Find Academic Supervisors

• Spine Biomechanics
• Back Pain
• Musculoskeletal Conditions
• +3 more

Alex Breen serves as Visiting Associate Professor at Bournemouth University's Faculty of Science and Technology and full-time Senior Research Fellow/Technology Lead at AECC University College. His research investigates mechanical aggravation of musculoskeletal conditions through quantitative fluoroscopy analysis of spinal motion, with emphasis on lumbar intervertebral dynamics and back pain mechanisms. His educational background includes: PhD in Prosthetics and Biomechanics (Bournemouth University, 2016) MSc in Medical Physics (Open University, 2012) BSc (Hons) in Physics with Medical Applications (Exeter University, 2005) Dr. Breen's research focuses on identifying biomechanical biomarkers for treatment-resistant chronic low back pain through quantitative assessment of intervertebral motion sharing patterns. He examines relationships between disc degeneration, spinal kinematics, and pain using fluoroscopic imaging, finite element modeling, and motion capture systems. Recent work explores microgravity effects on spinal geometry and spatio-temporal clustering of asymptomatic spinal motion. Analysis of his 2021-2025 publications reveals consistent emphasis on methodological validation (e.g., motion capture systems, fluoroscopy), clinical translation of biomechanical findings, and international collaboration in chiropractic research. Key themes include cervical/lumbar motion analysis, disc pressure modeling, and development of patient-specific spinal assessment protocols. His scientific recognition includes: 2019 NCMIC/JMPT RESEARCH AWARD Society for Back Pain Research Travel Fellowship (2016) Dr. Breen supervises PhD candidates Jacqueline Rix (biomechanical effects of spinal manipulation) and Terence McSweeney (quantitative imaging biomarkers). He has secured research funding including the Radiological Research Trust Travel Grant (2016) and serves on the Society for Back Pain Research executive committee (2018-present). His extensive peer review activities span journals including The Spine Journal, BMC Musculoskeletal Disorders, and Frontiers in Sports and Active Living. As AECC University College's Technology Lead, he directs biomechanical research infrastructure development and methodology innovation, particularly in spinal kinematics assessment and clinical translation of engineering approaches to musculoskeletal health.

• Computer Science
• Medical Multimedia
• Video Retrieval
• +2 more

Bernd Münzer is a researcher at the Institute of Information Technology at Alpen-Adria-Universität Klagenfurt. His work focuses on medical multimedia, video retrieval systems, and human-computer interaction, particularly in endoscopic and laparoscopic video analysis. He contributes to projects involving deep learning for content identification, interactive exploration tools, and medical data visualization. Medical video analysis and retrieval Human-computer interaction in multimedia systems Endoscopic imaging and surgical skill assessment Dynamic content descriptors for video processing Collaborative video search frameworks Temporal analysis of clinical multimedia data His research includes developing tools like ECAT for endoscopic annotation, diveXplore for interactive exploration, and datasets such as Cataract-101 for surgical analysis. Projects emphasize medical multimedia applications, efficient video encoding, and augmented interfaces for clinical workflows.

Hien NguyenView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Biomedical Image Analysis
• +9 more

Hien Nguyen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Houston's College of Engineering. His research focuses on machine learning, artificial intelligence, and their applications in biomedical image analysis, computational medicine, and drug discovery. He specializes in developing AI systems to enhance radiology workflows, improve diagnostic accuracy, and model radiologists' cognitive processes. His work spans domains like medical imaging segmentation, domain adaptation, and interpretable AI for clinical trust. Nguyen's research integrates multimodal data from LIDAR, satellite imagery, and histopathology slides to address challenges in urban mapping, kidney disease classification, and cellular analysis. He has contributed to frameworks like MIRAM for breast lesion risk prediction and ItpCtrl-AI for radiologist intent modeling. His team explores meta-learning, self-supervised learning, and diffusion models for tasks ranging from apoptosis forecasting to seismic data interpolation. Key technical strengths include domain invariant learning, medical dataset synthesis (e.g., MorphSet), and explainable AI systems. His work bridges human expertise with machine intelligence, emphasizing clinical adoption through collaboration between radiologists and AI algorithms.

Alaa AlZoubiView profile

Research Fellow

• Computer Vision
• Machine Learning
• Deep Learning
• +4 more

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.

Ayşe Atalay DutucuView profile

Lecturer

• GIS
• Remote Sensing
• Climate Change
• +3 more

Dr. Ayşe Atalay Dutucu is a Lecturer in the Department of Geography at Sakarya University's Faculty of Humanities and Social Sciences. Her research integrates GIS, remote sensing, and environmental science to address climate impacts on land cover, hydrology, and erosion dynamics. Research interests focus on: Geospatial analysis of climate change impacts Drought and flood risk modeling Land cover transformation in Mediterranean ecosystems Sustainable watershed management Publications demonstrate consistent focus on environmental monitoring through advanced geospatial techniques, with recent emphasis on ensemble modeling for endemic species conservation and flood susceptibility analysis. Awards: Makale Teşvik Ödülü (Publication Incentive Award), Sakarya University BAPK (2012) Advising & Grants: Supervised master's thesis on flood risk modeling and contributed to multiple BAP-funded projects including 'Spatial Analysis of Temperature Trends in Turkey' and 'Sakarya Tourism Information System'. Leads fieldwork teams for hydrological data collection and collaborates with national research networks on climate adaptation strategies.

Ioana Cristina PLAJERView profile

Lecturer

• Image processing
• Multispectral imaging
• Artificial Intelligence
• +7 more

Ioana Cristina PLAJER is a Lecturer at the Department of Mathematics and Computer Sciences, Faculty of Mathematics and Computer Sciences, Transilvania University of Brașov. Her work focuses on image processing, multispectral imaging, artificial intelligence, machine learning, formal languages, and compiler design. She has contributed to both theoretical and applied research, including advancements in spectral data analysis, medical imaging segmentation, and automata theory applications in diverse fields like dog breeding risk assessment and game design. Dr. PLAJER's research explores the intersection of computational methods and real-world challenges, particularly in remote sensing, medical diagnostics, and compiler optimization. Her publications span topics such as satellite data fusion, neural cellular automata for texture synthesis, and deep learning models for improving X-ray angiogram quality. She actively participates in international conferences, presenting work at venues like the IEEE Emerging Technologies and Factory Automation (ETFA) and the International Symposium on Signals, Circuits and Systems (ISSCS). Her technical contributions include developing algorithms for NDVI computation from hyperspectral imagery, creating datasets like DACIA5 for agricultural monitoring, and advancing techniques for tumor segmentation in CT scans. These efforts highlight her commitment to bridging algorithmic innovation with practical applications in healthcare and environmental science.

• Machine Learning
• Pattern Recognition
• Computer Vision
• +5 more

David M. J. Tax is a researcher affiliated with Delft University of Technology, Netherlands. His work spans machine learning, pattern recognition, and computer vision, with a focus on neural networks, anomaly detection, and medical image analysis. He has collaborated extensively with researchers like Marco Loog, Robert P. W. Duin, and Marcel J. T. Reinders on topics including multiple instance learning, dissimilarity-based methods, and physics-informed neural networks. Key research contributions include advancements in incremental learning for neural networks, personalized anomaly detection in biomedical signals, and frameworks for evaluating time-series anomalies. His recent work (2023–2025) emphasizes physics-informed neural networks, stochastic scheduling algorithms, and proximity-aware evaluation techniques. Tax’s publications reflect a strong emphasis on interdisciplinary applications, integrating computer science with biomedical engineering and operations research. His methodologies often address challenges in scalability, robustness, and interpretability, with applications ranging from healthcare monitoring to automated project scheduling. Tax maintains an active presence in top-tier conferences like AAAI, ICPR, and NeurIPS, contributing to both theoretical foundations and practical implementations of machine learning systems.

Matt BoutellView profile

Associate Professor

• Computer Vision
• Machine Learning
• Robotics Education
• +2 more

Matt Boutell is an Associate Professor in the Department of Computer Science and Software Engineering at Rose-Hulman Institute of Technology. His research focuses on multimedia content understanding, robotics education, and computer vision techniques for scene classification. Education: PhD in Computer Science, University of Rochester (2005) MS in Electrical and Computer Engineering, University of Rochester (1989) BS in Electrical Engineering, University of Rochester (1985) His research spans computer vision (semantic scene classification, image orientation detection), educational robotics (simulator development), and computer science pedagogy. Recent publications show strong interest in AI applications for education and security systems. Work integrates low-level vision features with contextual metadata for improved classification. Awards recognize innovations in multimedia analysis and semantic scene understanding. He co-developed Rose-Hulman's multidisciplinary robotics minor and created educational tools like the iRobot Create simulator. As advisor, he guides students in robotics capstone projects and mentors research in vision systems. He coordinates the robotics curriculum and maintains active industry collaborations.

Jin SunView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Deep Neural Networks
• +7 more

Jin Sun is an Assistant Professor in the School of Computing and a faculty fellow at the Institute for Artificial Intelligence at the University of Georgia. He holds a Ph.D. in Computer Science from the University of Maryland, College Park (2018). His research focuses on computer vision and deep learning , particularly in understanding human behavior, 3D geometry, and applying AI to healthcare, agriculture, and societal challenges. He is a Lilly Teaching Fellow and actively collaborates across disciplines. Education: Ph.D. Computer Science, University of Maryland (2018) Affiliations: School of Computing, Institute for AI, Franklin College of Arts & Sciences His research interests include machine learning , generative models , object detection , and context modeling . Recent work spans precision agriculture (e.g., cotton boll analysis), healthcare AI (e.g., bed occupancy detection), and multimodal geospatial applications. Grants & Funding: NIH R21 Grant (PI, 2024) USDOT Grant (Co-PI, 2025) UGA Presidential Interdisciplinary Seed Grant (Co-PI, 2024) Google Inc. Sponsorship (2022–2024) Key Contributions: Developed frameworks for 3D scene reconstruction, AR-driven data collection systems, and benchmark datasets for zooplankton recognition and enzymatic function prediction. His work bridges AI with real-world applications in environmental science and healthcare.

Yi-Chang James TsaiView profile

Professor

• Smart Cities Infrastructure
• Transportation Systems Engineering
• Pavement Preservation and Management
• +5 more

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.

• Multiphase Fluid Dynamics
• Bubble Induced Turbulence
• Surfactant Effects
• +3 more

Hendrik Hessenkemper is a Researcher at the Helmholtz Center Dresden-Rossendorf (HZDR), affiliated with the Institute of Fluid Dynamics. His work focuses on multiphase flow dynamics, particularly bubble-induced turbulence mechanisms, surfactant effects, and advanced bubble tracking technologies. His research integrates experimental methods with computational fluid dynamics and machine learning for imaging analysis. Key research areas include bubble swarm dispersion, Kolmogorov turbulence scaling, and RANS modeling for multiphase systems. He has contributed to studies on bubble cluster lifetimes, surfactant impacts on bubble dynamics, and 3D bubble tracking using deep learning. His experiments often involve analyzing bubble plume oscillations and lift forces in saline water solutions. Notable projects include investigations into bubble aspect ratios in low Morton-number systems and the development of particle shadow velocimetry techniques for bubbly flows. While no specific awards are listed, his work bridges fundamental fluid dynamics with applied engineering challenges in multiphase systems. Collaborations likely extend across HZDR's institutes and international partners due to the center's research focus. He advises through his research activities but no formal student advisees are listed. His current work explores contamination effects on lift forces and the scalability of bubble-induced turbulence models, with potential applications in chemical engineering and environmental fluid dynamics.

Lianhua ChiView profile

Lecturer

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +7 more

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.

Greg AllenbyView profile

Professor

• Marketing
• Applied Bayesian Modeling
• Statistics
• +3 more

Greg Allenby is the Helen C. Kurtz Chair in Marketing and Professor of Marketing at The Ohio State University's Max M. Fisher College of Business. He also holds a courtesy appointment as a Professor of Statistics . His work focuses on quantitative methods in marketing and Bayesian statistical modeling, with applications in product design, pricing, and consumer behavior analysis. Education: Ph.D., 1988: University of Chicago (Statistics and Marketing) M.B.A., 1986: University of Chicago (Statistics and Behavioral Science) M.S., 1981: Illinois Institute of Technology (Operations Research) B.S., 1978: Ohio Northern University (Mechanical Engineering) Research Interests: Bayesian modeling for marketing analytics Conjoint analysis and choice modeling Consumer preference dynamics and heterogeneity Text analysis and sentiment extraction Econometric demand modeling Applications in charitable fundraising and pricing strategy Professional Contributions: Former Editor of Quantitative Marketing and Economics Former Associated Editor of Marketing Science , Management Science , and Journal of Marketing Research Chair of the American Statistical Association's Section on Statistics in Marketing Awards: 2024 AMA Paul Converse Award 2023 Gil Churchill Lifetime Achievement Award 2012 AMA Parlin Award 2010 ISMS Long Term Impact Award Fellow, American Statistical Association (ASA) Fellow, Informs Society for Marketing Science (ISMS) Teaching: BUSML 7243: Prescriptive Analytics BUSML 8252: Marketing Models BUSML 8253: Recent Advancements in Marketing Research Labs/Teams: Active contributor to the Nationwide Center for Advanced Customer Insights at Fisher College.

• Precision Agriculture
• Remote Sensing
• Agricultural Engineering
• +4 more

Gambella Filippo is an Associate Professor at the University of Sassari's Agricultural School, Department of Agricultural and Environmental Sciences. His work focuses on precision agriculture, remote sensing technologies, and mechanization of agricultural processes. He specializes in vineyard management, UAS applications, and sustainable farming practices. Research interests include disease detection via computer vision, soil degradation analysis, and the integration of UAV systems for crop monitoring and spraying. He has pioneered studies on biostimulant efficacy using multisensor analysis and developed decision-support systems for resource optimization in viticulture. His publications highlight advancements in precision viticulture, including canopy height modeling, nitrogen content prediction, and invasive species monitoring through satellite imagery. He also explores circular economy models for agricultural waste and Industry 4.0 applications in livestock farming. Notable contributions include the mechanization of saffron harvesting and the optimization of traditional bread production in Sardinia. His work bridges engineering, environmental science, and agronomy, emphasizing sustainability and technological innovation in Mediterranean agriculture.

• Data Mining
• Database Systems
• Temporal Databases
• +4 more

John F. Roddick is a Professor affiliated with Flinders University in South Australia. His research focuses on data mining, database systems, and temporal databases, with significant contributions to association rule mining, schema evolution, and privacy-preserving techniques. He has collaborated extensively with researchers like Shu-Chuan Chu and Jeng-Shyang Pan, producing over 138 publications across journals and conferences. Key contributions include work on schema versioning, temporal vacuuming in databases, and algorithms for wireless sensor networks. His research extends to image processing, biometrics, and swarm intelligence, with notable applications in traffic prediction and secure communication systems. He has edited conference proceedings and contributed to encyclopedic entries on database systems and data warehousing. Roddick's work often bridges theoretical foundations with practical applications, emphasizing interdisciplinary approaches to data management challenges. His publications span venues such as IEEE Transactions on Knowledge and Data Engineering, Data & Knowledge Engineering, and the Journal of Network and Intelligence.