جست‌وجوی استاد و پژوهشگر

Yuejian Chenمشاهده پروفایل

استادیار

• Dynamic Modelling
• Vibration Signal Processing
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Yuejian Chen is an Assistant Professor in the Department of Mechanical Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on dynamic modelling, vibration signal processing, artificial intelligence, and prognosis/health management for complex systems like railway vehicles, wind turbines, and steam turbines. He holds a PhD from the University of Alberta (2020) and was a postdoctoral fellow there until 2021. Prior to joining the University of Manitoba in 2024, he served as an Assistant Professor at Tongji University's Institute of Rail Transit in China from 2021 to 2024. His research integrates digital twins and advanced health management methodologies to ensure safe and cost-effective operation of mechanical systems. Key areas include non-stationary vibration analysis, fault detection via AI (e.g., LSTM networks), and physics-informed machine learning. He has authored/co-authored over 60 publications and serves on editorial boards for journals such as IEEE Sensors Journal , IEEE Transactions on Instrumentation and Measurement , and Smart and Resilient Transportation . Dr. Chen's work bridges theoretical advancements with practical applications, emphasizing railway transportation and energy systems. His recent publications (2021–2025) highlight contributions to gearbox fault detection under variable speed conditions, multi-camera metro monitoring systems, and data-driven load identification strategies. His research trends emphasize fusion of physical principles with AI for robust diagnostics and health management across mechanical systems. He is actively recruiting graduate students interested in dynamic systems, signal processing, and AI applications in mechanical engineering. His research group collaborates with industry partners to address real-world challenges in machinery safety and efficiency.

• Production Engineering
• Industrial Engineering
• Materials Science
• +۵ مورد دیگر

Dragan Adamović is a Professor at the Department of Production Engineering, Faculty of Engineering, University of Kragujevac. His work focuses on manufacturing processes, materials science, tribology, and biomaterials. He has extensive experience in numerical modeling of metal forming processes like ironing and deep drawing, with a strong emphasis on optimizing mechanical properties through thermal treatments and surface treatments such as shot peening. His research integrates computational methods (e.g., FEA) with experimental validation, addressing challenges in automotive engineering, biomedical applications, and industrial safety. Notable contributions include studies on friction coefficient determination in forming processes, wear behavior of alloyed steels, and the development of biodegradable metallic implants. He also investigates occupational safety in industries like concrete production and bakery environments. Adamović has authored over 50 peer-reviewed articles since 2003, covering topics from welding technologies to additive manufacturing applications. His work bridges theoretical material science with practical industrial solutions, emphasizing sustainability and process efficiency in modern production systems.

Stavroulakis Georgiosمشاهده پروفایل

استاد

• Smart Structures
• Structural Engineering
• Vibration Control
• +۷ مورد دیگر

Stavroulakis Georgios is a Professor at the School of Production Engineering and Management, Technical University of Crete. His research focuses on smart structures, vibration control, finite element methods, and advanced materials. He leads interdisciplinary projects in structural mechanics, acoustics, and computational mechanics with applications in engineering systems and heritage preservation. Key areas include: (1) Development of robust control systems for smart structures, (2) Numerical modeling of masonry and composite materials, (3) Applications of artificial intelligence in structural analysis and material science. Office: Δ5.109, DPEM Building. Research activities emphasize: Active vibration suppression using piezoelectric systems and auxetic materials Advanced finite element analysis for biomedical and historical structures Data-driven computational methods for material characterization Acoustic comfort optimization in urban environments Structural health monitoring through physics-informed neural networks His work bridges traditional engineering disciplines with modern AI tools, addressing challenges in infrastructure resilience and sustainable design. Publications span smart materials innovation, nonlinear mechanics, and heritage building restoration.

Hubert Brücklمشاهده پروفایل

استاد

• Thin Films
• Magnetism
• Sensors
• +۳ مورد دیگر

Hubert Brückl is a Professor and Head of the Department for Integrated Sensor Systems at the University for Continuing Education Krems. He holds a PhD and habilitation in Physics from the University of Regensburg and has held key roles at institutions like the Technical University of Darmstadt, IFW Dresden, and the AIT Austrian Institute of Technology. His research focuses on thin films, magnetism, sensors, and micro/nanotechnology, with significant contributions to machine learning applications in materials science. Brückl's scientific career includes leadership in over 15 funded research projects, such as the development of AI-based corrosion monitoring systems and advanced magnetic sensors. He has authored numerous publications in high-impact journals, including Nature Scientific Reports , Physical Review Applied , and IEEE Transactions on Magnetics . His work spans interdisciplinary areas, combining physics, engineering, and data science to create innovative sensor technologies. Current projects emphasize AI-driven predictive modeling of materials properties and high-precision magnetic sensor fabrication. Brückl's lab integrates cutting-edge facilities for nanotechnology and sensor development, collaborating with industry partners like Siemens and aerospace firms. His research addresses challenges in sustainable materials, biomedical diagnostics, and industrial monitoring systems.

Richard P. Martinمشاهده پروفایل

دانشیار

• Wireless Networks
• Sensor Networks
• Network Security
• +۵ مورد دیگر

Richard P. Martin is an Associate Professor and Associate Chair in the Department of Computer Science at Rutgers University's School of Arts and Sciences. His research focuses on wireless networks, sensor systems, and network security applications, with particular emphasis on practical implementations that address real-world challenges in healthcare monitoring, energy efficiency, and network performance. He maintains an active research group working closely with WINLAB (Wireless Information Network Laboratory) and has numerous ongoing collaborations with industry and academic partners. Dr. Martin's research interests span several interconnected domains including wireless communication systems, sensor network design, network security, localization technologies, and energy-efficient computing. His work bridges theoretical computer science with practical engineering applications, often focusing on how wireless technologies can solve problems in healthcare monitoring, particularly medication adherence systems. His research group develops innovative solutions that combine hardware and software approaches to address challenges in wireless sensing, network security, and system optimization. The team's work often involves creating novel protocols and architectures that improve performance while maintaining security and privacy constraints. His recent publication trends show increasing focus on healthcare applications of wireless technology, particularly medication adherence monitoring systems using smart pill bottles and other unobtrusive sensing approaches. He has also maintained a strong research thread in wireless network security, localization systems, and energy-efficient computing. His work often combines machine learning techniques with traditional networking approaches to create more intelligent and adaptive systems. The interdisciplinary nature of his research spans computer science, electrical engineering, and healthcare technology domains. Dr. Martin has advised numerous graduate students who have co-authored publications with him, including Murtadha Aldeer, Mohammadreza Soltaniyeh, and Jorge Ortiz. His research has been supported by various funding sources that enable the development of practical wireless systems and sensor networks. He teaches courses in computer systems and structures, sharing his expertise in networked systems with undergraduate and graduate students. His laboratory work centers around WINLAB, where his team develops and tests wireless and sensor network technologies. The lab focuses on creating practical implementations of theoretical concepts, often building custom hardware and software solutions to address specific challenges in wireless communications and sensing. Current projects emphasize healthcare applications, energy efficiency, and security in wireless systems.

Pedro Henriquesمشاهده پروفایل

استاد

• Programming Languages
• Compiler Design
• Software Analysis
• +۲ مورد دیگر

Pedro Henriques is Professor of Computer Science at University of Minho, where he coordinates the Language Processing group at Algoritmi Research Center. With a PhD in Formal Languages and Attribute Grammars, he teaches compiler design and programming language engineering. His research develops: Formal methods for software analysis Educational tools for programming pedagogy Ontology-driven computational thinking frameworks AI applications in agriculture and healthcare Recent work includes neuroeducation-informed frameworks (OntoCnE) and Programming Cocktails methodology for optimizing cognitive load in code instruction. He has supervised 14 PhD dissertations and authored the foundational text "XML & XSL: da teoria a prática". Current EU projects explore VR cognitive rehabilitation and olive cultivar identification using deep learning.

• Agricultural Engineering
• Precision Agriculture
• Remote Sensing
• +۷ مورد دیگر

Troy Jensen is an Associate Professor at the School of Agriculture and Environmental Science , University of Southern Queensland. He is affiliated with the Centre for Agricultural Engineering and held a Fulbright Scholar position funded by the Kinghorn Foundation in 2023, alongside a Visiting Professorship at the University of Florida during the same year. Education: He holds a BEng (1983), MEng (1991), and PhD (2009) from the University of Southern Queensland. Research Interests: His expertise spans agricultural machinery, precision agriculture, remote sensing, controlled traffic farming, UAVs, and technology innovation . Specific focuses include sugarcane yield monitoring, soil tillage mechanics, and energy-efficient farming practices. He has pioneered work in UAV-based crop monitoring, discrete element modeling for soil analysis, and sustainable machinery design. Grants & Projects: Notable projects include: Australian Government’s Site-Specific Tools for Herbicide Management (2023) Cotton Seed Distributors’ Cotton Picker Extensions (2023) New Columbo Mobility Plan for Thailand-Australia engineering collaboration (2022) Supervision: He currently supervises doctoral students on topics like sugarcane replanting automation and pest monitoring technologies. Completed projects include studies on nutrient management in Nicaraguan sugarcane and soil compaction from cotton harvesting. Labs/Teams: His work is embedded within interdisciplinary teams at the Centre for Agricultural Engineering, focusing on mechanization, precision agriculture, and sustainable farming systems.

Mostafa Elseifiمشاهده پروفایل

دانشیار

• Pavement Engineering
• Asphalt Materials Science
• Transportation Engineering
• +۳ مورد دیگر

Mostafa Elseifi is the Occidental Chemical Corporation Distinguished Professor at Louisiana State University (LSU), affiliated with the Department of Civil & Environmental Engineering in the College of Engineering. He holds roles as the East Baton Rouge (EBR) Parish Engineer and Surveyor Selection Board member and the CEE Junior Faculty Mentor. His educational background includes a Ph.D. from Virginia Polytechnic Institute (2003), an M.S. (1999), and a B.S. (1996) in Civil Engineering from Cairo University. A licensed Professional Engineer in Virginia and a Fellow of the American Society of Civil Engineers (ASCE), Elseifi specializes in pavement engineering, particularly in asphalt materials, pavement instrumentation, and tire-pavement interaction. His research focuses on constitutive modeling of flexible pavements, laboratory characterization of hot-mix asphalt, and mitigation strategies for cracking and moisture damage. Key contributions include developing machine learning frameworks for pavement performance prediction and evaluating emerging additives like graphene nanoplatelets. Notable awards include the 2019 ASCE Journal of Transportation Engineering Editor’s Choice Award and the 2013 Michael R. Mangham Teaching Award. Elseifi’s work integrates computational methods (e.g., finite element modeling) with field studies to address infrastructure challenges. He has pioneered studies on E-scooter impacts, recycled materials in asphalt mixtures, and cost-effective preventive maintenance strategies in Louisiana’s hot and humid climate. His research also explores sustainable practices, such as superhydrophobic asphalt mixtures and self-healing mechanisms via induction heating. He has advised numerous projects funded by state and federal agencies, focusing on pavement management systems, structural health monitoring, and interlayer bonding. His contributions span over 100 peer-reviewed articles and have directly influenced Louisiana’s pavement maintenance guidelines. Current research includes optimizing chip seal applications and evaluating the environmental and economic impacts of microsurfacing.

Katrina Lythgoeمشاهده پروفایل

دانشیار

• Virology
• Epidemiology
• Evolutionary Biology
• +۳ مورد دیگر

Dr. Katrina Lythgoe is an Associate Professor in the Department of Biology at the University of Oxford and a Tutorial Fellow at Brasenose College. She holds roles as Group Leader at the Big Data Institute and Investigator at the Pandemic Sciences Institute. Her academic journey includes a BA from Oxford, MSc from Imperial College London, and a PhD in population genetics from Edinburgh. Lythgoe’s research focuses on viral evolution and epidemiology, particularly HIV, Hepatitis B/C, and SARS-CoV-2. She leads projects integrating population genetics, modeling, and genomic surveillance to understand viral adaptation and transmission dynamics. Roles: Associate Professor, Tutorial Fellow, Group Leader (Big Data Institute), Pandemic Sciences Institute Investigator Educations: BA (Oxford), MSc (Imperial), PhD (Edinburgh) Research interests include the evolution of chronic viral infections, within-host population dynamics, and genomic surveillance strategies. Key contributions include work on the ONS Covid Infection Survey as genetic lead, analyzing SARS-CoV-2 persistence and transmission chains. Her lab explores viral coevolution, treatment resistance, and pandemic response mechanisms. Scientific awards include the Sir Henry Dale Fellowship. Recent work addresses optimal genomic surveillance methods and the interplay between within-host and population-scale viral evolution. Ongoing projects involve long-read sequencing of Hepatitis C virus and machine learning approaches for lineage identification in SARS-CoV-2.

• Biomedical Signal Processing
• Decision-Making Methodologies
• Frailty Prevention in Elderly
• +۳ مورد دیگر

Aly CHKEIR is an Associate Professor at the University of Technology of Troyes (UTT), holding the 'SilverTech' chair focused on technologies for healthy aging and elderly autonomy support. He earned his PhD in biomedical sciences from the University of Technology of Compiègne (UTC) in 2011, specializing in uterine electrical activity modeling. His research emphasizes biomedical signal processing, decision-making methodologies, and applications in frailty prevention among the elderly. Education: PhD in Biomedical Sciences, UTC, France (2011). Research interests include signal segmentation/classification, biomedical engineering, and solutions for aging populations. Notable projects include the SilverTech chair's holistic approach to aging technologies and the PiCADo initiative for in-home medical monitoring systems. Recent work focuses on AI-driven diagnostics (e.g., MRI segmentation, cough analysis), radar-based mobility assessment, and voice recognition for health monitoring. His contributions span sensor fusion, gait analysis, and non-invasive health tracking tools. His lab collaborations include Doppler radar systems for gait speed measurement, thermal imaging for health indicators, and AI-enhanced geriatric assessment tools. Projects like ARPEGE and PiCADo highlight his commitment to deploying innovative healthcare solutions for aging populations.

MEHMET DİKİCİمشاهده پروفایل

دانشیار

• Hydrology
• Flood Modeling
• Drought Analysis
• +۲ مورد دیگر

Mehmet Dikici serves as Associate Professor in the Department of Civil Engineering at Alanya Alaaddin Keykubat University's Faculty of Engineering, specializing in hydrological systems and water resource management with 15+ years of academic experience. His academic credentials include: Doctorate in Civil Engineering from Istanbul University (2011-2016) focusing on Hydrodynamic modeling of Alibeyköy Basin and flood risk mapping Master's in Hydraulic and Water Resources Engineering from Istanbul Technical University (2006-2009) with thesis on Dry period analysis of Istanbul rainfall Non-thesis Master's in Urban Studies and Management from Istanbul Sabahattin Zaim University (2014-2015) Bachelor's in Civil Engineering from Istanbul Technical University (1986-1990) Research centers on flood/drought dynamics using remote sensing and hydromechanical modeling , with significant focus on Turkish basins including Alibeyköy, Seyhan, and Ceyhan. His work integrates geospatial technologies for risk assessment and numerical simulation for urban water management challenges, particularly in Mediterranean coastal regions. Recent publications demonstrate methodological evolution from traditional hydrological modeling (2018-2020) toward integrated remote sensing approaches (2021-2024), with increasing emphasis on climate change impacts and post-disaster flood analysis. Key trends include multi-index drought assessment, fire-flood interactions, and basin-specific flood probability frameworks. As Department Head (2018-2020), he led research projects including Impacts of Low Impact Developments on Surface Flow in Alibeyköy Watershed (2016) and Calibrated Hydrological Model for Alibeykoy Watershed (2018-2019), plus national initiatives like the IPCC Turkey report (2024) and 1st Water Council (2021). His applied work connects academia with disaster response through Turkish Red Crescent leadership (Alanya Branch Head since 2019) and board memberships in CEKUD and BAMDER (since 2012), focusing on coastal erosion studies and ancient city infrastructure evaluation.

Yujiang Xiangمشاهده پروفایل

دانشیار

• Biomechanics
• Robotics
• Exoskeletons
• +۴ مورد دیگر

Yujiang "Mike" Xiang is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Oklahoma State University (OSU), part of the College of Engineering, Architecture and Technology (CEAT). His research focuses on biomechanics, robotics, and musculoskeletal mechanics, with applications in exoskeletons, optimization algorithms, and human-robot collaboration. He holds a Ph.D. in Civil Engineering from the University of Iowa (2008), and M.S. and B.S. degrees in Automotive Engineering from Tsinghua University (2004 and 2001, respectively). Dr. Xiang's work emphasizes predictive models for human motion, including lifting tasks, gait analysis, and fatigue prediction. His research integrates multi-objective optimization, musculoskeletal modeling, and robotics to enhance ergonomic safety and human performance. Notable achievements include the Outstanding Research Contribution Award from the Virtual Soldier Research Program (2007). He teaches courses such as MAE 3324 (Machine Design I), MAE 3724 (Dynamic Systems Analysis), and MAE 5010 (Computational Biomechanics). His publications span over 15 years, addressing topics like exoskeleton control, fatigue modeling, and 3D lifting simulations. Recent efforts focus on hybrid predictive models and human-robot collaborative systems.

Moez Louatiمشاهده پروفایل

دانشیار مدعو

• Fluid Mechanics of Waves
• Pipeline Hydraulics
• Water Loss Management
• +۵ مورد دیگر

Moez Louati is an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology (HKUST). His expertise spans fluid mechanics of waves, pipeline hydraulics, and smart water infrastructure. He holds a PhD from HKUST, supported by scholarships and the SENG PhD Fellowship Award. His research focuses on pipeline defect detection, hydraulic transients, and smart urban water systems, with notable contributions including the discovery of Bragg resonance connections in pressurized pipes. He has led projects funded by RGC, ITF, and international collaborations, and co-founded Hyele Limited, a startup developing pipeline condition assessment technologies. Education: B.Eng. (Electro-Mechanical Engineering) and MPhil (Renewable Energy) from Tunisia’s National School of Engineering of Sfax (ENIS). Professional credentials include HKIE chartered engineer status and roles as Chair of IAHR’s Africa Division and Assistant Editor of the Journal of Hydraulic Research. Research Interests: Fluid mechanics of waves, computational fluid dynamics (CFD), machine learning for infrastructure optimization, and sustainable water management. Key projects include Smart Urban Water Supply Systems and high-frequency wave-based defect detection methods. Awards: Geneva Innovations Expo 2023/24 awards, Straub Award nomination, and HKUST’s Tech-Ship startup funding. Active in international organizations and advisory roles for Hong Kong’s WSD and DSD. Labs & Facilities: Designed three water lab facilities at HKUST and collaborated globally on projects like the FREJLEV Facility’s air-water stratified flow studies. Holds five patents in pipeline diagnostics and HPC applications.

• Artificial Intelligence in Medicine
• Cardiovascular Disease
• Machine Learning Applications
• +۳ مورد دیگر

Sumukh Vasisht Shankar is a Researcher at Yale University's Yale School of Medicine, Department of Internal Medicine. His work focuses on advancing artificial intelligence applications in cardiovascular diagnostics and healthcare data analysis, particularly leveraging deep learning and large language models (LLMs) to improve clinical decision-making. He specializes in developing algorithms for ECG analysis, structured data transformation from unstructured clinical reports, and scalable phenotyping across non-interoperable health systems. Key research areas include AI-driven detection of structural heart diseases, myocardial infarction, and atrial fibrillation risk assessment. His projects often involve multinational validation and integration of AI tools into clinical workflows, such as the CarDS-Plus ECG platform and digital twin strategies for clinical trial adaptation. Shankar's contributions bridge machine learning innovation with real-world medical challenges, aiming to enhance diagnostic accuracy, streamline healthcare data management, and personalize patient care. Recent work includes developing ensemble learning models for heart disease screening, retrieval-augmented generation (RAG) systems for clinical risk scoring, and AI-based surrogate imaging techniques for aortic stenosis. His methodologies emphasize scalability, adaptability to portable devices, and ethical compliance with data localization regulations.

• Brain Energy Metabolism
• Magnetic Resonance Imaging (MRI)
• Molecular Imaging
• +۵ مورد دیگر

Dr. D. S. Fahmeed Hyder is a Professor of Radiology & Biomedical Imaging and Biomedical Engineering at Yale University, with joint appointments in the Yale School of Medicine. He leads the Magnetic Resonance Research Center (MRRC) and directs the Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR). His research focuses on brain energy metabolism, developing advanced MRI techniques to study neuronal-glial interactions, metabolic dysfunction biomarkers, and molecular imaging of tumors. He holds over 240 peer-reviewed publications and has secured continuous NIH funding for 25+ years. Education: B.A. in Chemistry (Wabash College, 1990), Ph.D. in Biophysical Chemistry (Yale University, 1995). Postdoctoral training includes roles at Oak Ridge National Laboratory, University of Rochester, and Yale. Research Interests: Functional MRI (fMRI) advancements, calibrated fMRI for quantitative neural activity, BIRDS molecular imaging (for pH/temperature), and applications in neurodegenerative diseases (e.g., Alzheimer’s) and oncology. Current projects include pH biosensor development, tumor response monitoring, and neuropil density prediction via AI. Awards: Melvin H. Knisely Award, Niels Lassen Award, Lundbeck Visiting Professorship, and fellowships in AIMBE, ARBR, and Sigma Xi. Recently appointed Head of Trumbull College at Yale. Grants: Continuous NIH support across 7 institutes; active projects in molecular imaging, stroke models, and neurovascular coupling. Labs/Teams: MRRC (mrrc.yale.edu), QNMR (qnmr.yale.edu), collaborating with interdisciplinary teams in physics, engineering, and biology.