Find Academic Supervisors

Mohsen BadieyView profile

Professor

• Ocean Acoustics
• Signal Processing
• Machine Learning
• +4 more

Mohsen Badiey , Professor in the Department of Electrical and Computer Engineering at the University of Delaware's College of Engineering, leads the Ocean Acoustics & Engineering Laboratory (OAELab) with facilities at Evans Hall and STAR campus. His interdisciplinary work spans applied physics, mechanical systems, ocean sensing, and computational signal analysis. Research Focus: Geoacoustic inversion, waveguide physics, machine learning for seabed classification, and underwater communication challenges. Key Projects: Shallow Water 2006 (SW06) and Shallow Water Acoustic in Random Media (SWARM95) experiments analyzing nonlinear internal wave dynamics. Scientific Contributions include developing dictionary learning techniques for sound speed profile analysis, advancing graph neural networks for underwater signal processing, and studying acoustic propagation through intense internal waves. His work emphasizes both fundamental and applied research with field data collection and computational modeling. Recent Publications demonstrate expertise in physics-based machine learning for source localization, seabed classification, and time-varying signal reconstruction. His team's 2024 studies on transiting ocean observers and Sobolev graph networks highlight cutting-edge methodologies. Laboratory: OAELab employs specialized instrumentation for broadband acoustic signal analysis, combining experimental data with computational approaches to solve real-world oceanographic problems.

• Steel Structures
• Composite Materials
• Structural Stability
• +2 more

Andreas Taras is a Full Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, where he serves as Deputy Head of the Institute of Structural Engineering. His research focuses on steel and composite structures, structural stability, and fatigue behavior. Key research domains include: innovative steel-concrete-timber composite systems, stability and reliability of metallic structures, fatigue life prediction in bridges, and structural optimization using advanced analysis methods. His work integrates experimental testing with computational approaches to advance sustainable structural design. Recent investigations explore cutting-edge applications like iron-based shape memory alloys in structural glass systems, robotic additive joining for reclaimed steel, and novel hybrid systems combining folded sheet steel with cement-free concrete. Publications demonstrate consistent innovation in structural materials and connection technologies.

Silvia ToluView profile

Associate Professor

• Neurorobotics
• Bio-inspired Control Systems
• Neuromorphic Engineering
• +3 more

Silvia Tolu is an Associate Professor at the Technical University of Denmark's Department of Electrical and Photonics Engineering, specializing in Neurorobotics. She leads the NeuroRobotics Technology Lab (NRT-LAB), focusing on bio-mimetic control architectures for compliant robotic systems. Her research integrates neuroscience, computer science, and biology to develop solutions for assistive robotics and neurodegenerative disease diagnosis. Her research interests span: Neuro-robotics and neuromorphic engineering Bio-inspired control systems and adaptive motor control Machine learning for robotic applications Human-robot compliant interaction Cerebellar control models Publications primarily focus on neurorobotics, bio-inspired control, and human-robot interaction, with recent advances in learning-based control systems for soft robots and aerial manipulation. Awards include the AEG Elektrofonden Research Grant and funding for human-robot interaction safety research. Current projects include LOCOPD (Lundbeck Foundation), AEROTRAIN (EU Marie Curie ITN), and compliant human-robot interaction systems. She supervises multiple PhD students in neurorobotics and maintains international collaborations across Europe and Asia. Laboratory resources include advanced robotic platforms for musculoskeletal and soft robot control.

Jens Juul RasmussenView profile

Professor

• Plasma Physics
• Non-linear dynamics
• Fluid Dynamics
• +7 more

Jens Juul Rasmussen is a Professor in the Department of Physics at the Technical University of Denmark (DTU), where he conducts research in plasma physics, nonlinear dynamics, and fluid dynamics. His work is central to understanding turbulence, coherent structures, and transport in magnetically confined fusion plasmas. He is actively involved in international collaborations such as JET and ASDEX Upgrade, contributing to key advancements in fusion energy science. Department of Physics, DTU Research in Plasma and Nonlinear Physics Supervisor of multiple PhD projects Active contributor to EU fusion programs His research interests span plasma turbulence, solitons, vortices, L-H transitions, and transport phenomena in fusion devices. He applies dynamical systems theory and numerical modeling to understand the complex behavior of plasma edge and core regions. His work bridges theoretical physics with practical fusion reactor design challenges. The recent publications highlight a strong focus on turbulence modeling, wave interactions, isotope effects in fusion plasmas, and diagnostic development. Trends show sustained contributions to understanding transport barriers, fast-ion dynamics, and filamentary structures in the scrape-off layer. His work integrates fractional kinetics, collective scattering, and global modeling approaches. Jens Juul Rasmussen has supervised numerous PhD students, including Jens M. B. Olsen, Andreas S. Thrysøe, Mikkel Dam, and others, in projects related to coherent structures, L-H transitions, and blob dynamics. He has participated in major fusion research initiatives and served on scientific committees. Though no specific awards are listed, his extensive publication record and leadership roles indicate high recognition in the field. He has been involved in projects such as 'Studies of Coherent Structures in Magnetically Confined Plasmas' and 'Global Modelling of Turbulence and Transport,' often collaborating with leading institutions in Europe. His activities include invited talks at the EU-US Transport Task Force and workshops on wave-flow interactions, demonstrating his active engagement in the scientific community.

Tim HappelView profile

Research Professor

• Plasma Physics
• Nuclear Fusion
• Tokamak Physics
• +7 more

Dr. Tim Happel is a leading researcher in plasma physics and fusion energy, affiliated with the Max Planck Institute for Plasma Physics (IPP) in Garching, Germany, where he serves as Head of the Plasma Dynamics Division and a Scientific Member of the Max Planck Society since 2024. He also lectures at the University of Ulm, contributing to academic education in plasma physics. His research focuses on turbulence, confinement regimes, and advanced tokamak operation, particularly using the ASDEX Upgrade device. His research interests center on plasma turbulence in tokamaks, with a special emphasis on the Improved Energy Confinement Mode (I-mode) and discharges with negative triangularity, which are promising for future fusion reactors. He investigates turbulence-flow interactions, develops diagnostics like Doppler reflectometry, and validates gyrokinetic simulations against experiments. His work bridges theoretical modeling and experimental validation to improve predictive capabilities for ITER and DEMO. The 15 most recent publications highlight a strong trend toward predictive fusion science, with studies on gyrokinetic code validation (e.g., GENE), edge-localized modes, pedestal physics, and negative triangularity configurations. These works are published in top journals like Nuclear Fusion and Nature Communications , reflecting his leadership in advancing core and edge plasma physics for next-generation fusion devices. Itoh Prize for Plasma Turbulence (for doctoral work on Doppler reflectometry) Dr. Happel leads the Turbulence research group at IPP since 2023 and has been instrumental in major collaborative efforts, including the EUROfusion Tokamak Exploitation programme. His work is supported by extensive experimental campaigns and international grants, though specific funding sources are not detailed. He collaborates widely across institutions, as seen in co-authorship with teams from EUROfusion, ASDEX Upgrade, and other tokamak facilities. He heads the Plasma Dynamics Division at IPP, overseeing research on turbulence, transport, and confinement optimization in fusion plasmas. His team integrates experimental diagnostics, advanced data analysis, and high-performance simulations to tackle key challenges in plasma physics.

Marcus HandteView profile

Researcher

• Computer Science
• Pervasive Computing
• Sustainable Mobility
• +3 more

Dr. Marcus Handte is a Senior Researcher at the University of Duisburg-Essen, focusing on networked embedded systems, context-aware computing, and sustainable mobility. His academic journey includes a Habilitation in Computer Science (2013) and a PhD in Natural Sciences (2009) from Universität Stuttgart, alongside a Master's degree from Georgia Institute of Technology (2002). Research Interests Context-aware applications Localization and location-based systems Sustainable mobility solutions Internet of Things (IoT) Smart city infrastructure Privacy-preserving technologies His recent work involves developing platforms for multimodal mobility analysis (MOBYDEX), wireless EV charging systems (TALAKO, FAIR), and innovative approaches to indoor localization. Publications span journals like Machine Vision and Applications and conferences in pervasive computing. Scientific Recognition Best Poster Award at ACM KMIS 2023 Dr. Handte has contributed to projects such as ATMo2, INNAMORUHR, and GAMBAS, and maintains active collaborations across institutions. His expertise in adaptive middleware and distributed systems continues to shape research in smart mobility and ambient intelligence.

Zhenyu ChenView profile

Professor

• AI for Testing
• Testing for AI
• Software Testing
• +3 more

Zhenyu Chen is a Full Professor and Director of the iSE Laboratory at Nanjing University, specializing in AI-driven software testing methodologies. His research bridges artificial intelligence and software engineering with dual focus areas: leveraging AI to enhance testing processes ( AI for Testing ) and validating AI/ML systems ( Testing for AI ). His research interests center on deep learning framework testing , crowdsourced testing optimization , and Large Language Model applications in verification . Recent work demonstrates innovative approaches to metamorphic testing of neural networks, LLM-based test report analysis, and security hardening of code models against backdoors. Key contributions include the development of mooctest.com and frameworks like DevMuT for mutation testing of deep learning APIs. His publication trajectory reveals evolving focus from crowdsourced testing (2018-2020) to deep learning system validation (2021-2023) and current emphasis on LLM-powered testing solutions. Major venues include ASE, ICSE, and ISSTA where he serves regularly on program committees.

V. MenkovskiView profile

Associate Professor

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +7 more

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.

• Internet of Things
• RFID and antenna design
• Wireless sensor networks
• +5 more

Turke Althobaiti is an active researcher and faculty member whose recent work is concentrated in electrical and computer engineering, with strong interdisciplinary links to computer science and biomedical informatics. Based on co-author affiliations and publication scopes, he is associated with King Saud University, College of Engineering, Department of Electrical Engineering . Research Interests: Design of UHF RFID antennas and Internet-of-Things sensing systems. Localization and communication in smart cities, including non-line-of-sight mitigation and 5G/6G networks. Machine-learning-driven healthcare applications—ranging from COVID-19 detection via chest X-rays to arrhythmia and pneumonia screening. Assistive technologies for the visually impaired, employing contactless RF sensing and AI-based navigation aids. Cloud-security solutions, specifically ensemble intrusion-detection systems against flash-crowd attacks. Cross-disciplinary forays into metabolomics biomarkers and human-animal affective computing. Across 15 recent publications (2019-2025), Althobaiti demonstrates a clear trajectory toward AI-enabled sensing and communication . Workflows combine hardware-level innovations (antennas, RFID tags, USRP radios) with data-level advances (deep learning, ensemble methods, privacy-preserving techniques) to address real-world problems in healthcare, smart cities, and assistive living. Scientific Awards & Recognition: No specific awards are listed in the provided text. Advising & Grants: While no explicit list of students or funded projects is given, the high volume of multi-institutional collaborations and senior-author positions suggest active supervision of graduate researchers and participation in funded projects, most likely supported by the Deanship of Scientific Research at King Saud University or similar Saudi funding bodies. Laboratories & Teams: Though no formal laboratory names are provided, the breadth of hardware prototyping, RF experimentation, and AI model development implies access to well-equipped laboratories in RF/microwave engineering, embedded systems, and computational intelligence.

• Aerospace Engineering
• Experimental Aerodynamics
• Supersonic Jets
• +6 more

Raffaello Mariani is an Associate Professor at the Royal Institute of Technology (KTH), affiliated with the AEROSPACE, MOVEABILITY AND NAVAL ARCHITECTURE school and the Aeronautical and Vehicle Engineering Unit. He holds a BSc in Aerospace Engineering from Embry-Riddle Aeronautical University (2003), an MEng in Experimental Methods from Old Dominion University (2005), and a PhD in Fundamental Fluid Dynamics from The University of Manchester (2012). His career includes roles at BMT FM, ONERA, and Nanyang Technological University before joining KTH in 2018. Research focuses on experimental aerodynamics, supersonic jets, shock wave dynamics, and UAV design. Key areas include wind tunnel testing techniques (e.g., rainbow schlieren), flow control strategies, and hybrid-electric propulsion systems for sustainable aviation. He leads the Green Raven project, developing a hydrogen-powered blended-wing-body UAV to combat climate change. Teaching responsibilities include courses like Advanced Topics in Aeronautics and Future Sustainable Aviation . Active in interdisciplinary collaborations, he integrates electrochemistry, mechatronics, and embedded systems into aerospace engineering solutions. Award-winning contributions include pioneering work on vortex ring interactions and supersonic jet noise mitigation. His recent studies explore bio-inspired wing designs and ground-effect aircraft optimization.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +9 more

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

• Magnetic Confinement Fusion
• Plasma Physics
• Tokamak
• +12 more

Jonathan Graves is Professor of Physics at the University of York's School of Physics, Engineering and Technology and Senior Scientist at EPFL's Swiss Plasma Center (SPC-TH). He serves as Editor-in-Chief of Plasma Physics and Controlled Fusion and Director of the Varenna-Lausanne International Workshop in Theory of Fusion Plasmas. His research focuses on magnetohydrodynamic stability, kinetic theory, and transport phenomena in tokamak and stellarator plasmas, with applications to magnetic confinement fusion and ITER/JET experiments. PhD in Theoretical Mechanics (University of Nottingham, 1999), preceded by a first-class joint honors in Electronic Engineering and Mathematics (1996) Senior Scientist at EPFL since 2014 Member of EUROfusion STAC and DEMO Technical Advisory Group since 2015 His research interests include: magnetohydrodynamic confinement and stability of tokamak plasmas, linear and non-linear resistive instabilities, long-wavelength kinetic instabilities, fast particle physics, 3D Alfvenic and wave codes (LEMan), guiding centre theory (VENUS-LEVIS), ion cyclotron resonance heating in 3D (SCENIC code), and impurity transport in rotating plasmas. He has directed PhD theses on topics such as Equilibrium β-limits in stellarators , Heavy impurity transport in 3D perturbed plasmas , and Non-linear MHD modeling in tokamaks . His recent publications emphasize tokamak β-limits, fast ion generation in stellarators, impurity transport under MHD perturbations, and 3D equilibrium modeling. Key tools developed include the VENUS-LEVIS guiding centre code, SCENIC ion cyclotron resonance code, and LEMan Alfvenic wave solver. Collaborative work spans JET, TCV, and Wendelstein 7-X experiments. Graves has contributed to integrated modeling of fusion plasmas, including neutron activation dosimetry, Alfven eigenmode analysis, and disruption prediction algorithms. His team at EPFL and York explores kinetic-MHD interactions, plasma diagnostics via tomography, and optimization of auxiliary heating schemes for reactors.

Vincent TournatView profile

Researcher

• Nonlinear Acoustics
• Mechanical Metamaterials
• Wave Propagation
• +5 more

Vincent Tournat is a CNRS Researcher affiliated with the Institute of Acoustics at Le Mans Université. His work spans nonlinear acoustics, mechanical metamaterials, and wave propagation in complex media.

Kristín Björg ArnardóttirView profile

Research Fellow

• Quantum Optics
• Condensed Matter Physics
• Nonlinear Optics
• +3 more

Kristín Björg Arnardóttir is a Research Fellow at POLIMA, University of Southern Denmark. Her research focuses on quantum optics, condensed matter physics, and theoretical physics, particularly in polariton dynamics, light-matter interactions, and open quantum systems. She holds a BSc in Physics from the University of Iceland, an MSc in Theoretical Physics from Stockholm University, and a PhD from Nanyang Technological University (Singapore), where her work centered on strong light-matter coupling in solid-state systems. Prior to joining SDU in 2024, she conducted postdoctoral research at the University of St Andrews, UK, investigating organic polaritons and non-Markovian dynamics under Professors Jonathan Keeling and Brendon Lovett. Her current research at POLIMA explores polariton-mediated energy transfer and chiral polaritons under the supervision of Christos Tserkezis. Her academic trajectory includes significant contributions to understanding cavity quantum electrodynamics, nanocavity lasing mechanisms, and topological effects in materials. Recent work emphasizes non-Markovian effects in long-range energy transfer and temporal dynamics in polariton systems. Projects span theoretical modeling and interdisciplinary applications of quantum phenomena in optoelectronic systems. Key research themes include: Polariton-mediated energy transfer mechanisms Organic polariton lasing dynamics Non-equilibrium quantum systems Cavity QED in low-dimensional structures Topological effects in condensed matter No scientific awards or grants are explicitly listed in the provided information. Her work is part of the POLIMA research group, focusing on advancing theoretical frameworks for quantum optical systems with applications in next-generation optoelectronics and quantum technologies.

Amine BenzergaView profile

Professor

• Materials Science and Engineering
• Mechanics of Materials
• High-Performance Computing
• +5 more

Amine Benzerga is a Professor of Aerospace Engineering and Materials Science & Engineering at Texas A&M University, holding the General Dynamics endowed chair. As Director of the Center for Intelligent Materials and Structures (CiMMS), he leads cutting-edge research in smart material systems. He is affiliated with the Department of Aerospace Engineering within the College of Engineering, and his work integrates computational and experimental approaches to advance materials science. Education: Ph.D., Material Science & Engineering, École des Mines de Paris, France (2000) M.Sc., Mechanical Engineering, Université Paul Sabatier, Toulouse (1995) B.Sc., Aerospace Engineering, Sup'Aero, Toulouse (1995) Classes Préparatoires, Physics, Lycée Masséna, Nice (1989-1992) Research Interests: His work focuses on Mechanics of Materials , High-Performance Computing , and Anisotropy in Plasticity and Fracture . He explores Ductile Fracture mechanisms, Discrete Dislocation Plasticity , and Macromolecular Mechanics of Polymers . Recent studies include stress triaxiality effects on fracture behavior in magnesium alloys and polymer composites, failure under severe shear, and computational modeling of spall dynamics during hypervelocity impacts. Awards & Honors: Faculty Fellow, Texas Engineering Experiment Station (2014) Select Young Faculty Award, Texas Engineering Experiment Station (2009) National Science Foundation CAREER Award (2008) Highest Honors for Ph.D. Dissertation Work (École des Mines de Paris, 2000) Grants & Advising: Amine has led grants such as the 2019 Multiscale Modeling of Damage Tolerance in Hexagonal Materials and the 2018 CyberTraining: CIC Computational Materials Science Summer School . His advisory work includes supervising experimental and computational studies on void coalescence and plasticity in metals and polymers. Labs & Teams: As CiMMS Director, he oversees interdisciplinary projects in intelligent materials, including shape memory alloys and damage detection via sensory particles. His team collaborates on topics like texture effects in magnesium alloys and fracture locus path-dependence.