جست‌وجوی استاد و پژوهشگر

Jayishni Maharajمشاهده پروفایل

پژوهشگر ارشد

• Biomechanics
• Foot Biomechanics
• Musculoskeletal Modeling
• +۵ مورد دیگر

Dr. Jayishni Maharaj is a Postdoctoral Research Fellow at the School of Human Movement and Nutrition Sciences, University of Queensland. Her research integrates biomechanics, rehabilitative sciences, and computer science to explore the relationship between foot structure, function, and injury. She works in the Neuromechanics lab utilizing biplanar X-ray radiography, modeling, and simulation techniques to validate musculoskeletal foot models. At the Children's Research Centre, Dr. Maharaj investigates the effects of Botox on muscle structure and development in children with Cerebral Palsy. Dr. Maharaj's research spans multiple clinical populations including individuals with diabetic peripheral neuropathy, multiple sclerosis, and pediatric movement disorders. Her work employs advanced techniques such as biplanar videoradiography, statistical shape modeling, and wearable sensor technology to investigate foot mechanics and develop innovative rehabilitation approaches. She has made significant contributions to understanding how textured insoles affect balance and gait in neurological conditions, how foot structure relates to joint mechanics, and how advanced modeling techniques can improve clinical interventions. Analysis of Dr. Maharaj's publication record reveals a strong focus on translational biomechanics research with emphasis on foot and ankle function across various populations. Her work bridges engineering principles with clinical applications, particularly in developing interventions for foot-related disorders and improving mobility outcomes. Recent publications demonstrate growing expertise in wearable sensor technology, statistical shape modeling, and applications for both adult and pediatric populations with movement disorders. Dr. Maharaj completed her PhD at the University of Queensland in 2018 with research focused on 'The mechanical function of the subtalar joint and tibialis posterior muscle during walking.' Her doctoral work established the foundation for her current research in foot biomechanics and musculoskeletal modeling. Dr. Maharaj actively contributes to the biomechanics community through initiatives like the Biomechanics Research and Innovation Challenge and International Women in Biomechanics. Her collaborative work spans multiple disciplines including medical imaging, wearable technology, 3D printing for medical applications, and STEM education, demonstrating her commitment to interdisciplinary research and professional development within the field.

• Animal Genomics
• Livestock Conservation
• Dairy Cattle Genetics
• +۴ مورد دیگر

Ina Hulsegge is a Researcher in the Animal Breeding & Genomics group at Wageningen University & Research, specializing in livestock genomics and conservation. Her work focuses on genomic diversity analysis, inbreeding estimation, and precision livestock farming technologies, particularly for dairy cattle conservation. Her research spans Genomic profiling of livestock diversity Development of real-time animal identification systems using computer vision Conservation genomics for Dutch cattle breeds Deep learning applications in genotype data analysis with strong emphasis on practical implementation in breeding programs and gene banks. Recent publications highlight trends in Automated phenotyping through ear-tag recognition (2024) SNP array-based genomic diversity assessment (2024) Aneuploidy detection via deep learning (2023) Sub-population analysis in red-pied cattle (2023) demonstrating her dual focus on computational innovation and conservation applications. Scientific contributions include: Management systems for genomic information in gene banks (2024) EUGENA database updates for multi-species conservation (2024) Analysis of genetic changes in Dutch black-and-white cattle (2022) She actively engages in knowledge transfer through professional presentations at precision livestock farming conferences and collaborates with institutions like CGN (Centre for Genetic Resources, the Netherlands) on conservation initiatives. Her PhD thesis 'Genomics applied to conservation of genetic diversity in Dutch livestock' (2023) established foundational work for national conservation strategies.

Richard Meyesمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr.-Ing. Richard Meyes is a Scientific Researcher and Head of the Research Field 'Interpretable Learning Models' at the Institute for Technologies and Management of Digital Transformation (TMDT), University of Wuppertal. His work bridges artificial intelligence and industrial applications, with a focus on interpretable and practical machine learning systems. His research interests lie at the intersection of Artificial Intelligence, Machine Learning, and Industrial Digital Transformation . He focuses on developing interpretable neural networks , analyzing time series data from industrial sensors , and creating structured representations in deep learning models . These efforts support predictive maintenance, automated inspection, and intelligent decision-making in manufacturing and automotive domains. The recent publications highlight a strong trend in applying deep learning to industrial computer vision , sensor data analytics , and ethical AI . His work spans both technical innovation (e.g., BEV perception, CTR prediction) and societal implications (e.g., AI ethics in research). There is a consistent emphasis on real-world deployment and interpretability. Scientific Awards: No awards listed in the provided text. Advising and Grants: While no specific students or grant projects are listed, Richard Meyes leads a dedicated research field and actively collaborates on high-impact publications, suggesting involvement in funded research initiatives and potential mentorship of junior researchers within the TMDT institute. Labs and Teams: He leads the research field 'Interpretable Learning Models' at the Institute for Technologies and Management of Digital Transformation. This group is part of a larger interdisciplinary team working on digital transformation technologies, likely involving collaborations across AI, engineering, and industrial application domains.

• Medical Image Analysis
• Computer Vision
• Biomedical Imaging
• +۶ مورد دیگر

Constantino Carlos Reyes-Aldasoro is a Professor in the Department of Electrical and Electronic Engineering within the School of Mathematics, Computer Science and Engineering at City, University of London. With an active research career spanning over two decades, he maintains a prolific publication record with recent contributions in 2025 across multiple disciplines including medical image analysis, computer vision, and biomedical engineering. His research interests focus on Medical Image Analysis, Computer Vision, Biomedical Imaging, Cancer Research, and Image Processing. His work bridges engineering and medical sciences, developing computational methods for biomedical applications. He has made significant contributions to tumor microenvironment modeling, vascular analysis, and electropalatography studies. Analysis of his recent publications reveals a strong emphasis on deep learning applications in medical imaging, with particular focus on cancer diagnostics, vascular analysis, and neuroimaging. His research demonstrates consistent methodological innovation in image processing techniques applied to biomedical problems, with publications spanning from fundamental algorithm development to clinical applications. Professor Reyes-Aldasoro has mentored numerous researchers, with several co-authors appearing consistently across multiple publications as likely students and postdoctoral researchers including C. Karabağ, J.A. Solis-Lemus, and M.A. Ortega-Ruiz. His work demonstrates interdisciplinary collaboration across engineering, computer science, medicine, and biology, reflecting the increasingly interconnected nature of modern biomedical research. He maintains active research programs with recent publications indicating ongoing projects in AI implementation in medical imaging, tumor modeling, and cardiovascular analysis.

• Mechanical Engineering
• Materials Science
• Fatigue Analysis
• +۶ مورد دیگر

Raffaella Sesana is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the Polytechnic University of Turin. She is a member of the Interdepartmental Center J-Tech@PoliTO and serves on the Single Guarantee Committee. Her academic career spans multiple teaching levels from bachelor to PhD programs, with specialization in mechanical design, experimental mechanics, and thermographic analysis. Dr. Sesana's research focuses on fatigue behavior, mechanical characterization of materials, nanostructured materials, and thermography applications. Her work bridges theoretical mechanics with practical industrial applications, particularly in non-destructive testing and mechanical design. She has developed significant expertise in active and passive thermography techniques for material characterization and defect detection. Her recent publications demonstrate a strong trend toward applying thermographic methods to solve engineering challenges in aerospace, mechanical design, and materials science. These works show increasing integration of computational methods with experimental techniques, particularly in fatigue analysis, residual stress measurement, and additive manufacturing applications. Her research has clear applications in rocket engine development, bearing technology, and sustainable materials. Start Cup Piemonte and Valle d'Aosta (2024) Fund for the financing of basic research activities FFABR 2017 Dr. Sesana actively supervises numerous PhD students across mechanical and aerospace engineering programs, with current advisees working on topics ranging from liquid rocket engines to thermographic evaluation techniques. She leads multiple research projects including RILEnergy (2023-2024), development of active thermography methodology (2023-2024), and research on new Liquid Rocket Engine cooling systems (2024-2025). Her commercial research spans from 2010 to present, addressing industry challenges in mechanical design, welding, and materials characterization. She is a key member of the ISED: Industrial Systems Engineering and Design research group and leads the Design and Experimentation of Mechanical Drive Components and Thermography group. Her work connects academic research with industrial applications through numerous collaborative agreements with commercial partners across various engineering sectors.

Mustafa Al-Adhamiمشاهده پروفایل

مدرس

• Digital Architecture
• Sustainable Building Design
• Building Information Modeling (BIM)
• +۵ مورد دیگر

Mustafa Al-Adhami serves as Lecturer in Digital Built Environment at Birmingham City University, specializing in digital architecture and sustainability. With over a decade of experience across academia and industry, he teaches architecture and built environment courses at undergraduate and postgraduate levels while leading research in Building Information Modeling (BIM), reality-capturing technologies, and IoT applications. Previously, he held academic positions at the University of Huddersfield and Nottingham Trent University. His research integrates cutting-edge digital tools to address sustainability and efficiency challenges in infrastructure. Key focus areas include VR/AR for construction quality control, machine learning-driven energy prediction frameworks, and automated digitization of critical assets like railway bridges. This work bridges civil engineering, computer science, and sustainable design through practical implementations in real-world built environments. Recent publications reveal strong trends in applying extended reality for infrastructure assessment and real-time building performance monitoring. His 2025 systematic review on buried infrastructure condition assessment demonstrates leadership in synthesizing emerging techniques, while 2020-2022 work on thermal comfort evaluation and railway bridge digitization shows consistent innovation in merging IoT with traditional engineering practices. Dr. Al-Adhami's industry collaborations include KTP research developing semi-automated BIM solutions for railway infrastructure. His professional background spans UK and Middle Eastern AEC firms where he executed design documentation, building surveys, and technical drawing production. Current academic work emphasizes translating industry challenges into pedagogical frameworks for built environment education.

• Geospatial Information
• Remote Sensing
• GIS
• +۴ مورد دیگر

Attila Juhász serves as an Associate Professor within the Department of Photogrammetry and Geoinformatics at the Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). His academic responsibilities include teaching courses such as Digital Surface Modeling, Geospatial Information, Civil Engineering Informatics, and Monitoring and GIS, alongside supervising thesis work through course BMEEOFTTATZ. He maintains active research in geospatial technologies with emphasis on historical applications. His research program centers on applying geospatial information systems and remote sensing to reconstruct historical military operations, particularly World War II events. He develops methodologies for bomb crater detection, fortification mapping, and battlefield analysis using LiDAR and multi-temporal GIS. A significant contribution involves solving temporal data management challenges in historical reconstructions, enabling accurate modeling of evolving military landscapes. His work bridges technical geospatial innovation with humanities scholarship in archaeology and military history. Analysis of his publication record reveals consistent specialization in automated detection of World War II artifacts through LiDAR and spatial data fusion. His research demonstrates progressive technical sophistication from early 2000s case studies to current AI-enhanced object recognition systems. The work establishes critical frameworks for conflict archaeology and has produced notable reconstructions including Budapest defense lines and the Attila-line fortifications. No scientific awards or major fellowships are documented in the available sources. Dr. Juhász supervises thesis research as evidenced by course BMEEOFTTATZ, but specific student names and research grant details remain unreported in the source material. His teaching portfolio indicates extensive mentorship across undergraduate and graduate geospatial curricula. He maintains operational affiliation with the Endre Németh Measurement Station and Education Center, which provides critical infrastructure for geospatial data collection, LiDAR processing, and field-based historical reconstruction projects. This facility supports both his research initiatives and practical student training in advanced surveying techniques.

• Applied IoT
• Edge-Cloud Computing
• Distributed Systems
• +۴ مورد دیگر

Neda Maleki is a Senior Lecturer at the Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, starting in September 2024. Her research focuses on Applied IoT, Edge-Cloud Computing, Distributed Systems (Hadoop/Spark), Artificial Intelligence, and Machine Learning for data analysis. PhD in Computer Engineering (2014–2021), Science and Research Branch of Islamic Azad University, Tehran, Iran Master of Science in Computer Engineering (2009–2013), Ghazvin Islamic Azad University Bachelor of Science in Hardware Engineering (2004–2008), Ghazvin Islamic Azad University Her research explores IoT applications in energy forecasting, environmental conservation, and SME digital transformation. Key contributions include frameworks for power-aware Hadoop acceleration, energy-efficient IoT data formats, and predictive models for fuel consumption and city load forecasting. Recent publications highlight collaborations with industry partners in Sweden and international conferences across Qatar, Italy, Denmark, and the Netherlands. She teaches courses in Data Structures, Algorithms, IoT, and programming at the bachelor's and master's levels. 1DV018: Data Structures and Algorithms 1DV501: Introduction to Programming 4DV119: Applied IoT Competence (Master level) Final Thesis supervision

• Cyber-Physical Systems
• Interval Analysis
• Robotics
• +۵ مورد دیگر

Alexandre Chapoutot is a Lecturer and researcher at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS). He leads the Semantics of Hybrid Systems (SSH) research group and serves as Head of the 2nd year specialization of the engineering cycle in Computer Science. His academic work bridges theoretical computer science with practical engineering applications, focusing on the rigorous analysis and verification of complex systems. Chapoutot's research interests span the verification of cyber-physical systems, with a strong emphasis on interval analysis and mobile robotics. He has made significant contributions to the static analysis of programs through abstract interpretation, particularly for improving the accuracy of floating-point arithmetic calculations. His work bridges theoretical computer science with practical engineering applications, developing methods that ensure the reliability and safety of autonomous systems. The research has direct applications in robotics, control systems, and safety-critical embedded software where numerical precision and system correctness are paramount. His recent publications reveal a strong trend toward the integration of formal methods with practical robotics applications. The work spans theoretical foundations in hybrid systems verification, practical implementations for robotics navigation and control, and innovative approaches to combining different verification techniques. A significant portion of his recent work focuses on Signal Temporal Logic applications, trajectory optimization, and the development of verified algorithms for autonomous systems. The publications demonstrate a consistent progression from theoretical foundations to practical implementations with real-world robotics applications. Chapoutot actively supervises multiple PhD students working on cutting-edge research topics at the intersection of formal methods and robotics. His research group collaborates extensively with other institutions and researchers, as evidenced by the numerous co-authored publications across various domains. He contributes to the development of the DynIbex software library, which provides validated solutions for ordinary differential equations using Runge-Kutta methods. His laboratory work centers around the Semantics of Hybrid Systems (SSH) group, which develops theoretical frameworks and practical tools for the analysis and verification of cyber-physical systems. The group's research spans formal methods, numerical analysis, and robotics, with a strong emphasis on developing mathematically rigorous approaches that can be applied to real-world engineering problems.

Tasnuva Ming Khanمشاهده پروفایل

پژوهشگر

• Paleoecology
• Marine Biology
• Biodiversity Assessment
• +۴ مورد دیگر

Dr. Tasnuva Ming Khan is a Postdoctoral Researcher at the Institute of Paleontology , GeoZentrum Nordbayern , Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , where she focuses on paleoecology and marine biodiversity using interdisciplinary methods. She completed her PhD in Zoology at the University of Cambridge and British Antarctic Survey (expected 2025) and holds an MSc in Geosciences (2021) from FAU. Education: PhD Zoology (expected 2025) – University of Cambridge & British Antarctic Survey MSc Geosciences (2021) – Friedrich-Alexander-Universität Erlangen-Nürnberg BSc Earth Systems Science (2018) – Cornell University Research Interests include scale-dependent drivers of marine community structure, asymmetric range shifts in planktonic foraminifera under the AGELESS project , and the influence of colonial history/global economics on fossil record biases. Her work integrates Bayesian network inference , metacommunity theory , and spatial point process analyses to study modern and fossil marine ecosystems. Key Publications cover topics like deep-sea benthic community dynamics in Antarctica, thermographic studies of humpback whales, and geothermal energy projects at Cornell. Her recent studies (2024–2025) focus on automated detection of Antarctic benthic organisms and environmental heterogeneity impacts on polar ecosystems. Collaborations span institutions including University of Oxford , University of Colorado Boulder , and University of Washington , with affiliations in projects like Deep-time Ecology Group and AGELESS (Leveraging long-term planktonic diversity data for biodiversity protection in areas beyond national jurisdiction).

Tengjiao Jiangمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Structural Health Monitoring
• +۲ مورد دیگر

Tengjiao Jiang is a Researcher at the Department of Structural Engineering , Faculty of Engineering , Norwegian University of Science and Technology (NTNU) . Their work focuses on computer vision and deep learning applications for structural monitoring of transportation infrastructure. Research Interests : Computer Vision (CV) Machine Learning (ML) Optical Measurement Structural Health Monitoring (SHM) Condition Assessment of Transportation Infrastructures Recent Research Trends : Their publications (2020–2025) emphasize computer vision systems for railway and bridge monitoring, including uplift measurement, corrosion detection, displacement analysis, and damage assessment using CNNs and transformers. Key methods involve IMU sensors, line tracking, photogrammetry, and 3D reconstruction. Scientific Awards : Best Paper Award at IMAC-XLII conference (Jan 2024) Olympiad Medal in Olympiad in Engineering Science (May 2023) Supervision : Co-supervises PhD student Ziyue Lu (2022–present) and has co-supervised multiple Master’s students on topics like vision-based bridge inspection, modal identification, and vibration response analysis. Labs & Outreach : Conducts field tests on the Oslo Airport Line with vision-based monitoring systems.

• Unmanned Aerial Vehicles (UAVs)
• Non-Destructive Testing
• Sensor Technology
• +۴ مورد دیگر

Dr. Dayi Zhang is a postdoctoral researcher in the Department of Electronic and Electrical Engineering at the University of Strathclyde. He holds a PhD in Autonomous Unmanned Aerial Vehicle for Non-Destructive Testing (awarded May 2019) and contributes to the UN Sustainable Development Goals, particularly Quality Education and Industry Innovation. As a topical advisory panel member for Drones Journal (since 2024), he bridges UAV technology with industrial applications. Research interests focus on unmanned aerial vehicles for non-destructive testing , with expertise in ultrasonic inspection , eddy current sensing , and force-torque sensor integration . His work addresses challenges in pipeline monitoring , metallic structure assessment , and autonomous robotic positioning , often combining sensor fusion and remote sensing techniques. Key research areas include UAV-deployed sensor systems Structural health monitoring Electromagnetic acoustic resonance Capacitive and pressure sensor design Photogrammetric and LiDAR-based localization Scientific recognition includes the 2024 IEEE TIM Outstanding Reviewer and Innovator of the Future (2025) awards. Collaborations span nuclear facilities, aerospace, and manufacturing domains, with ongoing editorial contributions to Discover Sensors and Drones journals.

Kristina Mastanjevićمشاهده پروفایل

دانشیار

• Food Safety
• Analytical Chemistry
• Brewing Technology
• +۵ مورد دیگر

Kristina Mastanjević serves as an Associate Professor of Biotechnology at the University of Osijek's Faculty of Food Technology, Croatia. Her academic profile centers on food safety research within Biotechnical sciences, with student consultations held Mondays 9-11 AM in office 57/18 at Franje Kuhača 18, Osijek. She maintains active research engagement through publications in high-impact journals while teaching core food science disciplines. Her research program critically examines hazardous compounds in traditional Croatian food systems, specializing in polycyclic aromatic hydrocarbons (PAHs) in smoked meats (Slavonska slanina, Buđola) and malt, alongside mycotoxin contamination in brewing by-products. She pioneers analytical methodologies including GC-MS coupling with sensory analysis and computer vision systems for beer quality assessment. Additional focus areas encompass malting technology for alternative grains, brewing waste valorization, and temperature-dependent sensory properties of craft beer under Mediterranean conditions. Analysis of her 2020-2023 publications reveals consistent investigation of regional food safety challenges, particularly PAH formation mechanisms during traditional smoking processes and mycotoxin transfer through malting/brewing. Her work bridges analytical chemistry with practical food engineering solutions, addressing regulatory compliance gaps for contaminants in artisanal production while developing objective quality assessment tools through digital imaging. No scientific awards or honors were documented in the provided materials. The available information does not specify graduate student advisement activities, research grant acquisitions, laboratory facilities, or collaborative research teams associated with Dr. Mastanjević's work.

• Photogrammetry
• Remote Sensing
• Measurement Data Analysis
• +۲ مورد دیگر

Fatemeh Alidoost is a Lecturer at the Department of Photogrammetry and Geoinformatics at HFT Stuttgart. Her teaching areas include Photogrammetry, Remote Sensing, Measurement Data Analysis, Computer Vision, and Deep Learning. Her research focuses on 3D reconstruction techniques, deep learning applications in geospatial data analysis, and UAV-based photogrammetry for infrastructure and urban mapping. She has contributed to advancements in automated building detection, bridge modeling, and tunnel inspection systems. Her recent publications emphasize integrating convolutional neural networks with photogrammetric data for damage detection, semantic segmentation of point clouds, and multiscale analysis of remote sensing imagery.

Tatiana Podladchikovaمشاهده پروفایل

دانشیار

• Solar Physics
• Space Weather
• Coronal Mass Ejections
• +۵ مورد دیگر

Dr. Tatiana Podladchikova serves as Associate Professor, Director of the Center for Digital Engineering, and Director of the Engineering Systems Master's Program at Skolkovo Institute of Science and Technology (Skoltech). Her work bridges fundamental solar physics research with operational space weather services to protect critical infrastructure including satellites, aviation systems, and power grids from solar-induced geomagnetic disturbances. Her research centers on solar activity dynamics—particularly solar cycles, eruptive flares, coronal mass ejections (CMEs), and high-speed solar wind streams—and their terrestrial impacts. She develops predictive models for space weather phenomena using advanced data analysis techniques, with recent work integrating deep learning frameworks for solar observation translation and physics-informed neural networks for magnetic field extrapolation. Her research directly addresses infrastructure vulnerabilities during extreme space weather events through operational forecasting systems. Dr. Podladchikova's publications demonstrate a clear evolution toward AI-enhanced space weather prediction, combining multi-point solar observations (including Solar Orbiter mission data), coronal dimming analysis for early CME detection, and innovative applications like the DIRECD method for real-time propagation tracking. Her work spans fundamental solar physics, engineering applications for satellite systems, and environmental monitoring techniques. Her scientific recognition includes: International Alexander Chizhevsky Medal for Space Weather and Space Climate (2015, Belgium) Skoltech Best Professor Award (2018-2020) Outstanding Achievements in Education and Research (2022-2023) As an educator, she directs Skoltech's Engineering Systems Master's Program, leads the Space Weather Laboratory, and teaches Experimental Data Analysis and Data Analysis for Space Weather courses. Her leadership extends to public outreach initiatives including science communication events like 'Mysteries of the Sun: Space Weather of the 21st Century' lectures. She heads the Skoltech Space Weather Laboratory where her team develops operational forecasting tools, analyzes multi-spacecraft solar observations, and advances CubeSat technologies including the Skoltech-F mission. Current projects focus on real-time geomagnetic storm prediction systems and AI-driven translation of historical solar datasets to maintain observational continuity.