جست‌وجوی استاد و پژوهشگر

Colin N. Brooksمشاهده پروفایل

مدرس

• Remote Sensing
• Unmanned Aerial Systems
• Aquatic Ecology
• +۵ مورد دیگر

Colin N. Brooks serves as a Senior Research Scientist at the Michigan Tech Research Institute (MTRI) and an Adjunct Lecturer in Biological Sciences at Michigan Technological University. His work integrates remote sensing technologies with environmental and infrastructure monitoring, specializing in Great Lakes ecosystem studies and transportation asset management. Education: PhD in Biology Master of Environmental Management (MEM) in Resource Ecology Dr. Brooks' research centers on aquatic and landscape ecology, with emphasis on invasive species mapping (particularly submerged aquatic vegetation like Eurasian watermilfoil) using Unmanned Aerial Systems. He develops automated analysis methods for transportation infrastructure assessment including road/bridge condition evaluation through multispectral sensing and LiDAR, and creates decision support systems for environmental management workflows. His 15 most recent publications (2023-2025) reveal dominant trends in UAS-based infrastructure inspection (pavements, bridges, airfields) and environmental monitoring (copper tailings impacts, invasive species). Key methodological advances include machine learning integration for automated crack detection, 3D coastal mapping, and multitemporal analysis frameworks for ecosystem management. Scientific Awards: No awards were listed in the provided information. Advising and Grants: Student advising activities and specific grant funding details were not documented in the source materials. Labs and Teams: Dr. Brooks is actively engaged with the Great Lakes Research Center (GLRC), leading interdisciplinary projects that bridge remote sensing technology with practical solutions for environmental conservation and transportation infrastructure challenges.

Robert Cowenمشاهده پروفایل

استاد

• Biological Oceanography
• Plankton Ecology
• Larval Fish Biology
• +۵ مورد دیگر

Robert (Bob) Cowen is a Professor and the Director of the Hatfield Marine Science Center at Oregon State University. His research focuses on biological oceanography, plankton ecology, and larval fish biology, with a strong emphasis on using advanced in-situ imaging technologies to study marine ecosystems. He has published extensively on topics such as plankton community dynamics, larval fish dispersal, and the role of gelatinous zooplankton in marine food webs and carbon cycling. Research Interests: Biological Oceanography and Plankton Ecology Larval Fish Biology and Dispersal Marine Ecosystem Connectivity In-Situ Marine Imaging and Classification Zooplankton Dynamics and Trophic Interactions Oceanographic Drivers of Marine Life Distribution His recent work includes studies on anchovy trophodynamics in the California Current, imaging-based analysis of mixotrophic protists, and the ecological roles of doliolids and filter feeders in coastal upwelling systems. He has also contributed to technological advances in edge computing for real-time plankton classification at sea. Scientific Contributions: Over 100 peer-reviewed articles in marine science and oceanography Leadership in collaborative research on plankton size spectra and trophic links Development and application of high-throughput in-situ imaging systems Studies on fine-scale plankton distributions and predator-prey interactions Affiliations and Leadership: Director, Hatfield Marine Science Center, Oregon State University Professor, College of Earth, Ocean, and Atmospheric Sciences Active in interdisciplinary marine research and education initiatives

Yunhong Jiangمشاهده پروفایل

دانشیار

• Microbial Cell-Material Interactions
• Antimicrobial Surfaces
• Biological Metal-Organic Frameworks
• +۳ مورد دیگر

Dr. Yunhong Jiang is an Associate Professor in the Faculty of Health and Life Sciences at Northumbria University and a Vice-Chancellor’s Senior Fellow in the Hub for Biotechnology in the Built Environment (HBBE). He holds a PhD in Biochemical Engineering from the University of Leeds, followed by postdoctoral roles at the Universities of Leeds, Bath, and Bristol. His research focuses on microbial interactions with materials, antimicrobial surfaces, biological metal-organic frameworks (MOFs), functional living materials, and sustainable biopolymers. Recent work emphasizes thermally insulating materials derived from sustainable raw materials, particularly hemp-based composites. Collaborating across disciplines with biologists, chemists, and engineers, Dr. Jiang’s research has been funded by EPSRC, BBSRC, the Leverhulme Trust, and the Royal Society of Chemistry. He is a member of the Royal Society of Chemistry and Microbiology Society, and a Certified Passive House Designer. His contributions include advancements in moisture-buffering building materials, functionalized silica coatings, and bio-based insulation solutions for the built environment. Key collaborations include projects on hemp aggregates for construction materials and bacterial cellulose applications. His advisory role includes supervising PhD student Peregrine Greenhope on in situ modification of bacterial cellulose. Dr. Jiang’s work bridges biotechnology and sustainability, addressing environmental and energy challenges through innovative material science.

Alaa AlZoubiمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Dr. Alaa AlZoubi is a Research Fellow in Machine Learning and Computer Vision at The University of Buckingham, with a PhD in Computer Science from the University of Lincoln. He previously held a Research Fellow position at Cranfield University's Centre for Electronic Warfare Information and Cyber, part of the Defence Academy of the United Kingdom. His expertise spans Computer Vision, Medical Image Analysis, Autonomous Systems, and GIS, with 8+ years of industrial experience in R&D and software engineering. Education: BSc and MSc in Computer Information Systems, followed by a PhD in Computer Science from the University of Lincoln. Research Focus: Specializes in applying machine learning to medical imaging (e.g., thyroid and breast lesion classification), autonomous systems, and computer vision for humanitarian applications like land mine detection. Recent projects include a STFC-funded grant investigating robotics and remote sensing for safer land mine surveying. Publications: Over 20 peer-reviewed articles in journals and conferences, focusing on medical image analysis, deep learning frameworks, and vehicle activity recognition. Key contributions include Clustered-CAM for thyroid nodule classification and Faster R-CNN hyperparameter optimization for breast lesion detection. Labs/Teams: Involved in interdisciplinary projects combining robotics, remote sensing, and computer vision for humanitarian and industrial applications.

Clemente Ibarra-Castanedoمشاهده پروفایل

پژوهشگر

• Nondestructive Testing
• Signal Processing
• Image Analysis
• +۳ مورد دیگر

Dr. Clemente Ibarra-Castanedo is a Researcher at the Computer Vision and Systems Laboratory within the Electrical and Computing Engineering Department at Laval University, Québec, Canada. His expertise spans Nondestructive Testing, Signal Processing, and Infrared Thermography. He holds a B.Sc. in Mechanical Engineering from Regiomontana University (1996), an M.Sc. and Ph.D. in Electrical Engineering from Laval University (2000 and 2005). Research Interests: His work focuses on Nondestructive Testing and Evaluation (NDT&E) using Infrared Thermography and digital signal/image analysis. Recent studies emphasize advanced materials, hybrid composites, and aerospace structural diagnostics. He explores data-efficient machine learning models for medical imaging applications. Collaborations: He collaborates with institutions including Universidad del Valle, University of L’Aquila, and Cranfield University, working with co-authors like Xavier Maldague and Stefano Sfarra. His research highlights emerging trends in NDT methods and interdisciplinary applications. Advising/Grants: No advisees or grants were explicitly listed in available records. Labs & Teams: Active member of the Computer Vision and Systems Laboratory, contributing to cutting-edge imaging and NDT technologies.

• Remote Sensing
• Agricultural Technology
• Robotics
• +۳ مورد دیگر

Dr. Péter Udvardy is an Associate Professor at Óbuda University. He is affiliated with the university's engineering and environmental science programs, located at the Budai Út Campus in Székesfehérvár. His research focuses on advanced technologies such as UAV applications, agricultural sensors, robotics, and environmental monitoring. He actively contributes to interdisciplinary fields like precision agriculture, remote sensing, and GIS-driven urban planning. His work combines theoretical and practical innovations, addressing challenges in renewable energy infrastructure inspection, livestock health monitoring via ingestible sensors, and educational robotics. Notable projects include UAV-based wind turbine assessment and bolus sensor development for dairy cattle. Udvardy emphasizes real-world problem-solving in industrial maintenance, data collection methodologies, and sustainable energy solutions. He maintains a consultation schedule on Mondays and can be reached via udvardy.peter@amk.uni-obuda.hu. His research outputs are publicly accessible via Google Scholar and institutional repositories.

Peter Adrianمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Computer Vision
• +۳ مورد دیگر

Peter Adrian is an Associate Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology's College of Engineering and Science. He also serves as Affiliate Faculty in the Department of Electrical Engineering and Computer Science. His research focuses on machine learning applications in signal processing, computer vision, and statistical analysis. He leads projects in environmental sound classification, seismic event discrimination, and structural health monitoring, often leveraging edge computing and deep learning frameworks. Adrian's work bridges theoretical advancements with practical systems engineering, such as developing acoustic situational awareness tools (e.g., ACE plugin) and rail infrastructure monitoring systems. Key contributions include physically-augmented deep learning models for seismic analysis and multimodal datasets for firearm acoustics research. His publications span over 5 decades, addressing challenges in sensor networks, geospatial modeling, and shape analysis using information geometry principles. While no formal awards are listed, his prolific output demonstrates sustained academic engagement. Adrian’s research website provides further details on ongoing projects, though currently inaccessible. His interdisciplinary approach integrates mathematics, computer science, and engineering to solve real-world problems in transportation safety, environmental monitoring, and astrophysics.

Hafiz Alakaمشاهده پروفایل

استاد

• Artificial Intelligence
• Big Data Analytics
• Construction Technology
• +۵ مورد دیگر

Professor Hafiz Alaka is an academic leader and researcher at the University of Hertfordshire, serving as Associate Dean and Director of the Big Data Technologies and Innovation Lab. He leads the 'Prediction of Climate and its Impacts (PCI)' group within the Centre for Climate Change Research (C3R). His expertise lies in applying contemporary technologies like AI, big data, and machine learning to construction, infrastructure, energy, and sustainability challenges. Research interests include offsite construction adoption, pollution prediction using IoT and big data, building energy optimization, and circular economy practices. His work emphasizes interdisciplinary collaboration with industry partners such as Balfour Beatty and Reusefully Ltd, and international collaborations in Malaysia, Nigeria, and Indonesia. He has secured over £10M in research funding and oversees strategic research initiatives at Hertfordshire Business School. Recent publications focus on AI-driven solutions for construction challenges, energy efficiency modeling, and environmental monitoring. Notable projects include developing prediction models for cement quality and mitigating digital transformation risks in Malaysia. He actively contributes to academic governance as an Innovate UK assessor and editorial board member of international journals. His leadership spans research policy development, grant administration, and fostering innovation ecosystems. He mentors PhD students and early-career researchers, emphasizing inclusive, dynamic environments for academic growth.

Yi-Chang James Tsaiمشاهده پروفایل

استاد

• Smart Cities Infrastructure
• Transportation Systems Engineering
• Pavement Preservation and Management
• +۵ مورد دیگر

Dr. Yi-Chang James Tsai is a Professor and CISE Group Leader at Georgia Institute of Technology's School of Civil and Environmental Engineering. He specializes in smart cities infrastructure, transportation systems engineering, and pavement preservation using advanced sensing technologies. His work has significantly impacted Georgia’s pavement management systems, including the development of a GIS-based pavement preservation system for GDOT. He holds the prestigious Chinese Chang Jiang Scholar title (2009) and received an innovation award for GIS-enabled ship recognition (2007). His research integrates emerging technologies like 3D laser, LiDAR, and AI to enhance infrastructure resiliency, roadway safety, and asset management. Key projects include predictive safety modeling, autonomous vehicle integration, and sustainability of transportation networks. Dr. Tsai leads the Smart City Infrastructure (SCI) Lab, focusing on infrastructure condition assessment, deterioration analysis, and optimization strategies. Education: Doctorate in Civil Engineering (prior to Georgia Tech faculty role) Grants: Multiple GDOT-funded projects on pavement preservation, HFST implementation, and sensor-based safety systems Dr. Tsai’s work emphasizes data-driven decision-making, cost-effective maintenance, and innovation in transportation infrastructure. His lab has pioneered technologies for automated sign detection, pavement crack analysis, and real-time safety scoring. Key Achievements: Developed GDOT’s pavement preservation system managing 18,000 miles of highways Advanced AI-based crack detection and 3D imaging techniques Leader in roadway safety audits and curve sign optimization The Smart City Infrastructure Lab collaborates with industry partners to address challenges in infrastructure sustainability, resiliency, and smart mobility solutions for aging populations and energy efficiency.

• Deep learning
• Reinforcement learning
• Computer vision
• +۳ مورد دیگر

Dr. Jan Petrik is a full-time faculty member at ETH Zürich, affiliated with the Professorship for Advanced Manufacturing. His research focuses on integrating artificial intelligence with manufacturing processes, particularly in deep learning, reinforcement learning, and computer vision applications for metal forming and additive manufacturing systems. Current position: Professor, Advanced Manufacturing, ETH Zürich Research interests: AI-driven manufacturing optimization, microstructural control, and process modeling Recent work: Development of AI frameworks like DeepForge, RLTube, and CrystalMind for metal forming and additive manufacturing

Olaf Eisenمشاهده پروفایل

استاد

• Glaciology
• Climate Science
• Polar Research
• +۳ مورد دیگر

Prof. Olaf Eisen is a glaciologist at the Alfred Wegener Institute (AWI) and a Professor at the University of Bremen. His research focuses on ice sheet dynamics, glacial interactions with climate systems, and the role of ice masses in Earth's climate. He leads projects like the Beyond EPICA ice core drilling initiative to study ancient climate records. Key research areas include ice-ocean interactions, radar stratigraphy analysis, and the impact of climate change on polar regions. Education and affiliations include dual roles at AWI and the University of Bremen, with extensive fieldwork in Greenland and Antarctica. His work integrates advanced radar technology, machine learning for data analysis, and international collaborations to map ice sheet structures and model future climate scenarios. Awards include the Heinrich Award (2021). Current projects involve subglacial topography mapping, ice stream dynamics, and developing automated methods for tracing ice layers. His research bridges glaciology with geophysics, environmental science, and climate modeling to address global climate challenges.

• Neural Networks
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Zhigang Li is a Professor in the Department of Computer Science at South China University of Technology's School of Computer Science and Engineering. His research spans multiple technical domains with significant contributions to neural networks, medical imaging, computer vision, and sensor technologies. Recent collaborations include work with Northwestern Polytechnical University, Hong Kong University of Science and Technology, and various medical research institutions. Dr. Li's research interests focus on neural network architectures, particularly small-world and feedforward networks for system modeling and medical applications. His work bridges computer science with practical applications in healthcare (EEG analysis, schizophrenia detection, liver transplant allocation), environmental monitoring (wastewater treatment), and engineering systems (CMOS image sensors, UAV networks). His research demonstrates strong interdisciplinary connections between theoretical computer science and real-world problem solving. Analysis of his recent publication trends shows increasing focus on medical applications of AI, with significant work in brain functional network analysis, depression recognition, and schizophrenia detection. His technical contributions include novel neural network architectures, efficient sensor systems, and advanced signal processing techniques. The publications reveal a consistent pattern of high-quality output in top-tier journals across multiple disciplines. Dr. Li has received recognition through publications in prestigious venues including IEEE Transactions, Medical Image Analysis, and Expert Systems with Applications, though specific awards aren't documented in the provided bibliography. His work demonstrates significant impact across multiple fields, particularly in applying computational methods to healthcare challenges. His research program includes collaborations with medical researchers for brain imaging applications, electrical engineers for sensor development, and computer scientists for network architecture design. Current projects appear focused on multi-view brain network analysis, energy-efficient sensor systems, and medical AI applications with potential clinical impact.

Lianhua Chiمشاهده پروفایل

مدرس

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۷ مورد دیگر

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

Kostadinka Bizhevaمشاهده پروفایل

استاد

• Biophysics
• Medical Imaging
• Optical Coherence Tomography (OCT)
• +۳ مورد دیگر

Kostadinka Bizheva is a Professor and Graduate Officer in the Department of Biophysics at the University of Waterloo. Her research focuses on advancing optical coherence tomography (OCT) technologies for high-resolution, non-invasive imaging of biological tissues, particularly in ophthalmology and biomedical engineering. She leads projects on neurovascular coupling in the retina, corneal structure analysis, and dynamic contrast imaging for disease diagnosis. Her work integrates OCT with electrophysiological measurements (e.g., ERG) to study retinal physiology under healthy and pathological conditions. Key contributions include developing line-field and super-resolution OCT systems for cellular-level imaging of the cornea, limbus, and retinal vasculature. Dr. Bizheva’s research also explores applications in oncology, such as monitoring tumor response to radiation therapy using dynamic OCT. She has pioneered computational methods to correct optical aberrations and enhance image resolution, enabling precise measurements of blood flow, tissue morphology, and functional responses. Her interdisciplinary approach bridges engineering, physics, and medicine, with implications for early disease detection and treatment evaluation. Notable platforms developed in her lab include Powell lens-based line-scan SD-OCT for cellular-resolution imaging and physics-informed diffusion models for image reconstruction. These innovations support studies on keratoconus, glaucoma, and radiation-induced tissue changes. Her work has been published in high-impact journals and presented at conferences like Ophthalmic Technologies and Translational Vision Science.

Joakim Larssonمشاهده پروفایل

مدرس

• Wire Drawing Process Optimization
• Manufacturing Engineering
• Additive Manufacturing
• +۴ مورد دیگر

Joakim Larsson is a Lecturer at Örebro University's School of Science and Technology, Department of Natural Sciences and Technology. His work focuses on wire drawing processes, additive manufacturing, and process monitoring systems. He leads research projects such as 'Future wire drawing research' and 'Intelligent tool cooling for Wire drawing (iWire)', and contributes to the 'Mechanics and materials' research group. Research interests include optimizing wire drawing parameters, enhancing tooling via additive manufacturing, and improving thermal management in manufacturing. Notable projects address conformal cooling design, sensor-based process monitoring, and material wear analysis. Publications span topics like lattice structure optimization using electron beam manufacturing, pull force calculation methods, and nozzle size effects in 3D printing. Recent work emphasizes digitalization (Wire 4.0) and Industry 4.0 integration in wire production. His research integrates experimental and theoretical approaches, with applications in cemented carbide tooling, thermal imaging, and vibration analysis. Collaborations include industry partners like The Wire Association International (WAI) and academic conferences such as AMPA and MAMC.