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Vasilis SarhosisView profile

Professor

• Structural Engineering
• Masonry Structures
• Structural Health Monitoring
• +7 more

Professor Vasilis Sarhosis is a Professor of Resilient Structures and Infrastructure at the School of Civil Engineering, University of Leeds, within the Faculty of Engineering and Physical Sciences. He also serves as a visiting Professor at Southeast University, Nanjing, China, and is a CDRI (Coalition for Disaster Resilience Infrastructure) Fellow. His academic credentials include multiple degrees from the University of Leeds: a Ph.D. in Computational modelling of low bond strength masonry (2012), an MSc in Sustainable Waste Management (2006), an MSc in Infrastructure Asset Maintenance and Management (2004), and a BEng in Civil Engineering (2003). Professor Sarhosis's research focuses on understanding the long-term behaviour of existing masonry infrastructure and historic structures. His work encompasses both 'blue skies' research and 'near-to-market' development, with a strong translational focus. His research group investigates inspection, monitoring and condition appraisal of masonry infrastructure; structural assessment; and maintenance, repair and rehabilitation of masonry infrastructure. His expertise spans machine learning, artificial intelligence, structural health monitoring, sensor technology, digital twins, cultural heritage preservation, and masonry structures. Analysis of his recent publications (2021-2023) reveals a strong focus on computational modeling of masonry structures, particularly using discrete element methods. His work integrates artificial intelligence and machine learning techniques for structural health monitoring and assessment. Key research areas include seismic performance of masonry structures, digital twin technology for infrastructure assessment, and the development of smart materials for structural monitoring and rehabilitation. His work frequently addresses heritage conservation challenges and disaster resilience for masonry infrastructure. Scientific Awards: International Partnership Award, University of Leeds (2021) Queen's Anniversary Prize for Higher and Further Education (2013) First price award for Commercialisation of Research: Project sDNA (2014) Professor Sarhosis has successfully secured significant research funding including EPSRC grants, UKRI HEIF funding, MSCA-IF projects, and British Academy GCRF funding. He serves as Subject Editor for Engineering Failure Analysis (Elsevier) and Associate Editor for the International Journal of Masonry Research and Innovation. He has examined numerous PhD theses at institutions worldwide including Heriot-Watt University, Newcastle University, Politecnico di Milano, and ETH-Zurich. His professional memberships include Fellow of the Institution of Civil Engineers (FICE), Fellow of the Institute of Mechanical Engineers (FIMEchE), and Chartered Engineer (CEng).

• Image Processing
• Machine Learning
• Artificial Intelligence
• +7 more

Dilip K. Prasad is a Professor at the Department of Informatics, UiT The Arctic University of Norway. His work bridges Artificial Intelligence and Medical Imaging , with a focus on Interpretable AI , Scalable AI , and Life Science Applications . He has contributed to Maritime Technology and Biomedical Engineering . Ph.D. and B.Tech from Nanyang Technological University and IIT Dhanbad Senior Research Fellow at NTU (2015-2019), Research Fellow at NUS (2012-2015) Industry experience at IBM, Infosys, Mediatek, Philips His research explores Image Processing , Machine Learning , and AI Applications in Biomedicine . Recent work includes Dense Video Captioning , 3D Mitochondrial Modeling , and Physics-Guided Loss Functions . Articles span Neurocomputing , Optics Express , and top AI conferences like CVPR and NeurIPS . Prasad has received the Rolls-Royce Inventor Award (2016) and Best Paper Award (IJCIE 2017) . He has reviewed for 50+ journals and 30+ conferences, serving as Area Chair for NeurIPS 2022-23 and Organizer Chair for ICCV Workshop 2023 .

Markus RothView profile

Professor

• Laser and Plasma Physics
• Energy Loss in Plasmas
• Laser Ion Acceleration
• +7 more

Prof. Markus Roth is a leading plasma physicist at the Technische Universität Darmstadt , heading the Laser and Plasma Physics Group within the Department of Physics . His research focuses on experimental investigations of laser-plasma interactions, particularly in developing novel radiation sources and advancing inertial fusion technologies. Research Interests : Energy loss mechanisms in laser-generated plasmas Laser ion acceleration (TNSA, BOA, and relativistic transparency) Laser-driven neutron sources for radiography Warm dense matter generation and diagnostics Target development for high-energy experiments Integration of laser-accelerated ions into accelerators Scientific Awards : APS Fellow (2013) Defense Physics Award of Excellence (2014) Rosen Scholar Award (2012, 2016) Collaborations : Active in international projects including FAIR, ELI-NP, and LIGHT collaboration. His group develops detectors for laser-driven neutron sources and contributes to fusion research.

Akshya GuptaView profile

Associate Professor

• Diagnostic Radiology
• Abdominal Imaging
• Ultrasound
• +3 more

Akshya Gupta, M.D. , is an Associate Professor in the Department of Imaging Sciences at the University of Rochester School of Medicine and Dentistry . Her clinical expertise spans diagnostic radiology with a focus on abdominal imaging, ultrasound, and multimodality imaging of neuroendocrine tumors.

Edward J. DelpView profile

Professor

• Image and Video Compression
• Multimedia Systems
• Image Processing
• +4 more

Edward J. Delp is the Charles William Harrison Distinguished Professor of Electrical and Computer Engineering and Professor of Biomedical Engineering at Purdue University , with a courtesy appointment in the Department of Psychological Sciences. He is affiliated with the School of Electrical and Computer Engineering and the School of Biomedical Engineering within Purdue's College of Engineering. B.S.E.E. (cum laude) and M.S. from the University of Cincinnati Ph.D. from Purdue University Honorary Doctor of Technology from Tampere University of Technology (2002) Research interests include: Image and Video Compression Multimedia Systems Computer Vision Medical Imaging Communication and Information Theory Image Analysis Scientific awards and honors : IEEE, SPIE, IS&T, and AIMBE Fellow Technical Achievement Award (IEEE Signal Processing Society, 2004) Society Award (IEEE Signal Processing Society, highest award, 2008) Morrill Award (Purdue's highest career achievement recognition, 2014) Electronic Imaging Scientist of the Year (IS&T/SPIE, 2015) SPIE Technology Achievement Award (2017) IS&T Honorary Membership Award (2017) MultIPLE teaching and mentoring awards Leadership roles include chairing IEEE technical committees, editorial board memberships, and conference organization. His work spans over 500 publications and contributions to multimedia security, medical imaging, and compression technologies.

Zhiqiang TaoView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Computational Imaging
• +7 more

Zhiqiang Tao is an Assistant Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). Prior to RIT, he served as an Assistant Professor at Santa Clara University (2020–2022). He holds a Ph.D. from Northeastern University (ECE Department) and B.E. and M.E. degrees from Tianjin University. Research Focus: His research group develops smart, scalable, and secure machine learning algorithms for healthcare, AI education, computational imaging, and data science. Key areas include large vision-language models (LVLMs), calibrated uncertainties in low-level vision, ranking fairness, and multimodal understanding. Recent works explore LLM fairness, medical question-answering systems, and diffusion models for text-video retrieval. Awards & Honors: RIT GCCIS Emerging Scholar Award (2025) Teaching: IDAI-780: Capstone Project ISTE-780: Data-Driven Knowledge Discovery Advising & Team: Actively seeking motivated graduate/undergraduate students to join his research group. His team has produced impactful work in NeurIPS, CVPR, ECCV, and IEEE TPAMI/TKDE. Labs & Collaboration: Collaborates across disciplines to advance AI applications in healthcare and education, leveraging computational imaging and fair ML principles.

Zin LinView profile

Assistant Professor

• Quantum Photonics
• Electromagnetics
• Nanophotonics
• +4 more

Zin Lin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, based at the Virginia Tech Research Center in Arlington. His research focuses on inverse design principles in nanophotonics, computational modeling, and scientific machine learning, with applications in quantum photonics, electromagnetics, and optical imaging. He leads the Inverse Design and Discovery Group, emphasizing large-scale optimization for physical systems and novel device discovery through physics-based AI. Education: Postdoc in Applied Mathematics at MIT (2018–2022), Ph.D. in Applied Physics from Harvard University (2018), and a B.A. in Physics and Mathematics from Wesleyan University (2012). He is a recipient of the National Science Foundation Graduate Fellowship (2014–2018). Research interests include inverse design of nanophotonic devices, topology optimization, quantum optics, and computational imaging. His group explores cutting-edge topics like metasurface engineering, terahertz wave generation, and bio-chemical sensing through physics-driven optimization frameworks. Recent work emphasizes scalable optical systems, such as end-to-end optimized metalenses and meta-optics for imaging, as well as quantum control in graphene-based metasurfaces. Key contributions span nonlinear frequency conversion, high-energy particle detection via nanophotonic scintillators, and topology-optimized multi-layered optical systems. Notable awards include the NSF Graduate Fellowship. His team actively pursues interdisciplinary projects at the intersection of wave physics, machine learning, and high-performance computing, with open positions for PhD students and postdocs.

Riccardo BonazzaView profile

Professor

• Experimental Fluid Mechanics
• Impulsive Fluid Flows
• Shock-Interface Interactions
• +7 more

Riccardo Bonazza is a Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering and the Nuclear Engineering & Engineering Physics program. His research focuses on experimental investigations of impulsive fluid flows, shock-interface interactions, and shock-driven mixing phenomena with applications in inertial confinement fusion, combustion systems, and aerospace engineering. Bonazza holds a PhD (1992) and MS (1985) from Caltech, and a Laurea in Mechanical Engineering (1983 cum laude) from Università di Ancona. His experimental work uses advanced techniques like planar Mie scattering, laser-induced fluorescence (PLIF), and particle image velocimetry (PIV) in the Wisconsin Shock Tube Laboratory. Key research areas include Richtmyer-Meshkov instability dynamics, shock-accelerated vortex rings, and reactive shock flows. His studies explore both detrimental mixing effects in fusion applications and beneficial mixing enhancement in supersonic combustion systems. Recent experiments involve shock-bubble interactions, reshock phenomena, and turbulent mixing quantification. Notable awards include the 2016 Leaders in Engineering & Diversity Scholar Award and 2011 Outstanding Instructor Award. His 2023 work includes novel bovine thermodynamic models and advanced shock tube diagnostics. Bonazza teaches courses in aerodynamics, gas dynamics, rocket propulsion, and independent research supervision. Key facilities: Wisconsin Shock Tube Laboratory. Active collaborations include CFD validation, laser diagnostics development, and multi-phase flow studies.

Saghi HajisharifView profile

Researcher

• Visual Machine Learning
• HDR Imaging
• Photo-realistic Rendering
• +5 more

Saghi Hajisharif is a Researcher at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) group. She holds a PhD in Visualization and Media Technology from Linköping University (2020), an MSc in Advanced Computer Graphics (2013), and a BSc in Computer Science from Amirkabir University (2009). Her work focuses on computational imaging, visual machine learning, HDR imaging, and light field technologies. She is a core member of the Computer Graphics and Image Processing research group. Research interests include sparse representation learning for computational imaging, synthetic data ethics, BRDF material modeling, and algorithmic fairness in AI. Her contributions span interdisciplinary projects recognized in IVA’s 100 List (2024), highlighting societal impact potential. She has co-authored influential studies on topics such as FROST-BRDF sampling techniques and metadata standards for GenAI synthetic data. Her articles reflect expertise in computer vision, graphics, and AI ethics, with key contributions to light field imaging, GAN fairness, and material modeling surveys. The IVA’s 100 List recognition underscores her innovative work’s societal relevance. She collaborates across disciplines to advance imaging technologies and ethical AI practices.

S. Alireza BehnejadView profile

Associate Professor

• Design and Construction of Spatial Structures
• Formex Configuration Processing
• Structural Performance of Bamboo
• +4 more

Dr. S. Alireza Behnejad is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Surrey, UK. He holds a PhD in Geometry of Spatial Structures (2018) and serves as Director of the Spatial Structures Research Centre founded in 1963. His roles include Programme Leader for Undergraduate Civil Engineering and member of the University Ethics Committee. Behnejad is Editor-in-Chief of the International Journal of Space Structures and a key member of the International Association for Shell and Spatial Structures (IASS), including organizing the 2020/21 virtual IASS conference. Behnejad’s research focuses on spatial structures, bamboo engineering, and pedagogical methods. Notable projects include the Bamboo For Sustainable Construction initiative (with global partners) and the DAD Project , promoting hands-on learning through physical models. He has been awarded the Vice-Chancellor's Teacher of the Year (2021) for enhancing student learning. His work bridges historical architectural forms (e.g., Iranian Karbandi vaults) with modern engineering challenges, emphasizing sustainability and education. Behnejad collaborates with institutions worldwide, including Brazil’s University of São Paulo and China’s Southwest Jiaotong University. He supervises postgraduate students on topics like spatial structure conceptual design and leads efforts to improve engineering education through full-scale physical models and international student exchanges.

• Ultrasonic Technology
• Nondestructive Testing
• Smart Materials and Structures
• +5 more

Professor Ningqun Guo holds the dual roles of Professor in Mechanical Engineering and Head of School for both the Malaysia School of Engineering and School of Information Technology at Monash University Malaysia. He previously served at Nanyang Technological University, Singapore. His academic journey includes a B.Eng from Nanjing University of Aeronautics and Astronautics (China) and a PhD from Imperial College London (UK). His research focuses on stress wave propagation, ultrasound applications, smart materials, nondestructive testing, and civil infrastructure analysis. He has published over 130 papers, secured S$2 million in research grants, and supervised over 10 PhD students. Key research areas include ultrasonic technology for material characterization, smart material systems, and image processing for infrastructure monitoring. His work aligns with UN Sustainable Development Goals, emphasizing sustainable infrastructure and innovation. Notable collaborations span global institutions, with recent projects exploring AI-driven pavement crack detection, transparent object reconstruction, and 3D imaging systems. His contributions to magnetorheological fluid applications and nanofluidics further underscore his interdisciplinary impact. Grants and funding have supported projects in sensor development, structural health monitoring, and advanced imaging technologies. His advisory work has produced impactful PhD graduates in mechanical engineering and materials science. Prof. Guo leads research teams focused on smart materials, nondestructive evaluation, and computational imaging. His lab integrates experimental and theoretical approaches to solve challenges in infrastructure, photonics, and nanotechnology.

Gian Luca MarcialisView profile

Associate Professor

• Biometrics
• Presentation Attack Detection
• Facial Recognition
• +4 more

Gian Luca Marcialis is an Associate Professor at the University of Cagliari, leading the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) within the Department of Electrical and Electronic Engineering. He holds an M.S. and Ph.D. in Electronic Engineering and Computer Science from the University of Cagliari (2000 and 2004). His research focuses on biometric security, including fingerprint presentation attack detection, facial deepfake detection, and crowd anomaly analysis. Key areas include multimodal biometric systems, liveness detection in authentication, and the ethical implications of AI in forensics. Marzialis has contributed to initiatives like the LivDet competitions and BullyBuster project against cyberbullying. His work integrates machine learning with real-world applications in surveillance, healthcare, and public safety, emphasizing both technical innovation and societal impact.

Andrew James NeelyView profile

Professor

• Hypersonic Aerothermodynamics
• Fluid-Structure Interaction
• Thermal-Structural Modelling
• +4 more

Professor Andrew James Neely is the Associate Dean (Research Engagement) at UNSW Canberra, where he also holds a full professorship in the School of Engineering and Technology. His research focuses on hypersonic aerothermodynamics, thermal-structural modeling, fluid-structure interaction (FSI), and biomechanics of nerve tissue. He leads international collaborations including the HIFiRE, HyCAUSE, and SCRAMSPACE flight experiment projects, and hosts the High-Speed FSI Unit Cases database. His key roles include Non-executive Director at Canberra Innovation Network (CBRIN), Fellow of the Royal Aeronautical Society (RAeS), and President of RAeS Australian Division (2017–2019). He founded the RAeS 'Cool Aeronautics' STEM outreach program and actively engages with organizations like the American Institute of Aeronautics and Astronautics (AIAA) and International Society for Air Breathing Engines (ISABE). His awards span technical excellence (AIAA Best Paper Awards) and STEM outreach (Harry Staubs Award). Research Grants : 2023: Hypersonic FTSI Unit Case for a Thermally Buckled Structural Panel (USAF AFOSR) 2023: NCI Supercomputer Time Allocation (Fluid-thermal-structural interactions) 2022: Trailblazer Universities Program funding for Defense research 2022: USAF AOARD project on shape distortion sensing 2021: Lockheed Martin Australia grant on hypersonic aerothermal shape distortion 2021: DST Group funding for FSI experiments Neely supervises PhD/MRes students in areas like hypersonic FSI, biomechanics, and propulsion systems. He has a strong track record of graduated students now working in aerospace industry and academia. His work bridges computational and experimental hypersonics, with recent studies on NASA AePW3 panel flutter and optic chiasm compression mechanics. Labs/Teams : Leads UNSW's hypersonics research group, collaborating with Canberra Hospital, ANU Medical School, and European partners through the HEXAFLY-INT project. Involved in advanced diagnostics like high-speed IR thermography and digital image correlation systems.

Zhang PinView profile

Assistant Professor

• Explainable AI for Science
• Intelligent Computational Mechanics
• Uncertainty Quantification
• +5 more

Dr. Zhang Pin is a Presidential Young Professorship (PYP) Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS). He previously held roles at the University of Cambridge as a Royal Society Newton International Fellow, Department Teacher, and Wolfson College Research Associate. His research group focuses on explainable AI for science, intelligent computational mechanics, uncertainty quantification, granular mechanics, coastal infrastructure resilience, and digital twins. Dr. Zhang earned his PhD from The Hong Kong Polytechnic University (2022), with a visiting scholar year at the University of Oxford. He holds a Master of Engineering (2019) and a Bachelor of Engineering (2016) from Hunan University and Hefei University of Technology, respectively. His research interests include: Explainable AI for scientific modeling Physics-informed machine learning Uncertainty quantification in geotechnical systems Micro/macro-scale granular mechanics Resilient coastal infrastructure design Digital twin technologies Notable achievements include recognition as a World’s Top 2% Scientist (2023-2024), PolyU PhD Thesis Merit Award, and the prestigious Royal Society Newton International Fellowship. His work bridges computational methods with geotechnical engineering challenges, emphasizing practical AI applications. Dr. Zhang has advised students like He GF and Wu HN, and his research has been published in top-tier journals such as Computer Methods in Applied Mechanics and Engineering and Géotechnique . He actively contributes to professional bodies including the British Geotechnical Association and International Society for Soil Mechanics (ISSMGE).

Yunzhong HouView profile

Research Fellow

• Computer Vision
• Deep Learning
• Multiview Detection
• +4 more

Dr. Yunzhong Hou is a Research Fellow at the School of Computing, The Australian National University (ANU), where he collaborates with Prof. Tom Gedeon and Dr. Liang Zheng. He holds a PhD in Computer Science from ANU (2019–2023) and a Bachelor's in Electronic Engineering from Tsinghua University (2014–2018). His research focuses on computer vision and deep learning, particularly in multiview detection, sensor optimization, and efficient AI systems. Education: PhD in Computer Science, ANU (2019–2023) Bachelor of Electronic Engineering, Tsinghua University (2014–2018) Research Interests: Multi-view detection and tracking Active vision and sensor optimization Efficient AI systems His work spans topics such as camera configuration optimization for pedestrian detection, deep learning for color quantization, and multi-camera systems. He has contributed to projects like the socioeconomic impact analysis of water reforms and privacy-preserving perception for robotics. Hou serves as a reviewer for top conferences (CVPR, ICCV) and journals (TPAMI, TIP). His research emphasizes scalable solutions for real-world applications, including drone vision control and edge computing optimizations. Current projects include machine learning for socio-economic analysis and privacy-aware robotic perception.