Find Academic Supervisors

• Geophysics
• Seismic Engineering
• Non-destructive testing
• +5 more

Maria de La Vega Perez-Gracia is a Professor at the Department of Materials Resistance and Engineering Structures at Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). She leads the GiES (Geofísica i Enginyeria Sísmica) research group with an extensive publication record spanning nearly three decades, totaling 289 documented academic activities. Her research focuses on the application of Ground Penetrating Radar (GPR) technology across multiple domains. She has pioneered techniques for non-destructive testing of structures, seismic hazard assessment, cultural heritage preservation, and precision agriculture applications. Her work demonstrates a clear evolution from fundamental geophysical research toward increasingly applied, interdisciplinary projects that integrate robotics, AI, and digital twin technologies. The analysis of her recent publications reveals a strong trend toward practical applications of GPR technology, particularly in agricultural optimization and sustainable resource management. Her research has expanded from traditional civil engineering applications to include innovative solutions for irrigation management, root system analysis, and resource recovery from built environments. As a principal investigator, she has led numerous competitive research projects funded by European and Spanish programs, including Horizon Europe and various national research plans. Her collaborative network includes over 100 researchers across multiple departments at UPC, with particularly strong connections to Luis Gonzaga Pujades Beneit, Jose Oriol Caselles Magallon, and Jaime Clapes Boixader. Her research has been published in high-impact journals including Remote Sensing, International Journal of Architectural Heritage, and Journal of Archaeological Science, reflecting the interdisciplinary nature of her work that bridges engineering, geophysics, and cultural heritage conservation.

Chung Hwan KimView profile

Assistant Professor

• Computer Systems Security
• Cyber-Physical Security
• Software Security and Reliability
• +3 more

Chung Hwan Kim serves as an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas, where he directs the Software & Systems Security Laboratory (S³ Lab). His research focuses on critical security challenges in cyber-physical systems, embedded devices, and cloud infrastructure, with recognition including the NSF CAREER Award and UT Dallas New Faculty Research Symposium Grant. His expertise spans Computer Systems Security , Cyber-Physical Security , and Software Security and Reliability , emphasizing practical solutions for robotic vehicles, autonomous systems, and trusted execution environments. Current projects address signal injection attacks, resilience testing, and confidential computing through innovative fuzzing frameworks and hardware-assisted protections. Recent publications (2020-2026) reveal three dominant research thrusts: (1) Security for autonomous/robotic systems ( DriveFuzz , IMUFUZZER ), (2) Trusted execution in constrained environments ( Vessels , GEVisor ), and (3) Automated vulnerability discovery ( HFL , TZ-DATASHIELD ), consistently appearing in top venues like IEEE S&P and USENIX Security. Key honors include: NSF CAREER Award (premier early-career recognition) UT Dallas New Faculty Research Symposium Grant Top 10 finalist for CSAW Best Applied Research Paper Award (2018) As principal investigator of the S³ Lab, Kim mentors graduate researchers and secures competitive funding for projects spanning robotic vehicle security, embedded systems hardening, and confidential computing. His teaching portfolio includes Operating Systems, Information Security, and specialized courses on CPS/IoT security. The S³ Lab develops deployable security tools like TZ-DATASHIELD for embedded data protection and IMUFUZZER for resilience testing of aerial vehicles, collaborating with industry partners to translate research into real-world solutions for autonomous systems and critical infrastructure.

Marcel VoßhansView profile

Researcher

• Autonomous Driving
• Artificial Intelligence
• Computer Vision
• +3 more

Marcel Voßhans serves as a Researcher and Technical Project Leader for the AMEISE Team (Project RAFT) at Esslingen University of Applied Sciences' School of Computer Science and Engineering. He teaches specialized courses including Perceptional Algorithms, ROS, Sensory Systems (LiDAR/Camera), Autonomous Driving & Simulation, and Automotive Functional Safety. Education: 2015-2018: B.Eng. in Electrical Engineering and Information Technology, Hochschule Hannover 2018-2020: M.Sc. in Applied Computer Science: Autonomous Systems, Hochschule Esslingen Research Focus: His work centers on Autonomous Driving and AI-driven perception systems , with critical contributions to infrastructure reliability and sensor fusion for automated vehicles. He investigates how conventional road infrastructure interacts with autonomous systems through SLAM technology and deep learning-based environment detection. Publication Trends: His recent works demonstrate a cohesive research trajectory addressing real-world autonomous driving challenges, particularly in vehicle classification using stereo vision (2020), infrastructure-vehicle interaction requirements (2020), and conventional infrastructure reliability validation (2021). These studies emphasize practical implementation of AI in automotive contexts with strong safety considerations. Professional Background: Combines industry experience from Daimler AG (Steer-by-Wire development) and Continental Teves AG (testing rig automation) with academic leadership in the AMEISE research team. His international experience includes academic work in Sweden and China.

Tomonari FurukawaView profile

Professor

• Robotics
• Computational Mechanics
• Autonomous Systems
• +3 more

Tomonari Furukawa is a Professor and Zinn Faculty Scholar at the University of Virginia, leading the VICTOR Lab. He holds a B.Eng. in Mechanical Engineering from Waseda University (1990), an M.Eng. in Mechatronic Engineering from the University of Sydney (1993), and a Ph.D. in Quantum Engineering and Systems Science from the University of Tokyo (1996). His research focuses on robotics, computational mechanics, autonomous systems, and advanced sensor technologies. He has published over 300 papers, contributed to editorial boards, and secured grants such as the U.S. DOD DURIP grants for advanced research infrastructure. His work spans topics like autonomous robotic mapping, sensor fusion, and real-time deformation measurement for automotive safety. He has developed systems for tire tread profiling, crash deformation analysis, and 3D road surface reconstruction. Furukawa’s methodologies often integrate Bayesian approaches, neural networks, and multi-sensor data fusion to solve complex engineering challenges. His contributions to the NSF I/UCRC Centre for Tire Research highlight his impact on applied mechanics. Recipient of multiple career and paper awards, Furukawa emphasizes translational research. His VICTOR Lab explores cutting-edge robotics, including compliant bipedal designs for disaster response (e.g., DARPA Robotics Challenge) and autonomous navigation systems. Current projects leverage AI and advanced vision systems for infrastructure monitoring and human-robot collaboration.

Christiane SchmidtView profile

Academic

• Algorithm Design and Analysis
• Computational Geometry
• Air Traffic Management
• +3 more

Christiane Schmidt is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Communications and Transport Systems (KTS) research group. Her work focuses on algorithm design, air traffic management, railway optimization, and computational geometry. She contributes to both academic research and practical solutions for transportation systems. Education and Research: While specific educational details are not provided, her research emphasizes algorithmic solutions for complex transportation challenges. Her team develops models for air traffic controller workload prediction using machine learning and explores late train-crew rescheduling via heuristic approaches. Research Interests: Key areas include railway traffic optimization, air traffic management systems, and computational geometry applications. Recent projects involve guard route optimization for polygons, multi-agent path planning, and automated aircraft arrival scheduling. Publications: Her 2025 work highlights advancements in tabu-search-based scheduling, machine learning for air traffic analysis, and train path insertion methods. Earlier papers address workforce scheduling and disaster recovery in transportation networks. Labs and Teams: Active within the Communications and Transport Systems group, she collaborates on projects like the Flight Logistics Unit (FL) and contributes to initiatives like B-PREPARED for disaster preparedness.

• Antenna Engineering
• Radar Technology
• Electromagnetic Fields
• +3 more

Prof. Dirk Heberling is a Universitätsprofessor at RWTH Aachen University, leading the Institute for High Frequency Technology. His research focuses on advanced antenna systems, radar technology, and electromagnetic field exposure assessment in 5G/6G networks. He specializes in high-frequency components, including leaky-wave antennas, reconfigurable intelligent surfaces, and robot-based antenna measurement systems. His work addresses challenges in antenna design, signal processing, and environmental compliance for next-generation communication infrastructure. Key research areas include: Development of wideband antennas for IoT and smart building applications Numerical analysis of near-field antenna measurements and phase recovery techniques Assessment of radio frequency exposure from massive MIMO base stations using digital twins Integration of radar systems with automotive technologies for self-localization and object detection Optimization of antenna arrays to mitigate grating lobes and phase synchronization issues His lab operates a state-of-the-art robot-based mm-wave test system capable of spherical near-field measurements and real-time auralization of aircraft noise. This infrastructure supports both fundamental research and industry collaborations in telecommunications, automotive radar, and environmental monitoring. Current projects emphasize: Exposure modeling for 6G networks considering increased base station utilization Generative adversarial networks for radar data synthesis Calibration techniques for polarimetric radar systems

• Robotics
• Control Systems
• Nuclear Fusion Engineering
• +5 more

Alberto Vale is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Lisbon's Instituto Superior Técnico. His research focuses on robotics, control systems, nuclear fusion engineering, and autonomous systems. He is affiliated with the Institute of Plasmas and Nuclear Fusion and teaches courses such as Control of Cyber-Physical Systems and Autonomous Systems . His work emphasizes remote maintenance systems for nuclear facilities, radiation detection technologies, and mobile robotics applications. Research interests include: Advanced control strategies for cyber-physical systems Development of autonomous systems for nuclear fusion facilities Radiation detection and localization using drones and mobile robots Path planning and navigation in constrained environments Recent publications highlight innovations in fusion diagnostics (e.g., reflectometry systems for DEMO), LIDAR-based SLAM techniques, and optimization of autonomous fleets for solar farm inspection. He has received the EDA Research, Technology, and Innovation Papers Award 2023 and the Best Presentation Award .

Dave WhiteView profile

Professor

• Offshore Renewable Energy
• Geotechnical Modelling
• Site Characterisation
• +3 more

Professor Dave White is a leading academic in Infrastructure Geotechnics at the University of Southampton's School of Engineering. He specializes in offshore geotechnical engineering with a focus on renewable energy infrastructure, seabed characterization, and subsea systems. His work bridges fundamental research with industry applications, contributing to design codes and innovative technologies such as robotic soil testing devices. University Affiliation: University of Southampton Current Roles: Professor of Infrastructure Geotechnics, Research Group Leader (Infrastructure Group), Member of Southampton Marine & Maritime Institute Research Focus: Offshore wind energy, geotechnical modeling, seabed anchor systems, pipeline stability Recent research includes advanced mooring line-seabed interaction studies, dynamic anchor reliability analysis, and spatial planning for offshore wind farms. He leads major projects like the EU-funded MARITEC-X initiative and the UKRI-supported TAILWIND program. Key achievements include over 250 publications, development of PIV analysis software, and roles in technical committees like the Royal Academy of Engineering. Awards include the Telford Gold Medal and Fellowships from multiple engineering institutions. Active in PhD supervision across geotechnical engineering topics, with current advisees including Amin Rashidimehrabadi and Shengqi Xia. Collaborates with industry partners on projects involving subsea cable protection and floating offshore wind technologies.

Jiangfeng ZhangView profile

Associate Professor

• Battery Management Systems
• Smart Grid Integration
• Renewable Energy
• +2 more

Jiangfeng Zhang is an Associate Professor in the Department of Automotive Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on electric vehicle technologies, battery management, renewable energy integration, and smart grid solutions. Research Areas: His work spans battery optimization, grid resilience, autonomous vehicle control, and sustainable energy planning. Recent projects include solid-state battery advancements, V2G integration, and AI-driven energy management. Publication Trends: His articles predominantly address optimization challenges in electric mobility and grid stability, utilizing machine learning, game theory, and control systems. Key themes include decarbonization, cyber-physical security, and renewable integration under uncertainty.

Chengyu CaoView profile

Associate Professor

• Dynamics and Control
• Adaptive Systems
• Aerospace Engineering
• +6 more

Chengyu Cao is an Associate Professor at the University of Connecticut's School of Mechanical, Aerospace, and Manufacturing Engineering since 2008. He holds a Ph.D. in Mechanical Engineering from MIT (2004), an M.S. in Manufacturing Engineering from Boston University (1999), and a B.S. in Electronics and Information Engineering from Xi’an Jiaotong University (China, 1995). His research focuses on dynamics and control, adaptive systems, mechatronics, unmanned systems, and aerospace applications, alongside theoretical work in cosmology and physics, particularly his groundbreaking 'world membranes' theory challenging traditional relativity. Dr. Cao co-authored L1 Adaptive Control: Guaranteed Robustness with Fast Adaptation and authored the Universes are Black Holes series, proposing an axiomatic framework for spacetime dynamics. This theory addresses dark energy/matter, resolves cosmological paradoxes, and predicts novel phenomena awaiting experimental validation. His work bridges gaps between experimental data and theoretical models, emphasizing simplicity and precision. His engineering research spans advanced control strategies for aerospace systems, robotics, and energy infrastructure, with applications in autonomous vehicles, power plants, and combustion monitoring. He has published over 180 peer-reviewed papers and pioneered fiber-optic sensing technologies for high-temperature environments.

• Artificial Intelligence
• Neuromorphic Computing
• Brain-inspired Computing
• +13 more

Vittorio Fra is a Fixed-term Assistant Professor at the Interuniversity Department of Regional and Urban Studies and Planning (DIST) at Politecnico di Torino, where he conducts research in artificial intelligence, neuromorphic computing, edge computing, and robotics. He is a member of the PIC4SeR Interdepartmental Center for Service Robotics and contributes to interdisciplinary research bridging engineering, nanotechnology, and smart urban systems. His research interests center on AI and neuromorphic computing for industrial and IoT applications , with strong emphasis on brain-inspired computing, memristive devices, and nanoscale technologies. His work spans from low-level hardware characterization to high-level algorithm design, integrating machine learning, bio-inspired computing, and scientific simulation. He actively explores neuromorphic solutions for real-world edge applications such as human activity recognition, smart traffic control, and assistive technologies like Braille readers. The trend across his recent publications (2022–2025) reveals a consistent focus on deploying spiking neural networks and neuromorphic architectures on commercial edge devices, optimizing neural execution, developing benchmarking tools (e.g., NeuroBench, WiN-GUI), and validating neuromorphic solutions on practical problems like Sudoku and the knapsack problem. His work bridges theoretical AI with applied engineering, targeting sustainability and innovation in infrastructure and urban communities. Scientific Awards: No scientific awards explicitly mentioned in the text. Advising and Grants: Vittorio Fra supervises Filippo Aisa , a PhD candidate in Electrical, Electronic, and Communications Engineering. He leads a commercially funded research project titled Supporto allo sviluppo di un smart digital water distributor monitoring system (2025–2026) , serving as the Scientific Responsible. His teaching roles include PhD instruction, course collaboration, and invited membership in academic councils across engineering and planning programs. Labs and Research Teams: He is a member of the PoliTO Interdepartmental Centre for Service Robotics (PIC4SeR) , a multidisciplinary research center focused on robotics for societal applications. His collaborations span multiple institutions and projects, involving teams working on neuromorphic ecosystems (e.g., Lava-Loihi), wireless sensor networks, and brain-inspired computing frameworks.

• Green Logistics
• Operational Research
• Artificial Intelligence
• +12 more

Emrah Demir is a Professor of Operational Research in the Department of Logistics and Operations Management at Cardiff Business School, Cardiff University . Prior to this, he was an Assistant Professor at Eindhoven University of Technology, Netherlands. He holds a PhD in Management Science from the University of Southampton and degrees in Industrial Engineering from Baskent University, Turkey. Education: PhD in Management Science, University of Southampton, UK (2012) MSc in Industrial Engineering, Baskent University, Turkey (2008) BSc in Industrial Engineering, Baskent University, Turkey (2005) His research centers on green logistics, artificial intelligence, human/AI collaborative decision-making, and operational research , with a focus on optimizing freight transportation and supply chains for sustainability. He applies quantitative and analytical methods—including exact and heuristic optimization techniques—to address real-world logistics challenges. His recent work explores AI-driven routing, drone and robot delivery, maritime emissions, and behavioral aspects of algorithm adoption. The analysis of his recent publications reveals a strong integration of AI, sustainability, and logistics optimization . Key themes include last-mile delivery with autonomous systems, green vehicle routing, maritime decarbonization, and human-AI collaboration in decision-making. His work spans both theoretical modeling and practical applications in industry contexts, particularly with Ocado and in international sustainability initiatives. Scientific Contributions and Editorial Roles: Area Editor, Journal of Heuristics (Logistics and Supply Chain Management) Associate Editor, IMA Journal of Management Mathematics Associate Editor, Frontiers in Future Transportation – Freight Transport and Logistics Associate Editor, OR Spectrum Associate Editor (Engineering), Arabian Journal for Science and Engineering (from 2025) He has secured multiple external grants, including from the Colombia-UK PACT Programme (GIRO-ZERO) and Ocado Group plc , focusing on decarbonizing freight and optimizing supply chains. He supervises several PhD students on topics such as driver behavior, AI collaboration, and greenhouse gas emissions in shipping. He has held academic leadership roles including Programme Director for the MSc in Logistics and Operations Management and Deputy Head of Section for Learning & Teaching. Research Labs and Teams: He is affiliated with the Logistics and Systems Dynamics Group (LSDG) , Co-Director of the PARC Institute of Manufacturing, Logistics and Inventory , and Director of the Business & Economics Artificial Intelligence Research (BEAR) Network , reflecting his interdisciplinary engagement in AI, logistics, and sustainable operations.

Marc ChaumontView profile

Associate Professor

• Visual Data Analysis
• Remote Sensing
• Steganography
• +10 more

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

• Aerospace Manufacturing
• Autonomous Systems
• Aviation Management & Operations
• +8 more

Dr. Ip-Shing Fan is a Senior Lecturer in Enterprise Systems at Cranfield University's IVHM Centre, with academic career spanning over three decades. His work bridges technology implementation, organizational behavior, and complex system interactions across aerospace, manufacturing, and healthcare domains. Dr. Fan leads the Digital MRO and Hangar laboratories at Cranfield's DARTeC Initiative and serves as Course Director for the MSc in Aviation Digital Technology Management. PhD in Computer Integrated Manufacturing from Cranfield University BSc in Industrial Engineering (First Class Honours) from Hong Kong Commonwealth Scholarship recipient His research focuses on socio-technical systems in technology implementation, with specialization in Digital Twin applications for aircraft health management, Industrial Automation in aerospace contexts, and Human Factors in maintenance operations. He established the European BEST research consortium for enterprise system implementation knowledge and develops solutions for Predictive Maintenance , Robotic Inspection systems, and Agile Manufacturing frameworks. Key publication trends show expertise in Aerospace Informatics (2022-2025), Enterprise System Implementation (1995-2010), and Supply Chain Knowledge Modeling (2008-2010). Recent work addresses AI Document Reconciliation , Autonomous Inspection Systems , and 3D Printing Applications in aviation maintenance. Dr. Fan chairs the BCS Beds Bucks Northants Branch and has consulted for industry leaders including Boeing, BAE Systems, and SAP. His career combines academic research with practical implementation across Digital Transformation in aviation, Operational Optimization in healthcare, and Knowledge Engineering standards development.

• Cybersecurity
• Cyber-physical systems security
• Social network analysis
• +7 more

Andrey Chechulin is a Professor at the St. Petersburg Federal Research Center of the Russian Academy of Sciences, Institute of Informatics Problems, where he leads research in cybersecurity within the Information Security Department. His work spans over 15 years with more than 80 publications in top-tier security conferences and journals. Dr. Chechulin's primary research interests include cybersecurity, cyber-physical systems security, social network analysis, and bot detection. His work focuses on developing practical methodologies for incident investigation, access control, and vulnerability assessment. He has pioneered approaches for analyzing social media bots, particularly on the VKontakte platform, and developed innovative frameworks for cryptocurrency transaction anomaly detection using neural networks. His publication record shows consistent contributions to major security venues including PDP, COMSNETS, and Sensors, with a notable increase in output since 2018. Recent work demonstrates his adaptation to emerging security challenges in AI-generated content detection and blockchain security. Best Paper Award at PDP 2021 Cybersecurity Research Excellence Award (2019) Dr. Chechulin maintains strong collaborative relationships with Igor V. Kotenko (60 joint publications), Dmitry Levshun, and Maxim Kolomeets. His research often bridges theoretical security concepts with practical implementations for real-world systems, including smart city infrastructure and automotive security applications.