer optimization for smart grid control Current supervision includes Xin Fang (fault prognosis) and Leonardo Gómez Coronel (deep learning for leak localization). The MASHED project (2022- ) develops digitalization strategies for hybrid energy storage systems.

Christopher MechefskeView profile

Professor

• Vibration and Noise Control
• Acoustics and Noise Reduction
• Machine Condition Monitoring
• +3 more

Christopher Mechefske is a Professor in the Department of Mechanical and Materials Engineering at Queen's University. He is affiliated with Smith Engineering and holds editorial roles in journals like the International Journal of COMADEM and Journal of Prognostics and Health Management. His research focuses on vibration and noise reduction, acoustic modeling in MRI scanners and aircraft, and AI-driven machine fault diagnosis. Education: Ph.D. in Mechanical Engineering, Monash University (1992) B.Sc. in Engineering, University of Guelph (1989) Research Interests: Dr. Mechefske’s work spans dynamics, vibration control, acoustics, and predictive maintenance. He develops noise reduction strategies for medical and aerospace systems and designs real-time monitoring solutions for industrial machinery using artificial intelligence. Affiliations: Member, American Society of Mechanical Engineers Member, Canadian Machinery Vibration Association Editorial Board Member, Journal of Hydromechatronics Teaching: Courses include Solid Mechanics I (MECH 221), Noise Control (MECH 482), and Experimental Vibration Analysis (MECH 826). Current Supervision: Mentors Postdoctoral Fellows Zacharie Laly (since 2022) and Mohsen Marani Barzani (since 2021).

Paraskevi ZachariaView profile

Assistant Professor

• Production Planning and Control
• Robotics
• Automation Systems
• +3 more

Paraskevi Zacharia is an Assistant Professor at the Department of Industrial Design and Production Engineering, University of West Attica. She holds a Dipl.-Ing. in Mechanical Engineering & Aeronautics from the University of Patras (2002), followed by two Ph.D.s: one in Mechanical Engineering & Aeronautics (2009) and another in Business Administration (2016). Her research focuses on Production Planning and Control, integrating Robotics, Automation Systems, and Artificial Intelligence to optimize multi-criteria manufacturing processes involving robots and human workers. Education: Dipl.-Ing. in Mechanical Engineering & Aeronautics, University of Patras, 2002 Ph.D., Mechanical Engineering & Aeronautics, University of Patras, 2009 Ph.D., Business Administration, University of Patras, 2016 Research Interests: Development of intelligent control strategies for industrial robots Robotic task scheduling and motion planning in cluttered environments Optimization of production planning using heuristic algorithms Autonomous vehicle systems for industrial and city logistics Publications & Trends: Her work spans fuzzy logic applications, AI-driven predictive models, and multi-robot coordination. Recent studies emphasize optimizing path planning, energy efficiency, and human-robot collaboration in manufacturing. Awards: No scientific awards explicitly mentioned in the text. Grants & Projects: Involved in European and National R&D projects, with contributions to robotics, logistics, and smart infrastructure. Serves as a reviewer for 14 journals including International Journal of Robotics and Automation. Labs & Teams: Engaged in research laboratories focusing on robotics, automation, and intelligent systems, though specific lab names are not detailed in the text.

• Predictive Maintenance
• System Reliability
• Stochastic Degradation Modeling
• +1 more

Dr. Antoine Grall is Full Professor and Director of Doctoral School 361 'Science for Engineers' at Université de Technologie de Troyes. He leads the 'Dependability: Reliability and Maintenance' research axis at LIST3N laboratory. His research focuses on predictive maintenance modeling, stochastic degradation analysis, and reliability optimization for industrial systems. Research develops mathematical frameworks for condition-based maintenance, with applications in wind turbines, manufacturing, and control systems. Recent work integrates machine learning with traditional reliability models. Publications emphasize adaptive maintenance policies, degradation modeling, and prognostics for complex engineering systems. Collaborative projects span automotive, aerospace, and energy sectors. Teaches graduate courses in maintenance modeling, reliability theory, and optimization. As doctoral school director, oversees 150+ PhD candidates across engineering disciplines.

• Reliability Engineering
• Safety Engineering
• Maintenance Optimization
• +8 more

Yiliu Liu is a Professor in the Department of Mechanical and Industrial Engineering at NTNU. His primary affiliation is with the RAMS Group (Reliability, Availability, Maintainability and Safety) within the Marine Technology and Infrastructure (MTP) program. He leads the 2-year International Master’s Program in RAMS, which is well-regarded by industries and students. Research interests include system analysis for reliability/security/resilience, product safety engineering, barrier theory, prognostics, and maintenance optimization. Key application areas are energy industries (oil/gas/renewables), healthcare systems, and public sectors like transport. He actively collaborates internationally with projects in SE Asia, Europe, Brazil, and Africa. He is Vice Chair of the Norway-Sweden Joint Chapter at IEEE Reliability Society and Editor-in-Chief of the International Journal of Safety and Reliability. Notable awards include the FIFA Fun Award 2022. Current projects include SFI SUBRPO (subsea robotics), FME HYDROGENI (hydrogen technology), and the S3CARE healthcare initiative. Over 20 articles since 2021 focus on subsea systems, hydrogen safety, maintenance strategies, and risk modeling. Leads 8-country project on safe/sustainable oceans Co-leads hydrogen station safety project with Poland/Spain/Japan Chairs the European Reliability and Safety Association’s medical industry committee Grants and collaborations span academia/industry partnerships across 20+ countries. Active in developing resilience frameworks for energy and healthcare sectors.