Find Academic Supervisors

• Data Analytics
• Internet of Things (IoT)
• Machine Learning
• +3 more

Shafique Chaudhry is an Assistant Professor of Information Systems and Operations at the Reh School of Business, Clarkson University. He holds a Master's in Computer Science from the University of Punjab, Pakistan, and a PhD from Ajou University, Korea. His postdoctoral research at University College Cork, Ireland, preceded his faculty roles in Saudi Arabia and Oman. His research focuses on Data Analytics, IoT, and Machine Learning, with applications in cybersecurity, smart grids, and healthcare. He has authored over 30 peer-reviewed articles and holds two international patents. Notable awards include IITA Korea's research award and Dhofar University's Best Researcher award (2015). Dr. Chaudhry teaches courses such as Applied Data Analytics, Machine Learning, and Database Design. His work spans IoT security frameworks, sensor network optimization, and policy compliance systems. Recent trends in his publications emphasize hybrid ML techniques for IoT security, environmental sensor calibration, and pediatric neurosurgery outcome analysis. Education : MSc Computer Science (Punjab University), PhD (Ajou University) Patents : Two international patents (details not specified) Lab/Teams : Active in IoT, cybersecurity, and smart systems research groups

Riccardo FelicettiView profile

Research Fellow

• Fault Detection and Diagnosis
• Fault Tolerant Control
• Optimization
• +3 more

Riccardo Felicetti is a Research Fellow at the Department of Information Engineering, Università Politecnica delle Marche, within the Faculty of Engineering. He holds a Master's Degree (cum laude) in Computer and Automation Engineering (2016) and a Ph.D. (cum laude) in Information Engineering (2021) from the same university, with a thesis on “Active fault tolerant control for overactuated unmanned vehicles.” He also serves as Senior Tutor, coordinating tutor activities in the Faculty of Engineering. His research focuses on fault detection and diagnosis, fault-tolerant control, and optimization, with applications to unmanned vehicles (e.g., drones, ROVs) and energy management systems. He has contributed to advancements in systems like actuator fault tolerance, vibration-based fault detection, and energy optimization in smart grids. Key technical areas include disturbance observer-based control, adaptive Kalman filtering, and machine learning integration for system identification. His work bridges theoretical control frameworks with practical implementations in aerospace and marine robotics.

Riccardo RosatiView profile

Research Fellow

• Machine Learning
• Deep Learning
• Biomedical Engineering
• +5 more

Riccardo Rosati (born 1992) is a Research Fellow and Ph.D. student at the Polytechnic University of Marche, where his work focuses on Machine Learning and Deep Learning applications in biomedical engineering and Industry 4.0. He holds a Master of Science in Biomedical Engineering (cum laude, 2018) from the same institution and completed an internship at the Image Processing Division of Leiden University Medical Center (2018-2019). His current Ph.D. scholarship, funded by the Marche Region, supports research in "Embedded Automation and Edge Computing for Industry 4.0". Research Areas: Deep learning for biomedical imaging (e.g., facial liveness detection, lung ultrasound analysis) AI-driven predictive maintenance in industrial systems Augmented reality frameworks for industrial design Steganalysis and biometric-based security systems Human-centered AI applications Key Publications Trends: His recent work spans facial liveness detection, wastewater sensor forecasting, excavation monitoring, and predictive maintenance of ATMs. The research combines convolutional neural networks, LSTM architectures, and transformer models with applications in healthcare, industrial automation, and cybersecurity. Contact: Email r.rosati@univpm.it , Office: +39 071 2204465, Room: Q165_121.

Thomas StatherosView profile

Assistant Professor

• Autonomous Systems
• Aerospace Engineering
• Avionics
• +4 more

Thomas Statheros is an Assistant Professor in Aerospace Engineering (Avionics, Autonomy, and Safety) at Coventry University, affiliated with the Research Centre for Future Transport and Cities (CFTC). He leads the Critical Autonomy and Safety Laboratory (CAUSLab) and contributes to British Standards Institution (BSI) standards for autonomy and safety. With over 23 years of experience in industrial and academic research, he specializes in Autonomous Systems, AI, Embedded Systems, and Avionics Design, collaborating with aerospace, automotive, and marine industries. He has designed safety-critical systems for projects like the LEMV airship and helicopter weather radar control panels, adhering to standards like DO-178B and ARP4754. His research focuses on Autonomous Systems Architecture, eVTOL aircraft, Avionics, and Safety-Critical Systems. Recent work includes developing efficient certification processes for aerospace and automotive industries, as well as designing verification/validation frameworks using simulators like Gazebo/Omniverse. He has led projects such as the EV-LIFT (Innovate UK), contributing £389K to Coventry University’s research on electric vehicle safety and performance. Key research trends in his articles include advancements in eVTOL design methodologies, battery health monitoring via optical sensors, and secure communication systems for non-terrestrial networks. His work bridges academic innovation with industrial application, emphasizing real-world testing and certification challenges. Teaching contributions include pioneering requirement-based verification in avionics modules and supervising over 35 undergraduate/postgraduate projects on autonomy, control systems, and safety-critical engineering. Collaborative efforts span industries and academic institutions, with notable partnerships in the UK, Europe, and the US. Current initiatives aim to address sustainability in aerospace and automotive sectors through hydrogen production and energy-efficient technologies.

Yuejian ChenView profile

Assistant Professor

• Dynamic Modelling
• Vibration Signal Processing
• Artificial Intelligence
• +5 more

Dr. Yuejian Chen is an Assistant Professor in the Department of Mechanical Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on dynamic modelling, vibration signal processing, artificial intelligence, and prognosis/health management for complex systems like railway vehicles, wind turbines, and steam turbines. He holds a PhD from the University of Alberta (2020) and was a postdoctoral fellow there until 2021. Prior to joining the University of Manitoba in 2024, he served as an Assistant Professor at Tongji University's Institute of Rail Transit in China from 2021 to 2024. His research integrates digital twins and advanced health management methodologies to ensure safe and cost-effective operation of mechanical systems. Key areas include non-stationary vibration analysis, fault detection via AI (e.g., LSTM networks), and physics-informed machine learning. He has authored/co-authored over 60 publications and serves on editorial boards for journals such as IEEE Sensors Journal , IEEE Transactions on Instrumentation and Measurement , and Smart and Resilient Transportation . Dr. Chen's work bridges theoretical advancements with practical applications, emphasizing railway transportation and energy systems. His recent publications (2021–2025) highlight contributions to gearbox fault detection under variable speed conditions, multi-camera metro monitoring systems, and data-driven load identification strategies. His research trends emphasize fusion of physical principles with AI for robust diagnostics and health management across mechanical systems. He is actively recruiting graduate students interested in dynamic systems, signal processing, and AI applications in mechanical engineering. His research group collaborates with industry partners to address real-world challenges in machinery safety and efficiency.

Qingjin PengView profile

Professor

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +5 more

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

Lorand SzaboView profile

Professor

• Electrical Machines
• Variable Reluctance Motors
• Linear Electric Generators
• +4 more

Lorand Szabo is a Full Professor at the Department of Electrical Machines and Drives , Faculty of Electrical Engineering , Technical University of Cluj-Napoca , Romania. He has been affiliated with this institution since 1990, transitioning from Research and Design Engineer to Lecturer (1999), Associate Professor (2007), and Full Professor (current). He also served as an Invited Professor at Pannon University (Hungary, 2007-2008) and University of Pavia (Italy, 2015). Teaching : Special Electrical Machines (1985-present), Electrical Machines and Drives (1996-present), Computer Aided Design of Electromechanical Systems (2009-present). Supervision : Ph.D. supervisor since 2007 for 8 students (6 completed). Research Focus : Linear and variable reluctance electrical machines, fault-tolerant designs, bearing fault detection in motors, and condition monitoring. His work extends to automotive applications (electric vehicles, hybrid systems), wind energy generators, and sustainable material sourcing for electrical machines. Recent Publications (2023-2025) address critical topics in sustainable mobility, including rare earth material dependency, deep-sea mining for electric technologies, battery management systems, and resource-efficient motor designs. These works reflect his interdisciplinary approach combining electrical machine innovation with environmental and industrial systems thinking. Technical Expertise : Finite element analysis (JMAG, ANSYS Motor-CAD), LabVIEW modeling, fault detection algorithms (wavelet transforms, kurtosis deconvolution), and modular motor construction for automotive and renewable energy applications.

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +5 more

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

Hubert BrücklView profile

Professor

• Thin Films
• Magnetism
• Sensors
• +3 more

Hubert Brückl is a Professor and Head of the Department for Integrated Sensor Systems at the University for Continuing Education Krems. He holds a PhD and habilitation in Physics from the University of Regensburg and has held key roles at institutions like the Technical University of Darmstadt, IFW Dresden, and the AIT Austrian Institute of Technology. His research focuses on thin films, magnetism, sensors, and micro/nanotechnology, with significant contributions to machine learning applications in materials science. Brückl's scientific career includes leadership in over 15 funded research projects, such as the development of AI-based corrosion monitoring systems and advanced magnetic sensors. He has authored numerous publications in high-impact journals, including Nature Scientific Reports , Physical Review Applied , and IEEE Transactions on Magnetics . His work spans interdisciplinary areas, combining physics, engineering, and data science to create innovative sensor technologies. Current projects emphasize AI-driven predictive modeling of materials properties and high-precision magnetic sensor fabrication. Brückl's lab integrates cutting-edge facilities for nanotechnology and sensor development, collaborating with industry partners like Siemens and aerospace firms. His research addresses challenges in sustainable materials, biomedical diagnostics, and industrial monitoring systems.

Garofalakis MinosView profile

Professor

• Database Systems
• Data Streams
• Big Data Analytics
• +7 more

Minos Garofalakis is a Professor of Computer Science at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), where he directs the Software Technology and Network Applications Laboratory (SoftNet). He is also the Director of the Information Management Systems Institute (IMSI) at the Athena Research and Innovation Centre in Athens. Previously, he held roles at Yahoo! Research, Intel Research Berkeley, and Bell Laboratories, and was an Adjunct Associate Professor at UC Berkeley. Education: He earned a BSc in Computer Engineering from the University of Patras (1992), followed by MSc (1994) and PhD (1998) in Computer Science from the University of Wisconsin-Madison. Research Interests: His work focuses on Big Data analytics , including database systems, data streams, approximate query processing, probabilistic databases, and secure/private data analytics. Key areas include distributed stream processing, data synopses, and machine learning applications. He has authored over 150 papers and holds 29 patents, with an h-index of 63 and 13,500+ citations. Recent Work Trends: His recent articles emphasize scalable stream analytics (e.g., OmniSketch), privacy-preserving techniques, and distributed event processing. He explores challenges in handling high-velocity data streams, uncertainty in databases, and real-world applications like healthcare analytics. Scientific Awards: ACM Fellow (2018), IEEE Fellow (2017), TUC Excellence Award (2015), and multiple patents from Bell Labs/Yahoo/AT&T. Advising & Grants: He led EU projects such as FERARI, LEADS, and The Human Brain Project. His lab, SoftNet, develops tools for extreme-scale analytics and declarative networking. Current work includes interactive cross-platform analytics (Infore) and AI-driven medical data systems. Labs/Teams: Director of SoftNet Lab and IMSI. Collaborates with industry partners on distributed systems and privacy-preserving technologies.

Wei LeView profile

Associate Professor

• AI
• Machine Learning
• Software Engineering
• +5 more

Dr. Wei Le is an Associate Professor in the Department of Computer Science at Iowa State University. His primary affiliations include the Program Analysis & AI Lab and collaborative work with Mayo Clinic. He holds a Ph.D. in Computer Science from the University of Virginia (2010). His research focuses on the intersection of AI and software engineering, particularly in trustworthy AI systems, vulnerability detection, and program analysis. Key areas include AI for medicine, analyzing AI models, and improving software reliability through techniques like dataflow analysis and symbolic execution. Recent work emphasizes efficient vulnerability detection using deep learning (e.g., ICSE 2024), numerical instability in ML applications (FSE 2025), and GPU numerical testing (SC 2024). His research has been supported by NSF, Google, and DARPA. Notable awards include the Distinguished Paper Award at FSE 2016 and Best Presentation at FSE 2008. He has advised over 10 students and collaborates with institutions like Columbia University and UCLA. Teaching responsibilities include courses on program analysis (COM S 413/513), principles of programming languages, and advanced AI topics. He actively contributes to conferences as a PC member and area chair (e.g., ICSE, FSE).

Robyn LutzView profile

Professor

• Software Engineering
• Software Safety
• Molecular Programming
• +5 more

Robyn Lutz is a Distinguished Professor of Computer Science and Faculty Member in Bioinformatics and Computational Biology at Iowa State University. She directs the Laboratory for Software Safety (LSS) and co-directs the Laboratory for Molecular Programming (LAMP). Her research focuses on software engineering for safety-critical systems, molecular programming, and formal methods. She has held roles such as ACM Distinguished Scientist and IEEE Fellow. Her work spans federally funded projects including NASA’s safety-aware ecosystems for unmanned systems and NSF initiatives on molecular programming and software dependability. She has authored influential publications on safety assurance cases, chemical reaction networks, and requirements engineering for nanoscale systems. Dr. Lutz has served on program committees for major conferences like ICSE, RE, and FSE, and delivered keynotes at venues like SAFECOMP and RE 2016. Her contributions include advancements in safety-critical software design, molecular systems verification, and interlocking safety frameworks for autonomous systems. She teaches courses such as Software System Safety and Requirements Engineering, and has developed tools like PLFaultCAT for safety analysis. Her labs focus on bridging software engineering principles with emerging domains like synthetic biology and nanotechnology.

Sinan GeziciView profile

Professor

• Wireless Communication
• Signal Processing
• Electrical Engineering
• +3 more

Prof. Sinan Gezici is a faculty member in the Department of Electrical and Electronics Engineering at Bilkent University. He was awarded the TÜBİTAK Science Award 2024 for outstanding contributions to location estimation in wireless communication systems. His research focuses on wireless communication systems, signal processing, and localization technologies involving intelligent reflecting surfaces (RIS) and visible light positioning. Affiliations: Bilkent University, Faculty of Engineering. Awards: TÜBİTAK Science Award 2024 (Engineering Sciences). Research interests include RIS-aided systems, secure communication protocols, non-line-of-sight (NLoS) localization, and parameter estimation under adversarial conditions. His work bridges theoretical foundations with practical implementations in next-generation wireless networks. Recent articles highlight advancements in RIS design for sensing and localization, visible light positioning resilience, and game-theoretic approaches to strategic communication.

Motagh M.View profile

Researcher

• Remote Sensing
• Geodesy
• Seismology
• +3 more

Motagh M. is a Researcher at the Deutsches GeoForschungsZentrum (GFZ) in Potsdam, Germany. With expertise in Remote Sensing and Geodesy , their work focuses on geohazard assessment through satellite-based interferometric techniques. Current research explores land subsidence in Afghanistan and Iran using Sentinel-1 data Developing hybrid machine learning models for landslide susceptibility assessment Pioneering quantum machine learning applications in deformation detection Investigating mining-induced subsidence in Germany's Hambach region Contributing to multi-sensor InSAR integration for complex geological analysis Publications since 2011 demonstrate sustained contributions to: Earthquake source modeling (e.g., Christchurch 2011, Maule Chile 2010) Volcanic deformation studies in Iceland Environmental geoscience applications across three continents Collaborations span institutions in: New Zealand (University of Canterbury, GNS Science) Iran (University of Tehran, K. N. Toosi University) Germany (GFZ Potsdam, Technical University Munich) USA (University of Miami)

• Image Processing
• Computer Vision
• Signal Processing
• +4 more

Sos S. Agaian is a Professor affiliated with City University of New York (CUNY), USA, with a former position at the University of Texas at San Antonio's Department of Electrical and Computer Engineering. His research focuses on advanced image and signal processing techniques, including thermal imaging, medical imaging, neural networks, and computer vision applications. He has authored numerous publications in top-tier journals and conferences, contributing to fields like adversarial attack defenses, solar panel segmentation, and quantum-inspired algorithms. His work emphasizes practical solutions for image enhancement, deblurring, and segmentation, with applications in healthcare, energy systems, and cybersecurity. Research Interests: Image Processing and Computer Vision Signal Processing and Machine Learning Medical and Thermal Imaging Neural Networks and Deep Learning Quantum Computing Applications in Imaging Recent Trends in Articles: Recent work highlights advancements in quaternion-based neural networks for weather removal, lightweight networks for solar panel fault detection, and novel entropy models for thermal imaging. Collaborations span medical diagnostics, cybersecurity, and energy systems, reflecting interdisciplinary impact. Grants and Collaborations: While specific grants are not detailed, his extensive publication record suggests active research funding in areas like computer vision and quantum information processing. Collaborators include institutions globally, emphasizing translational research. Labs/Teams: Not explicitly mentioned, but his work implies involvement in imaging and machine learning research groups focused on practical solutions for real-world challenges.