جست‌وجوی استاد و پژوهشگر

• Highway Engineering
• Asphalt Materials
• Pavement Design
• +۵ مورد دیگر

Markus Oeser is a full professor at the Chair and Institute of Highway Engineering, affiliated with the Federal Highway Research Institute (BASt). His research advances pavement technology, smart infrastructure, and sustainable materials through computational modeling and experimental characterization. Research Focus His work spans: Advanced asphalt materials (self-healing, inductive, nano-enhanced) Digital twins for real-time pavement monitoring Traffic flow modeling and AI-driven infrastructure management Sustainable recycling of construction waste Piezoresistive sensors for smart roads Publication Trends (2024-2025) Recent articles emphasize multi-scale modeling (atomic to structural levels), integration of AI/ML in pavement systems, and sustainability-driven material innovations. Dominant themes include nanomaterial-enhanced composites, digital twins for infrastructure, and traffic microsimulation. Affiliations & Infrastructure Leads research at BASt's highway engineering facilities, leveraging laboratories for materials testing, computational modeling, and full-scale pavement validation.

Heinrich Wörtcheمشاهده پروفایل

استاد

• Sensor Technology
• Biomechanics
• Nuclear Physics
• +۳ مورد دیگر

Heinrich Wörtche is a Professor at the Faculty of Medical Sciences/UMCG (University of Groningen) and holds professorships at Hanze University of Applied Sciences (Sensors and Smart Systems, since 2018) and Eindhoven University of Technology (Miniature Wireless Explorative Sensor Systems, since 2015). His work spans interdisciplinary domains including sensor technology, nuclear physics, and biomechanics. Professor, Sensors and Smart Systems, Hanze University of Applied Sciences (2018–present) Professor, Miniature Wireless Explorative Sensor Systems, Eindhoven University of Technology (2015–present) His research focuses on sensor systems for health monitoring, nuclear structure analysis (e.g., neutron-skin modes, isospin properties), and biomechanics. Key applications include dementia care, exergaming, and postural control assessment. Recent publications highlight his work on wireless sensor systems for dementia behaviors (2024), nuclear resonance studies (2016), and biomechanical movement analysis using machine learning (2015). Keywords span Gerontechnology , Nuclear Physics , Biomechanics , and Data Analytics . Collaborations include institutions like Hanze University of Applied Sciences, Eindhoven University of Technology, and the University of Groningen. Research outputs total 96, with 2014–2024 active contributions in physics, sensor technology, and health informatics.

Kathrin Buschمشاهده پروفایل

پژوهشگر

• Biological Oceanography
• Deep-Sea Ecology
• Microbial Communities
• +۵ مورد دیگر

Kathrin Busch is a biological oceanographer conducting systems-oriented and interdisciplinary environmental research focused on cold and deep marine environments. Her work integrates ecological perspectives with advanced methodological approaches to study microbial communities within "deep-sea forests" formed by sponges and coral reefs. Research Interests: Ecosystem dynamics across spatial-temporal scales Matter fluxes and nutrient cycling Biodiversity assessments of marine microbiomes Abiotic-biotic interactions in extreme environments Biological networks and feedback mechanisms Connectivity in deep-sea ecosystems Methodological Expertise: In situ observational and experimental approaches Molecular ecology techniques Big data management and bioinformatics pipelines Integrative modeling frameworks Advanced data visualization "Digital ocean" technologies

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Kaiyan Yuمشاهده پروفایل

دانشیار

• Autonomous Robotic Systems
• Control Systems
• Mechatronics
• +۴ مورد دیگر

Kaiyan Yu is an Associate Professor in the Mechanical Engineering Department at Binghamton University. She holds a BS from Nankai University (2010) and a PhD from Rutgers University (2017). Her research focuses on autonomous robotic systems, mechatronics, and motion control with applications to nano/micro-particle manipulation, biomedical systems, and Lab-on-a-Chip technologies. She leads the Automated Control Systems and Robotics Lab and has been recognized with an NSF CAREER Award for her work in nanobot research. Education: BS in Intelligent Science and Technology, Nankai University (2010) PhD in Mechanical and Aerospace Engineering, Rutgers University (2017) Research Interests: Autonomous robotic systems Electrophoresis-based micro/nano manipulation Dynamic systems and control Motion planning and control Biomedical engineering applications Her publications emphasize nanowire control in fluid suspensions, adaptive control strategies, and robotic systems for civil infrastructure. Recent work includes physics-informed neural networks for vehicle dynamics and ensemble control for nanomanipulation. She actively collaborates on NSF-funded projects and has pioneered techniques for 3D pose identification of micro/nanoparticles under bright-field microscopy. Awards: NSF CAREER Award (2024) Her lab develops advanced control systems for biomedical and industrial applications, focusing on precision manipulation at micro/nano scales. Current projects involve autonomous crack-filling robots for infrastructure maintenance and novel electrophoresis-based microfluidic platforms.

Andriy Miranskyyمشاهده پروفایل

دانشیار

• Applied Machine Learning
• Big Data
• Blockchain
• +۴ مورد دیگر

Dr. Andriy Miranskyy is an Associate Professor in the Department of Computer Science at Toronto Metropolitan University. His research focuses on applied machine learning, quantum computing, cloud computing, and software engineering, with notable contributions to anomaly detection in cloud systems and quantum software engineering. He leads the AMiR Lab, exploring risk mitigation and software development challenges in emerging technologies. Education: Ph.D. in Computer Science from The University of Western Ontario (2011). Research Interests: He investigates quantum software engineering methodologies, cloud-native systems governance, and big data applications. His work bridges theoretical advancements with industrial-scale implementations, such as IBM Db2 quantum safety case studies and cloud monitoring tools like CloudHeatMap. He emphasizes practical solutions for flakiness detection in quantum programs and sustainable software development practices. Awards: Recognitions include the Rogers Cybersecure Catalyst Fellowship (2023-2024), IBM CAS Best Project (2021), and a Guinness World Record for pioneering a 3Pb data warehouse (IBM DB2 team). Teaching: Teaches courses like CPS 840 (Quantum Computing), CPS 847 (Software Tools for Startups), and CPS 731 (Software Engineering). Labs/Teams: Directs the AMiR Lab, focusing on risk-aware software engineering in quantum and cloud domains.

• Cardiology
• Cardiovascular Medicine
• Interventional Cardiology
• +۳ مورد دیگر

Dr. Jonathan Ciofani is a Clinical Associate Lecturer at the Northern Clinical School within the Faculty of Medicine and Health at the University of Sydney. His research focuses on cardiovascular medicine, with a particular emphasis on aortic stenosis, interventional cardiology, and the application of statistical methods like Mendelian randomization in understanding disease mechanisms. He has published extensively on topics including lipid-lowering therapies for aortic valve disease, machine learning for risk prediction in STEMI patients, and the interplay between calcific aortic stenosis and atrioventricular conduction abnormalities. His work frequently integrates computational modeling of coronary hemodynamics and explores bidirectional relationships between cardiovascular diseases and comorbidities such as renal dysfunction. Recent studies highlight his interest in novel therapies and genetic epidemiology, particularly in East Asian populations. Dr. Ciofani’s research also extends to vaccine-related myocardial injury and cardiac complications in hospitalized patients with severe infections like SARS-CoV-2. Publications span high-impact journals such as the Annals of Thoracic Surgery , Journal of Clinical Medicine , and Internal Medicine Journal . His articles often combine observational studies with advanced analytic techniques to address clinical questions in cardiology. Notably, he has contributed to the development of decision-making algorithms for transcatheter aortic valve replacement and coronary flow assessment.

• Construction Engineering and Management
• Data Analytics
• Artificial Intelligence (AI)
• +۶ مورد دیگر

Professor Nora El-Gohary is a faculty member in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. She holds the title of CEE Excellence Faculty Fellow and has held academic positions including Assistant Professor (2009–2017), Associate Professor (2017–2023), and full Professor (2023–present). Her professional experience includes roles at the University of Manitoba and Hyundai Engineering & Construction Co. Ltd. Education: B.Sc. Construction Engineering, American University in Cairo (1999) M.Sc. Construction Engineering, American University in Cairo (2002) Ph.D. Civil Engineering, University of Toronto (2008) Research focuses on data analytics, AI, and BIM for sustainable infrastructure. Key areas include: Automated compliance checking using NLP Machine learning for energy consumption prediction Human-centered systems for construction management Awards include the NSF CAREER Award (2013), NCSA Fellow (2018), and 2025 ASCE Computing in Civil Engineering Award. She has led over 100 research projects funded by agencies like NSF and Illinois Department of Transportation. Editorial roles include Co-Editor-in-Chief of the ASCE Journal of Computing in Civil Engineering (2024–present) and Associate Editor (2012–2023). Active in professional societies such as TRB and ASCE.

• Structural Engineering
• Seismic Engineering
• Hazard Mitigation
• +۴ مورد دیگر

James M. LaFave is a Professor of Civil Engineering at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering and the Department of Civil & Environmental Engineering (CEE). He holds the title of CEE Excellence Faculty Scholar and currently serves as Co-Chair of the Illinois CEE Structures Group (since 2023). Prior roles include Associate Dean for Facilities & Capital Planning (2020-21), Director of CEE Education Programs for the Zhejiang University-UIUC Joint Institute (2018-20), and CEE Associate Head (2016-20). He has also held visiting professorships at the University of Washington (2007-08) and served as a NIST Associate (2015). Education: B.S. (1986) and M.S. (1987) in Civil Engineering from UIUC; Ph.D. (1997) in Civil (Structural) Engineering from the University of Michigan. Professional experience includes consulting roles at Wiss, Janney, Elstner Associates and Sargent & Lundy Engineers, and licensure as a Professional Engineer in Michigan. Research focuses on structural connections/joints, seismic and wind hazard mitigation, bridge engineering, and high-performance concrete. Notable projects include studies on integral abutment bridges, concrete connection behavior, and machine learning applications in structural assessment. Research Highlights: Integral abutment bridge dynamics, seismic retrofit strategies, and masonry system performance. Editorial Roles: Founding Co-Editor-in-Chief of Journal of Building Engineering ; Editorial Board member of Engineering Structures . Awards: ACI Wason Medal (2009), Martin P. Korn Award (2023), and multiple teaching honors. Teaching responsibilities include structural design courses for undergraduate and graduate students, alongside advising the UIUC CEE Steel Bridge Team. Professional service includes leadership roles in ACI committees (e.g., Joint Committee 352 on concrete joints) and editorial judging for engineering awards.

• Structural Health Monitoring
• Wind Energy
• Renewable Energy Systems
• +۳ مورد دیگر

Dr. Wei Jiang is a Research Associate in Mid-Infrared Lasers and Detectors at the School of Electrical and Electronic Engineering, University of Sheffield. His work focuses on advancing population-based structural health monitoring (SHM) techniques, particularly for wind energy systems. He specializes in integrating spatial autoregressive models, sensor networks, and environmental data analysis to optimize wind farm performance and detect structural anomalies. Research interests include: Development of advanced SHM frameworks for large-scale infrastructure Data-driven approaches for wind farm wake field prediction and optimization Environmental mapping using Eov fields for infrastructure longevity Integration of machine learning in structural anomaly detection Recent publications emphasize automated structure selection, spatial modeling for turbine performance, and database systems for SHM networks. His work bridges electrical engineering principles with renewable energy systems, contributing to sustainable energy infrastructure advancements. No scientific awards or grants are explicitly listed in the provided information. Dr. Jiang collaborates on interdisciplinary projects involving sensor technology, environmental engineering, and data science applications.

Caspar A.S. Pouwمشاهده پروفایل

پژوهشگر ارشد

• Pedestrian Dynamics
• Crowd Flow Modeling
• Fluid Dynamics
• +۳ مورد دیگر

Caspar A.S. Pouw is a Research Fellow in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He holds a dual role as a Postdoc researcher and Data Scientist at ProRail. His work focuses on advancing human crowd flow monitoring, modeling, and nudging technologies, particularly within the HTCrowd project. Pouw’s research integrates fluid dynamics principles to analyze pedestrian behavior in crowded environments, aiming to enhance safety and efficiency in urban spaces. Educated at TU/e, he earned his Master’s in Applied Physics (specializing in Fluids and Flows) and a Bachelor’s in Combustion Science. He has taught courses on sociophysics, covering crowd dynamics analysis, modeling, and nudging strategies. His contributions align with UN Sustainable Development Goals related to safe cities and resilient infrastructure. Recent research emphasizes data-driven modeling of pedestrian dynamics, psychological influences on train boarding efficiency, and real-time monitoring systems. His work bridges physics, computer science, and urban planning, with applications in transportation and public safety. Collaborations include ProRail and interdisciplinary teams at TU/e. Pouw’s datasets and software tools, such as those for pedestrian trajectory analysis, are openly available. His media coverage highlights innovations in crowd management post-COVID-19. Future work involves expanding predictive models for crowd behavior and optimizing transport infrastructure design.

Christina Boucherمشاهده پروفایل

دانشیار

• Bioinformatics
• Antimicrobial Resistance
• Metagenomics
• +۵ مورد دیگر

Dr. Christina Boucher is an Associate Professor in the Department of Computer and Information Science and Engineering at the University of Florida. Her research focuses on bioinformatics, with a particular emphasis on developing algorithms and software for genomic data analysis, antimicrobial resistance monitoring, and metagenomics. She is known for her contributions to succinct data structures and alignment methods, and her work on tools like MEGARes and AMRPlusPlus has significantly impacted antibiotic resistance classification. Dr. Boucher holds a Ph.D. from the University of Waterloo, where she was supported by prestigious awards including the Google Anita Borg Memorial Scholarship and NSERC Fellowships. Her research interests span computational genomics, algorithm design, and software development for high-throughput sequencing. She has pioneered methods for pangenome indexing, long-read haplotype reconstruction, and portable metagenomics analysis. Dr. Boucher actively promotes diversity in bioinformatics through her roles on committees such as the NSF Research Traineeships Program advisory board and the University of Florida’s Implicit Bias committee. Key Awards: ESA 2016 Best Paper Award, NSERC Doctoral Award, Google Anita Borg Memorial Scholarship Software Contributions: MEGARes, AMRPlusPlus, METAMarc, Moni Professional Roles: NIH BDMA Study Section Standing Member, Board Member of SIG BIO Dr. Boucher’s publications reflect a blend of algorithmic innovation and applied genomic research. Recent work addresses challenges in portable sequencing device security, resistome-mobilome colocalization detection, and high-accuracy assembly polishing. Her interdisciplinary collaborations span microbiology, veterinary medicine, and clinical sciences, underscoring the translational impact of her computational approaches. Her teaching and mentorship efforts focus on curriculum development and fostering inclusive environments in STEM. She has been a key figure in advancing computational tools for antimicrobial resistance monitoring and has contributed to NIH-funded initiatives aimed at improving antibiotic therapy through causal modeling techniques.

Richard Loenderslootمشاهده پروفایل

دانشیار

• Structural Health Monitoring
• Composite Materials
• Vibration Analysis
• +۳ مورد دیگر

Richard Loendersloot is an Associate Professor specializing in dynamics-based maintenance, focusing on structural integrity diagnostics and prognostics. His work integrates advanced sensors and data-driven methods for predictive maintenance in composite materials and infrastructure systems. Research interests include structural health monitoring (SHM), vibration analysis, and damage accumulation modeling. He has contributed to methodologies like Bayesian filtering for prognostics and non-collinear wave mixing for material aging assessment. Recent publications (2024-2025) emphasize sensor technology comparisons, ML-assisted frameworks for cyber-physical systems, and scaled bridge testing for vehicle-bridge interaction models. He collaborates widely, with activities including invited talks on smart maintenance and presentations at international conferences. Supervised 13 academic works, though student names are not listed. Labs/Teams: Part of dynamics-based maintenance research groups, contributing to datasets like motor current/vibration monitoring for e-motor-driven pumps.

Hung Laمشاهده پروفایل

دانشیار

• Robotics
• Computer Vision
• Control Systems
• +۲ مورد دیگر

Hung La is an Associate Professor in the Department of Computer Science and Engineering at the University of Nevada, Reno's College of Engineering. He directs the Advanced Robotics and Automation Lab, focusing on robotic systems for infrastructure inspection (culverts, bridges), autonomous navigation, and control systems. His research integrates computer vision, machine learning, and multi-agent systems to develop field-deployable robotic solutions. La's publications demonstrate expertise in robotic perception (3D registration, SLAM), control systems (quadrotors, climbing robots), and secure machine learning. Recent work emphasizes practical applications in civil infrastructure inspection, warehouse automation, and medical robotics. His research consistently combines theoretical advances with hardware implementations for real-world deployment.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۳ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.