جست‌وجوی استاد و پژوهشگر

• Structural Health Monitoring (SHM)
• Composite Materials
• Fatigue Analysis
• +۹ مورد دیگر

Zahra Sharif Khodaei is a Professor in Aerospace Structural Durability and Health Monitoring at Imperial College London's Department of Aeronautics within the Faculty of Engineering. She earned her PhD in numerical modelling of functionally graded materials from Czech Technical University in 2008. Her professional experience includes a research associate role at Imperial College from 2009 before transitioning to a lectureship in 2014. Professor Khodaei holds prestigious fellowships from the Royal Aeronautical Society and Women in Engineering Society. Her research focuses on advanced structural health monitoring (SHM) techniques for aerospace composites, including fatigue analysis, damage detection via guided waves, and integration of physics-informed machine learning. Key areas include development of wireless sensor solutions, probabilistic frameworks for impact localization, and optimization of sensor networks for large composite structures. She also explores metamaterials, fiber optic sensing systems, and reliability analysis under environmental variations. Expertise: SHM, composite materials, fatigue analysis, neural networks, guided wave techniques Affiliations: Aerospace Materials and Structures, Space Engineering, Structural Integrity and Health Monitoring, The Composites Centre Recent work emphasizes baseline-free damage detection methods, probabilistic impact localization, and multi-objective sensor path optimization. Her publications span structural simulations, material characterization under extreme conditions, and hybrid sensor systems for aerospace applications. Scientific Awards: Fellow of Royal Aeronautical Society, Fellow of Women in Engineering Society Current research includes development of digital clone platforms for SHM system testing under uncertainty, vibration-based offshore wind turbine monitoring, and 3D-printed flexible strain sensors. She also investigates automated crack growth quantification using neural networks and temperature compensation frameworks for guided wave systems in large composites.

Rebecca A. Hutchinsonمشاهده پروفایل

دانشیار

• Machine Learning
• Ecology
• Computational Sustainability
• +۵ مورد دیگر

Rebecca A. Hutchinson is an Associate Professor at Oregon State University, affiliated with both the School of Electrical Engineering and Computer Science and the Department of Fisheries, Wildlife, and Conservation Sciences. Her research focuses on computational sustainability, integrating machine learning with ecological challenges to enhance ecosystem health and inform computer science. Key areas include species distribution modeling, occupancy models, and leveraging citizen science data. She has collaborated extensively with mentors like Thomas G. Dietterich and Matthew G. Betts, and contributed to the unmarked R package for ecological analysis. PhD in Computer Science from Carnegie Mellon University (2009), advised by Tom M. Mitchell. Bachelor’s in Computer Science & Engineering from Bucknell University (2002). Her work bridges statistical ecology and machine learning, addressing issues like habitat modeling, semi-parametric techniques in probabilistic models, and species interaction networks. Recent projects include improving species distribution models using spatial clustering and neural networks (StatEcoNet). Her research has been recognized with awards like the Best Paper (proposals track) at the 2021 ICML workshop on Climate Change. Grants include National Science Foundation SEES Fellowships and collaborations with organizations like the Fred Hutchinson Cancer Research Center. She advises on computational ecology tools and has mentored students in interdisciplinary projects combining AI and environmental science.

Ali Mehmanparast Nodehiمشاهده پروفایل

استاد

• Structural Integrity
• Offshore Renewable Energy
• Offshore Wind Energy
• +۷ مورد دیگر

Ali Mehmanparast Nodehi is a Professor of Structural Integrity at the University of Strathclyde, within the Department of Naval Architecture, Ocean and Marine Engineering, Faculty of Engineering. He plays a key leadership role in the Strathclyde-Oxford-Edinburgh Centre for Doctoral Training in Wind and Marine Energy Systems and Structures (WAMSS CDT), and previously led the REMS CDT programme. His work bridges academia and industry, with extensive collaborations across global offshore wind operators and research institutions. Education: MEng and PhD from Imperial College London; MBA from Cranfield University Professional Affiliations: Chartered Engineer (IMechE), Fellow of HEA, Chartered Manager (CMI) Editorial Roles: Energy Engineering Subject Editor for Engineering Failure Analysis ; editorial board member for Applied Ocean Research , Forces in Mechanics , Materials at High Temperatures , Journal of Multiscale Modelling , and Wind External Role: Member, UK Forum for Engineering Structural Integrity (FESI) Council since 2014 Prof. Mehmanparast's research centers on structural integrity, fatigue and fracture mechanics, and life extension of offshore renewable energy structures. His work integrates advanced modeling, experimental techniques, and additive manufacturing innovations to solve critical challenges in offshore wind and tidal energy systems. Key themes include corrosion-fatigue interactions, residual stress effects, and integrity assessment of welded and additively manufactured components. The recent publications highlight a consistent focus on enhancing the durability and reliability of offshore energy structures. Trends include the use of advanced materials (e.g., nickel coatings, LTT alloys), optimization of manufacturing processes (e.g., wire arc additive manufacturing), and refinement of fatigue design standards. Subfields span erosion resistance, crack growth prediction, S-N curve validation, and structural health monitoring, all contributing to safer, longer-lasting offshore installations. Scientific honors include: Chartered Engineer (CEng), Institution of Mechanical Engineers (IMechE) Fellow, Higher Education Academy (HEA) Chartered Manager (CMgr), Chartered Management Institute (CMI) Fellow, Chartered Management Institute Prof. Mehmanparast has secured and led major research grants, including the £7.5M EPSRC Co-Tide Programme Grant and the £3M SLIC Joint Industry Project. He has supervised over 10 PhD/EngD students to completion and currently mentors several doctoral researchers. His projects are industry-sponsored and address real-world challenges in structural lifecycle management. He also organizes and chairs international conferences, reinforcing his leadership in engineering structural integrity. He leads or co-leads multiple active research initiatives such as: Evaluation of Corrosion-Fatigue Behaviour in Offshore Wind Turbine Substructures Improving Fatigue Life Assessment by Considering Manufacturing Imperfections CoTide: Co-design for Scalable Tidal Stream Energy Structural Lifecycle Industry Collaboration (SLIC JIP)

• Failure probability
• Interval computation
• Statistics for imprecise data
• +۱۰ مورد دیگر

Marco De Angelis is a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering at the University of Strathclyde's Faculty of Engineering. His work focuses on computational methods for handling uncertainty in engineering systems, with applications in structural reliability and health monitoring. Education: PhD in Risk and Uncertainty (2015) from University of Liverpool's Institute for Risk and Uncertainty Master of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Bachelor of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Dr. De Angelis specializes in computing with imprecision, developing methods to propagate uncertainty through models using interval analysis, probability bounds, and other mathematical frameworks. His research enables rigorous inference with scarce empirical data and builds trust in simulation for structural reliability assessment. His work intersects civil engineering, computer science, and statistics, with particular emphasis on practical applications in infrastructure monitoring and risk assessment. His recent publications demonstrate a strong focus on high-dimensional uncertainty analysis, optimization under uncertainty, and verified computational methods for reliability engineering. The research shows increasing sophistication in handling complex uncertainty representations while maintaining computational tractability for real-world engineering problems. Scientific Awards: Best student paper (June 18, 2025) The NASA and DNV Challenge on Optimization under Uncertainty (June 17, 2025) Bronze poster award (July 27, 2021) Teaching and Learning Award (May 17, 2017) ISIPTA-IJAR Young Researcher Award (August 2015) Dr. De Angelis teaches structural engineering theory, computer programming, interval computation, probability theory, and machine learning to undergraduate students. He has developed teaching materials from scratch for advanced dynamics courses. He serves as Co-investigator on the REUN project (Reduction of Uncertainties in risk assessment of structures and infrastructures against Natural hazards) funded by the Royal Society of Edinburgh, running from April 2025 to March 2027. His professional activities include conference participation, journal peer review, and invited talks in his specialty areas. He is actively involved with the Centre for Intelligent Infrastructure, where he contributes to research on digital twins and computational methods for infrastructure monitoring and assessment.

Cormac Realeمشاهده پروفایل

مدرس

• Geotechnical Engineering
• Structural Health Monitoring
• Probabilistic Assessment
• +۹ مورد دیگر

Cormac Reale is a Lecturer in the Department of Architecture and Civil Engineering at the University of Bath, where he contributes to the Centre for Sustainable Energy Systems (SES). He joined the university in 2019 after postdoctoral positions at Delft University of Technology and University College Dublin. Education: PhD in Geotechnical Engineering, University College Dublin (2015) Bachelor of Engineering (Civil, First Class Honours), University College Cork (2011) His research focuses on geotechnical risk, infrastructure resilience, and probabilistic assessment of aging systems such as railway slopes and flood embankments. He employs field data, laboratory testing, and stochastic modeling to evaluate degradation and optimize maintenance strategies. His work extends to offshore foundations, particularly for renewable energy applications, and integrates remote sensing techniques like InSAR for infrastructure monitoring. His recent publications reflect a strong trend in applying advanced geotechnical testing (e.g., CPT), probabilistic methods, and remote sensing to assess infrastructure under climate stress. Topics include pile installation modeling, InSAR coherence prediction, and liquefaction risk in flood defences, indicating a focus on predictive modeling and resilience in civil infrastructure. Scientific Contributions: Active editorial board member, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards (since Dec 2024) He leads and collaborates on multiple research grants, including projects funded by The Royal Society, EPSRC, and The British Academy, focusing on offshore wind, storm wave loading, and coal tip safety. He is open to supervising doctoral students in geotechnical risk, unsaturated soils, and climate resilience. His work aligns with UN Sustainable Development Goals related to sustainable infrastructure and climate action. He is affiliated with research initiatives such as the Centre for Sustainable Energy Systems and contributes to national and international collaborations, particularly in the Netherlands and Croatia. His work integrates field-based monitoring with computational modeling to support evidence-based infrastructure management.

Wei Xieمشاهده پروفایل

دانشیار

• Interpretable AI
• IoT
• Computer Simulation
• +۷ مورد دیگر

Wei Xie is an Associate Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on interpretable AI, IoT, and data-driven stochastic optimization for complex systems in biopharmaceutical manufacturing, smart grids, and healthcare. He holds a PhD from Northwestern University (2014) and has received prestigious awards including the NSF CAREER Award (2025) and the INFORMS Simulation Society Outstanding Publication Award. His work bridges simulation, machine learning, and operations research to address real-world challenges in manufacturing, energy, and healthcare. Education: PhD in Industrial Engineering and Management Sciences from Northwestern University (2014). Research Interests: Interpretable AI and hybrid modeling for bioprocesses Blockchain for supply chain risk management Stochastic optimization in renewable energy systems Data integrity and big data analytics Notable Projects: Principal Investigator for NSF-funded research on mechanism-informed AI for biomanufacturing, and NIIMBL grants on mRNA vaccine potency assessment and stem cell culture monitoring. Awards: 2025 NSF CAREER Award 2023 Constantinos Mavroidis Award INFORMS Simulation Society Award (2023) Advising & Grants: Supervised PhD students in areas like stochastic simulation and biomanufacturing. Secured over $800K in federal and industry grants since 2018. Labs/Teams: Active contributor to Northeastern's Institute for Experiential AI and National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL).

• Strength Engineering Methods
• Fatigue Analysis
• Reliability and Robustness in Product Design
• +۳ مورد دیگر

Professor Mårten Olsson holds the Sverker Sjöström Professorship in Reliable Structures at the Royal Institute of Technology (KTH) within the Department of Materials and Structural Mechanics. He has led the department since 2006 and previously held roles such as Director of PhD Education and Head of Solid State Engineering programs. His research focuses on strength engineering methods to enhance product reliability and robustness through advanced fatigue analysis, multiaxial loading models, and probabilistic failure prediction. Olsson has authored over 60 peer-reviewed articles and supervised 14 completed PhD students, contributing to fields like additive manufacturing, engine block dynamics, and composite material behavior. His work integrates computational modeling with experimental validation to address industry challenges in structural reliability and optimization. Research Interests: Olsson’s research spans fatigue analysis, multiaxial loading effects, reliability-based design optimization, and material degradation mechanisms. Notable areas include probabilistic modeling of standing contact fatigue, fracture mechanics in composites, and vibration analysis in engine systems. Key Contributions: His publications highlight advancements in second-order reliability methods, fatigue probability models for complex geometries, and the integration of deformation effects into structural dynamics. Olsson’s methodologies have been applied to aerospace components (e.g., gas turbine blades) and automotive systems (e.g., engine gaskets). Academic Leadership: Beyond research, Olsson has orchestrated educational programs at KTH, including the Master of Science in Engineering Physics, and pioneered competency specialization tracks in Solid State Engineering. His teaching spans undergraduate through doctoral levels, emphasizing practical industrial applications.

• Steel and Composite Systems
• Perforated Beams
• Structural Topology Optimisation
• +۷ مورد دیگر

Konstantinos Daniel Tsavdaridis is a Full Professor in Civil Engineering at City, University of London, leading the Steel, Steel-Concrete Composite and Hybrid Structures group. He holds dual roles as a researcher, educator, and industry consultant, with expertise in structural design, seismic resilience, and sustainable construction. His academic journey includes a PhD from City University London and post-doctoral appointments at Leeds University and City University. Education: BEng in Civil Engineering (City University London, 2005) MSc in Structural Engineering (Imperial College London, 2006) PhD in Civil/Structural Engineering (City University London, 2010) Research focuses on innovative steel and composite systems, including perforated beams, topology optimization, and seismic-resistant design. His group pioneered 3D-printed modular connections and lightweight composite flooring. Key contributions include over 160 peer-reviewed publications and patents in structural engineering. Professional roles: Senior Fellow, Royal Academy of Engineering (2019) Membership: Institution of Civil Engineers (Fellow), Engineering Council (Chartered Engineer), and multiple international engineering societies. Grants and funding: EPSRC grants for modular building resilience and energy-efficient design Royal Academy Fellowship for 3D-printed connections Labs/Teams: Leads the Steel-Composite Structures Group and the 3DMBC (3D-Printed Modular Building Connections) initiative, collaborating with industry and global partners.

• Machine Learning
• Bioinformatics
• Biomedical Applications
• +۶ مورد دیگر

Marc Vaisband is a researcher at the Life and Medical Sciences Institute (LIMES) of the University of Bonn, where he pursues his PhD under Prof. Hasenauer. His research focuses on machine learning applications in biomedical imaging and oncology, supported by funding from the Salzburg Cancer Research Institute. Education B.Sc. and M.Sc. in Mathematics at the University of Bonn Specialized in stochastics and numerical analysis, with theses on KPZ theory and Totally Asymmetric Simple Exclusion Process Research Interests His work bridges machine learning and biomedical challenges, particularly in age-related macular degeneration diagnostics, cancer therapy optimization, and stochastic modeling. He develops neural networks for medical imaging and survival analysis in hematologic malignancies. Scientific Contributions Marc's 15 most recent publications include machine learning-driven AMD detection via fluorescence microscopy, precision dosing in anticancer therapy, and deep learning validation for NGS genetic variants. His articles emphasize statistical modeling, image analysis, and clinical outcome prediction. Professional Context As part of the Hasenauer Lab, Marc contributes to computational medicine projects. His interdisciplinary approach integrates mathematical rigor with clinical applications.

Assaf Talمشاهده پروفایل

استاد

• Neuroimaging
• Magnetic Resonance Spectroscopy
• Brain Disease Monitoring
• +۳ مورد دیگر

Prof. Assaf Tal is a faculty member in the Department of Bio-Medical Engineering at The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University. His research focuses on developing advanced neuroimaging methodologies using magnetic resonance spectroscopy (MRS) and imaging (MRI) to investigate brain function and disease mechanisms. His primary research interests center on neuroimaging physics and brain disease monitoring , with specific emphasis on: Developing novel MRS/MRI techniques combining spin physics and signal processing Tracking neurochemical changes during cognitive processes Detecting and monitoring neurodegenerative diseases including multiple sclerosis, traumatic brain injury, and Alzheimer's Disease Understanding brain encoding mechanisms across neurochemical, electrophysiological, and structural levels His work bridges biomedical engineering with clinical neuroscience to create improved diagnostic and monitoring tools. Prof. Tal's recent publications (2022-2025) demonstrate strong focus on functional MRS and advanced spectral-temporal analysis , with significant contributions to motion correction, uncertainty estimation, and microstructural modeling in neuroimaging. His research shows increasing integration of computational methods like Bayesian inference and machine learning for precision neuroimaging. His laboratory develops specialized software tools including the Visual Display Interface (VDI) for MRS data processing and simulation, supporting both preclinical and clinical neuroimaging research.

• Structural Health Monitoring
• Civil Engineering
• Vibration Analysis
• +۵ مورد دیگر

Dr. Zijie Zeng is a Grant-Funded Researcher at the University of Adelaide , affiliated with the School of Architecture and Civil Engineering under the Faculty of Sciences, Engineering and Technology . He is eligible to supervise Masters and PhD students as a co-supervisor. Research Focus: Vibration-based and guided wave-based Structural Health Monitoring (SHM) of engineering structures Key Areas: System identification, damage detection, probabilistic optimization methods, deep learning techniques for structural anomaly detection His work emphasizes enhancing reliability and accuracy in structural monitoring systems through advanced computational methods.

Yuqi Songمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Mining
• +۳ مورد دیگر

Yuqi Song is an Assistant Professor in the Department of Computer Science at the University of Southern Maine (USM), where she joined in August 2023 after completing her Ph.D. at the University of South Carolina. Her interdisciplinary research bridges machine learning with materials science, tourism, and recommender systems. Her educational background includes: Ph.D. in Computer Science, University of South Carolina (2023), supervised by Dr. Jianjun Hu M.S. and B.S. in Computer Science, Chongqing University, supervised by Dr. Ming Gao Dr. Song's research focuses on applying state-of-the-art deep learning techniques—including generative adversarial networks, graph neural networks, and transformer models—to solve real-world problems. She develops AI-driven solutions for materials discovery (predicting crystal structures and properties) and tourism applications (employee turnover prediction systems). Her work uniquely combines computational methods with domain-specific challenges, emphasizing practical implementation through user-friendly tools like her materials informatics web platform MaterialsAtlas.org. Analysis of her recent publications (2023-2025) reveals three dominant research thrusts: (1) materials informatics using transformer-based generative models for crystal structure prediction, (2) robust recommender systems security against data hybrid attacks, and (3) computer vision innovations in depth estimation and medical image analysis. Her work consistently leverages attention mechanisms and cross-disciplinary data integration. Dr. Song actively mentors graduate students, currently advising Reihaneh Maarefdoust (Complex Learning and Machine Learning) and Zahra JahediBashiz (NLP, Generative AI). She teaches core courses including Software Engineering (COS 430) and Artificial Intelligence (COS 470), emphasizing practical programming skills. Her lab seeks motivated students for projects in materials discovery and tourism analytics. She leads a research group focused on interdisciplinary AI applications, collaborating with materials scientists and hospitality industry partners to develop deployable solutions. Current initiatives include deep learning models for predicting piezoelectric properties and generative design of 2D materials, alongside tools for tourism workforce analytics.

• Computer Science
• Signal and Image Processing
• Remote Sensing
• +۵ مورد دیگر

Christian Germain is a Professor of Computer Science at Bordeaux Sciences Agro, an engineering school specializing in agronomy. He focuses on information technologies and their applications to agriculture and environmental science, conducting research in image analysis at the IMS laboratory. His work spans remote sensing, embedded agricultural imaging, and digital tool development for vineyards. Key Roles: Co-holder of the AgroTIC business chair (29 corporate sponsors), Scientific Director of DigiLab (open platform for wine-growing experiments). Research Themes: Remote sensing, agricultural imaging systems, covariance pooling in machine learning, and texture analysis for material science. His recent publications highlight collaborations with industry and academic partners, emphasizing applications in vineyard health monitoring, carbon composite modeling, and vine disease detection. Germain’s team utilizes CNNs, Gaussian mixture models, and SAR imaging techniques to advance agricultural and materials engineering. He has contributed to international conferences and journals, integrating computational methods with real-world agricultural challenges, including proximal sensing for crop management and 3D microstructure simulation.

MEHMET İSHAK YÜCEمشاهده پروفایل

استاد

• Hydrology
• Fluid Mechanics
• Drought Modeling
• +۴ مورد دیگر

Mehmet İshak Yüce is a Professor in the Department of Hydraulics within the Faculty of Engineering at Gaziantep University. His career spans over 30 years, starting as a Research Assistant in 1993 and achieving professorship in 2021. He holds a PhD in Civil Engineering from the University of Manchester (2005), an MSc in Water Engineering from Istanbul Technical University (1995), and a BSc in Civil Engineering from Middle East Technical University (1992). Research Focus: Dr. Yüce specializes in hydrology, fluid mechanics, and climate impacts on water systems. His work integrates computational modeling, statistical hydrology, and sustainable resource management. Key themes include: Drought prediction using copula models and machine learning Hydrokinetic turbine design for renewable energy Hydraulic transients and pollutant transport Climate-driven hydrological variability in Mediterranean basins Awards & Recognition: Gaziantep University 2015 Second Best Doctoral Thesis Award Academic Leadership: He has supervised 6 doctoral and 32 master's students, focusing on hydrology, energy systems, and hydraulic infrastructure. His grants include projects on hydrokinetic turbines (2012-2022) and drought analysis (2015-2019), funded by national agencies. Administrative Roles: Former Dean of Engineering (2021), Institute Director (2020-2023), and multiple terms as Department Deputy Head.

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.