جست‌وجوی استاد و پژوهشگر

Seyed Shariatipourمشاهده پروفایل

استادیار

• CO2 Geological Storage
• Reservoir Simulation
• Fluid Flow in Porous Media
• +۳ مورد دیگر

Seyed Shariatipour is an Assistant Professor at Coventry University's CEES School of The Environment, leading the Reservoir Engineering group at the Flow Measurement and Fluid Mechanics Research Centre. He holds a PhD in CO2 Behavior Modelling from Heriot-Watt University (2014) and an MSc in Petroleum Engineering (2010). His research focuses on CO2 geological storage, reservoir simulation, and fluid dynamics in subsurface systems. He has pioneered approaches to enhance CO2 storage security in saline aquifers and depleted reservoirs, emphasizing caprock integrity and injection optimization. Key roles include Co-Chair of the Reservoir Development and Revitalization committee at the American Association of Petroleum Geologists and Associate Membership at Calgary University's Containment & Monitoring Institute. He has supervised over 8 PhD/MSc students and delivered training programs in CCS through UKSA-funded projects. Research interests span CO2-EOR, caprock-aquifer interface dynamics, and wellbore integrity in CO2 environments. Notable contributions include studies on aquifer morphology impacts, convective instability in porous media, and cement degradation mechanisms. He has authored over 66 publications and secured research funding from the Royal Academy of Engineering and UKSA. Awards include the 2016 Low Carbon Hero Award (Istanbul Carbon Summit) and SPE Aberdeen cash prizes (2012-2013). His work integrates numerical simulation with field-scale validation, addressing challenges in CO2 storage site selection, plume migration prediction, and long-term security. Collaborations span institutions in the UK, South Africa, and Canada, emphasizing practical solutions for carbon management.

Stefania Cecchiمشاهده پروفایل

دانشیار

• Audio Signal Processing
• Electronics Engineering
• Nonlinear Systems
• +۳ مورد دیگر

Prof. Stefania Cecchi is an Associate Professor at the Department of Information Engineering (DII) within the Faculty of Engineering at the University of Marche (UNIVPM). Her research focuses on audio signal processing, nonlinear systems, and biomedical engineering applications. She holds office hours on Mondays from 9:30–12:30 in DII or via Microsoft Teams. Her work spans innovative audio technologies such as binaural systems, real-time noise cancellation, and spatial audio rendering. She also explores interdisciplinary applications like bee hive monitoring through acoustic analysis and physiological response measurement to sound stimuli. Recent projects include developing low-cost medical devices for stress detection and optimizing automotive audio systems. Key research areas include: Head-Related Transfer Function (HRTF) interpolation for 3D audio Real-time audio processing for driver monitoring and health applications Nonlinear system identification in audio devices Acoustic-based fault detection in drones and vehicles Her publications highlight trends in immersive audio systems, bioacoustic analysis, and adaptive filtering techniques. She actively contributes to advancing audio engineering and its biomedical applications through interdisciplinary collaborations.

Faris Elashaمشاهده پروفایل

مدرس ارشد

• Rotating Machines Diagnosis and Prognosis
• Condition Monitoring
• Asset Integrity and Management
• +۳ مورد دیگر

Faris Elasha is a Senior Lecturer in Dynamics at Coventry University's CEES School of Engineering since September 2015. Previously, he served as a Research Fellow at Cranfield University and held roles in the Power Generation Industry, leading maintenance teams for power stations. He holds a First Class BSc in Mechanical Engineering from Sudan University of Science and Technology, an MSc (Distinction) in Mechanical Engineering Design from The University of Manchester, and a PhD in Mechanical Engineering from Cranfield University. His research focuses on condition monitoring, diagnostics, and prognosis of rotating machinery, particularly in gearbox dynamics, asset integrity management, and vibration-based analysis. He has contributed to developing the first condition monitoring system for tidal turbines. His work aligns with UN Sustainable Development Goals, emphasizing reliability improvement and cost reduction in mechanical systems. Key research trends in his publications include advanced signal processing techniques (e.g., wavelet transforms), machine learning applications for prognostics, and vibration analysis for fault detection in gears and bearings. His articles span aerospace (helicopter gearboxes), renewable energy (wind and tidal turbines), and general mechanical systems. Elasha has supervised one student and engaged in academic activities such as PhD examinations and collaborative research with institutions like Cranfield University. His expertise bridges industrial practice and academic research, addressing challenges in rotating equipment health management.

Paul Allenمشاهده پروفایل

استاد

• Railway Engineering
• Vehicle Dynamics
• Condition Monitoring
• +۳ مورد دیگر

Academic Profile Paul Allen is Professor and Director of the Institute of Railway Research at the University of Huddersfield's School of Computing and Engineering. He serves as Associate Dean (RIKE) and leads research in railway dynamics and safety. With a PhD in contact mechanics from Manchester Metropolitan University, he has expanded the Institute of Railway Research from 9 to 30+ staff with £3m annual expenditure. Research Focus His work centers on railway systems optimization, including wheel-rail contact mechanics, braking systems, condition monitoring, and vehicle dynamics. He developed the £2.5m HAROLD test facility and secured over £10m in research funding, including the £22m Centre for Innovation in Rail project that created 50+ industry jobs. Publications Recent articles (2022-2025) focus on high-speed train dynamics, fault detection algorithms, composite materials in bogie frames, and machine learning applications in railway maintenance. Research consistently addresses wear analysis, structural integrity, and predictive maintenance methodologies. Honors & Leadership Fellow of the Institute of Mechanical Engineers Fellow of the Chartered Management Institute Advisor to HS2, Network Rail, and Department for Transport BBC commentator and industry press contributor Academic Activities Leads PhD supervision in railway engineering and serves on editorial boards for international conferences. His research group collaborates globally with rail industry partners including Alstom and Unipart Rail.

• Condition Monitoring
• Predictive Maintenance
• Robotics and Control
• +۳ مورد دیگر

Dr. Xihui (Larry) Liang is an Associate Professor in the Department of Mechanical Engineering at the University of Manitoba, Canada. He holds a PhD in Mechanical Engineering from the University of Alberta (2016) and completed a postdoctoral fellowship there. His research focuses on condition monitoring, predictive maintenance, robotics, and reliability engineering, with applications in machinery and infrastructure systems. He is a recipient of the Terry G. Falconer Memorial Award for his work in developing intelligent monitoring systems. Education: PhD in Mechanical Engineering, University of Alberta, 2016 Research Interests: Dr. Liang's work spans advanced condition monitoring techniques for mechanical systems, robotics and control for manufacturing, and reliability modeling. Key areas include: Embedded systems and smart sensors for structural health monitoring Machine learning for fault diagnosis and RUL prediction Maintenance optimization strategies for wind turbines and gas engines Smart bearing design with self-sensing capabilities Recent Work Trends: His publications emphasize innovative sensor technologies (e.g., FBG and PVDF transducers) and machine learning approaches for predictive analytics. Recent studies address challenges in non-stationary signal processing and cross-domain fault detection. Awards: Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award (2024) Advising & Labs: Dr. Liang mentors graduate students in embedded systems, robotics, and reliability engineering. His lab develops advanced monitoring tools for industrial applications, including smart bearings and additive manufacturing quality control.

Udaya Annakkageمشاهده پروفایل

استاد

• Power System Control and Stability
• Electrical Energy Systems
• Power Systems
• +۵ مورد دیگر

Dr. Udaya Annakkage is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba. He holds a PhD from the University of Manchester (UK) and has over 30 years of experience in teaching, research, and consulting. His research focuses on power system control and stability, electrical energy systems, and renewable energy integration. Dr. Annakkage has served as Head of the Department of Electrical and Computer Engineering (2008–2012 and 2020–2021) and was an editor of IEEE Transactions on Power Systems (2009–2012). He has supervised over 20 PhD students and has contributed extensively to IEEE and CIGRE working groups. Education: PhD, University of Manchester, UK (1987) MSc, University of Manchester, UK (1984) BSc (Eng.), University of Moratuwa, Sri Lanka (1982) Research Interests: Dr. Annakkage specializes in power system dynamics, stability analysis, renewable energy integration, grid-forming inverters, and advanced control strategies for power systems. His work emphasizes enhancing grid resilience, improving transient stability, and addressing challenges in inverter-dominated networks. Publications: His recent work explores topics such as grid-forming converter control, microgrid stability, and oscillation analysis in power systems. These contributions highlight advancements in renewable energy integration and dynamic system modeling. Grants & Advising: While currently not offering graduate student opportunities, Dr. Annakkage has a distinguished record of mentoring PhD and MSc students. His research has been supported by grants focusing on power system innovation and sustainability. Labs & Teams: Collaboration with IEEE and CIGRE underscores his involvement in industry-relevant research. Key projects include co-simulation platforms for real-time security assessment and stability analysis tools for large-scale power systems.

• Bayesian methods
• Biostatistics
• Data science
• +۵ مورد دیگر

Saman Muthukumarana is a Professor and Department Head in the Department of Statistics at the University of Manitoba's Faculty of Science. His research focuses on Bayesian methods, biostatistics, data science, environmental statistics, multivariate analysis, and statistical machine learning. He actively publishes in top-tier journals and conferences, addressing challenges in imbalanced data classification, anomaly detection, and computational statistics. Recent work includes advancing SMOTE techniques for high-dimensional data, Bayesian network analysis for environmental monitoring, and applications of machine learning to healthcare and sports analytics. He is a sought-after collaborator, contributing to interdisciplinary projects in ecology, engineering, and public health. His academic profile highlights over 60 peer-reviewed publications since 2008, reflecting a strong commitment to methodological innovation and real-world impact. Muthukumarana is also involved in supervising graduate students and mentoring early-career researchers. His departmental leadership positions him at the forefront of statistical education and research strategy within the Faculty of Science.

Janis Terpennyمشاهده پروفایل

استاد

• Smart Manufacturing Systems
• Design Education
• Digital Twin Technologies
• +۳ مورد دیگر

Janis Terpenny is a Professor at George Mason University with joint appointments in the Department of Systems Engineering and Operations Research and the Department of Mechanical Engineering. She brings extensive leadership experience from previous roles including Dean of Engineering at the University of Tennessee, Department Head at Penn State and Iowa State, and Director of the NSF Center for e-Design. Her research focuses on smart integrated systems for manufacturing and design education innovations. Education: PhD and MS in Industrial and Systems Engineering from Virginia Tech, BS in Applied Mathematics from Virginia Commonwealth University Her research spans smart manufacturing systems, design engineering, digital twins, and engineering pedagogy. Recent work emphasizes predictive maintenance algorithms and democratizing additive manufacturing through cloud platforms. She has received recognition as Fellow of ASME and IISE. Key scientific contributions include developing data fusion frameworks for manufacturing quality control, creating cloud-enabled predictive maintenance systems, and advancing ontology-based design tools for product lifecycle management. Her editorial roles include Area Editor for Engineering Economist and board membership at Computers in Industry . NSF Center for e-Design Director Digital Manufacturing Commons™ Initiative Leader Industry experience with General Electric Professional affiliations include AAAS, Alpha Pi Mu, ASEE, SME, and Tau Beta Pi. Current research explores AI-driven manufacturing diagnostics, inclusive engineering education models, and cloud-based design optimization.

Dimitrios Papageorgiouمشاهده پروفایل

دانشیار

• Nonlinear Control Systems
• Sliding-Mode Control
• Marine Technology
• +۲ مورد دیگر

Dimitrios Papageorgiou is an Associate Professor in the Department of Electrical and Photonics Engineering at DTU, specializing in nonlinear and fault-tolerant control systems. His research develops advanced control methodologies for applications including marine vessel autonomy, wind turbine reliability, and industrial automation. Research highlights: Nonlinear sliding-mode control algorithms for robust system performance Fault diagnosis and prognosis for critical energy infrastructure Maneuvering control and situation awareness for autonomous vessels Biomimetic control approaches for robotic systems He leads PhD projects in aerial robotics inspection, wind turbine fault tolerance, and compliant human-robot interaction systems. His group collaborates with maritime and renewable energy industries to implement control solutions in operational environments.

• Power Electronics
• Electric Drives
• Motor Control
• +۳ مورد دیگر

Fabio Immovilli is an Associate Professor at the University of Modena and Reggio Emilia , affiliated with the Department of Engineering Sciences and Methods . His research focuses on power electronics, motor drives, and condition monitoring, with expertise in current source inverters, fault detection, and energy conversion systems. He teaches advanced courses on electric drives, power converters, and static energy conversion. Research Interests: Immovilli’s work spans power electronics architectures for industrial drives, fault diagnosis using machine learning, and innovative solutions for high-efficiency motor systems. His recent studies include partial discharge monitoring in industrial machinery, resonance damping in current source inverters, and scalable data acquisition systems for condition monitoring. Teaching: He instructs graduate-level courses such as Advanced Electric Drives and Power Converters Systems , Static Energy Conversion , and Inverters and Electric Machines for Industry . These courses emphasize practical applications of control systems, power electronics, and motor design. Key Contributions: Immovilli has developed novel modular data acquisition systems for partial discharge detection and scalable digital solutions for industrial condition monitoring. His work on current source inverters explores efficiency improvements and low-speed operation extensions. He also collaborates on projects involving electric propulsion systems for naval applications and fault-tolerant multi-phase drives.

• Power Electronics
• Battery Technology
• Energy Storage Systems
• +۳ مورد دیگر

Prof. Nicola Schulz is a Lecturer for Electric Power Systems at the FHNW Institute of Electric Power Systems within the FHNW School of Engineering and Environment at the University of Applied Sciences Northwestern Switzerland. Their teaching focuses on power electronics, energy storage technologies, and energy systems design for mechanical engineers. Research interests include power electronics reliability, SiC semiconductor applications, battery technology (e.g., second-life lithium-ion batteries), and magnetic component characterization. They also explore energy lab setups for environmental engineering education. Research activities emphasize multi-physical characterization of power semiconductors, online condition monitoring, and optimization from device to system levels. Battery-related work includes technical and economic analyses of repurposed batteries and battery management systems (BMS). Recent publications highlight advancements in SiC converter reliability, proton/neutron radiation effects on power devices, and smart grid optimization through solid-state transformers and multi-agent simulation frameworks like SmartStability. Prof. Schulz’s work bridges academic research and practical applications in sustainable energy systems. Their address is at Klosterzelgstrasse 2, 5210 Windisch, with direct contact via nicola.schulz@fhnw.ch. They are affiliated with the FHNW Campus Brugg-Windisch and actively engaged in industry partnerships through research collaborations.

• Electromagnetics
• Finite Element Methods
• Magneto-Quasi-Static Problems
• +۵ مورد دیگر

Ruth Vazquez Sabariego is a Professor at KU Leuven in Belgium, with her office located at Kasteelpark Arenberg 10, box 2445, 3001 Leuven. She maintains an active research program in computational electromagnetics and power electronics with numerous recent publications and ongoing PhD supervision. Her research interests span Electromagnetics , Finite Element Methods , Magneto-Quasi-Static Problems , Power Electronics , Magnetic Components , and the application of Physics-Informed Neural Networks to electromagnetic problems. She has pioneered work in homogenization techniques for magnetic components, simulation of electromagnetic phenomena, and the application of machine learning to traditional electromagnetic problems. Analysis of her recent publications reveals a strong trend toward integrating machine learning with traditional electromagnetic simulation techniques, particularly physics-informed neural networks for solving magneto-quasi-static problems. Her work also shows consistent development of homogenization methods for windings and magnetic cores, with increasing focus on thermal-electromagnetic coupling in power electronics applications. Professor Sabariego actively supervises PhD students as both promotor and co-promotor, including Thomas Jochmans (2024), Michele Vescovo (2025), Jiaze Kong (2023), Sina Toghranegar (2023), and Ziqing Guo (2022). Her research group is involved in multiple significant projects including 'Magnetic components for on-board chargers of electric vehicles', 'Hosting capacity improvement of power electronic converters', and 'AI Development for topology optimisation of magnetics and cooling systems'. Her laboratory work focuses on electromagnetic field computation, particularly for power electronics applications. The research involves developing advanced numerical methods for simulating magnetic components, with recent emphasis on integrating machine learning techniques with traditional finite element approaches to improve computational efficiency while maintaining accuracy.

• Internet of Things
• Wireless Sensor Networks
• Human-in-the-Loop Systems
• +۶ مورد دیگر

Jorge Sá Silva is a Professor at the Department of Informatics Engineering within the Faculty of Sciences and Technology at the University of Coimbra, Portugal. With a prolific publication record spanning over two decades, he has established himself as a leading researcher in Internet of Things, Cyber-Physical Systems, and Human-in-the-Loop computing. His research has significant impact across multiple domains including smart cities, healthcare applications, industrial IoT, and privacy-preserving technologies. Professor Sá Silva's research interests focus on the intersection of human-centered computing and networked systems. His work particularly emphasizes Human-in-the-Loop Cyber-Physical Systems where humans actively participate in the sensing and decision-making processes. He has pioneered approaches to unobtrusive sensing, privacy-preserving frameworks for IoT applications, and edge-based AI systems that maintain user privacy while delivering intelligent functionality. His research spans theoretical foundations to practical implementations across various application domains including healthcare, smart cities, and industrial automation. His recent publications reveal a strong trend toward integrating edge computing with privacy-preserving AI techniques, particularly federated learning approaches applied to IoT systems. There's a clear progression from foundational networking research toward human-centered applications that address real-world challenges in healthcare, construction, and education. His work consistently bridges theoretical networking concepts with practical implementations in constrained environments, with a growing emphasis on sustainability and ethical considerations in technology design. Throughout his career, Professor Sá Silva has demonstrated strong leadership in academic advising and collaborative research. His publication record shows consistent mentorship of junior researchers, with many co-authored papers featuring doctoral students and early-career researchers. He has secured significant research funding through multiple EU and national projects focused on IoT, cyber-physical systems, and human-centered networking. His collaborative network spans institutions across Europe, South America, and Asia, reflecting the international impact of his work. Professor Sá Silva leads research activities within the networking and IoT group at the University of Coimbra, which maintains strong connections with industry partners in the telecommunications and industrial automation sectors. His team has developed several prototype systems including the Green Bear LoRaWAN-based platform for sustainable cities and the CONFLUENCE integration model for privacy-preserving IoT solutions. The research group maintains state-of-the-art testbeds for IoT experimentation and has been involved in numerous EU-funded research initiatives addressing challenges in the Internet of Things and cyber-physical systems domains.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Haitao Wang is a prominent researcher affiliated with the Chinese Academy of Sciences, specifically with the Institute of Automation and State Key Laboratory of Management and Control for Complex Systems in Beijing. His research spans multiple disciplines including artificial intelligence, computer vision, engineering systems, and geospatial information processing. With extensive publications across top-tier journals and conferences, he demonstrates significant academic impact in both theoretical and applied research domains. Wang's research interests focus on the intersection of artificial intelligence and practical engineering applications. His work in machine learning includes developing novel algorithms for distance selection, corrosion prediction models for oil and gas infrastructure, and domain-specific large language models for geological applications. In computer vision, he has made contributions to building pattern recognition, image restoration techniques, and UAV-based environmental monitoring systems. His engineering research spans fault diagnosis systems, precision mechanical design, and human-robot interaction frameworks. Analysis of his recent publications reveals a strong trend toward integrating large language models with specialized domain knowledge, particularly in geological applications (GeoProspect) and robotic task reasoning (Double-Feedback). His work increasingly focuses on practical applications in high-risk industries, emergency response systems, and environmental monitoring, demonstrating a shift toward solving real-world problems with AI technologies. His scientific contributions demonstrate excellence across multiple domains, though specific awards are not documented in the provided information. The breadth of his collaborative work across different institutions and research areas highlights his interdisciplinary approach and academic leadership. Wang's research group appears to be involved in numerous projects related to AI-driven decision support systems, particularly in nuclear safety and emergency response scenarios. His recent work on human reliability analysis frameworks suggests active involvement in high-stakes application domains where human-machine collaboration is critical.

• Electrical Engineering
• Robotics
• Signal Processing
• +۷ مورد دیگر

Jewon Lee is a researcher with a focus on interdisciplinary domains spanning Electrical Engineering , Robotics , Signal Processing , and Computer Science . Their work includes collaborations with co-authors like Joon-Young Jung and Sang Woo Kim on fault diagnosis for permanent magnet synchronous machines, IQ data compression methods, and video streaming optimizations. Publications highlight expertise in sensor technology, telecommunications, and machine learning model efficiency. Research interests are diverse, encompassing sensor design , fault detection in electric motors , adaptive video streaming , and large language model (LLM) optimization . These areas are reflected in their contributions to journals such as IEEE Transactions on Industrial Electronics , Sensors , and IEEE Access , as well as conferences like ICTC and ICUFN. Their recent work on LLaMA-3.2-Vision and knowledge graphs indicates an expanding role in Computer Science and Open Science initiatives, aligning with global efforts like the National Research Data Infrastructure (NFDI) . Despite active contributions, specific awards, educational background, and student advising details are not publicly documented in the provided sources.