جستجوی استاد راهنمای دانشگاهی

Min Shinمشاهده پروفایل

استاد

• Computer Vision
• Multiple Object Tracking
• Automated Visual Inspection
• +۴ مورد دیگر

Min Shin is a Professor and Chair of the Computer Science Department at the University of North Carolina at Charlotte (UNC Charlotte), where he also serves as Assistant Dean for Research in the College of Computing and Informatics. He earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2001. His research focuses on Computer Vision, particularly multiple object tracking and automated visual inspection of nuclear power plants. He leads the Video and Image Analysis Lab, developing user-friendly tracking software funded by NSF, NIH, DARPA, and industry grants. Notable projects include the ABCTracker.org platform, which deploys algorithms refined over 10+ years of research. Dr. Shin’s work spans tracking applications in diverse domains such as ants, bees, cells, termites, robots, and vehicles. He has mentored Ph.D. and undergraduate students extensively, emphasizing computational methods in biology and engineering. His service includes roles as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics-B, and program committees for top conferences (ICCV, CVPR, ECCV). His grants and collaborations reflect interdisciplinary strengths in vision algorithms, usability, and real-world deployment.

Ernesto Jiménez-Ruizمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۵ مورد دیگر

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

Colleen Baileyمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

Peyman Badakhshanمشاهده پروفایل

پژوهشگر

• Business Process Management
• Process Mining
• Industrial Engineering

Dr. Peyman Badakhshan is a Researcher at the Chair of Information Systems and Business Process Management at the University of Münster. His expertise spans Business Process Management, Process Mining, and Industrial Engineering. Educational background includes a PhD in Business/Managerial Economics from Universität Liechtenstein, an MSc in Information Systems, and an M.Eng. in Industrial Engineering. Research focuses on operationalizing process analytics across domains like supply chain, healthcare, and manufacturing. Recent work develops frameworks for agile BPM and process mining tools, with applications in medical training, emergency services, and industrial process optimization. Publications demonstrate consistent innovation in mapping business process landscapes, developing diagnostic instruments, and creating large-scale datasets for academic use. Research integrates methodologies from decision analysis, fuzzy logic, and pattern recognition to solve complex operational challenges.

Yi Duمشاهده پروفایل

پژوهشگر

• Infectious Disease Surveillance
• Data Science in Public Health
• COVID-19 Surveillance
• +۳ مورد دیگر

Yi Du is a Healthcare Epidemiologist in the Department of Epidemiology at the College of Public Health, University of Nebraska Medical Center (UNMC), a position held since 2022. Previously, Du served as an Emergency Medical Services (EMS)/External Cause of Injury Code (E-code) Data Analyst Intern and State Epidemiologist Assistant at the Nebraska Department of Health and Human Services, focusing on COVID-19 surveillance. Education: PhD in Epidemiology, University of Nebraska Medical Center, 2017-2022 MS in Public Health, University of Miami, 2014-2016 MS in Developmental Neurobiology, Lanzhou University, 2011-2014 BS in Bioscience, Lanzhou University, 2007-2011 Yi Du's research focuses on infectious disease surveillance, particularly for COVID-19, including case investigation, hospitalization, death, and vaccine effectiveness. Additional areas include injury surveillance (drug overdose, suicide, EMS), data science applications in public health for data integration and visualization, and studies on nonalcoholic fatty liver disease. Du employs cross-sectional, surveillance, and survey methodologies to address public health challenges. Recent publications (2021-2022) demonstrate a strong emphasis on COVID-19 epidemiology, with multiple studies on school-based transmission, community risk assessment, and case growth rates. Other work addresses occupational health in agricultural settings (musculoskeletal discomfort, pulmonary conditions) and injury surveillance. The research integrates data science for public health applications, reflecting a multidisciplinary approach to epidemiological challenges. No scientific awards, prizes, or fellowships were mentioned. Yi Du has not listed any formal advisees or students. Additionally, no grant information was provided in the text.

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +۵ مورد دیگر

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics

Yasmeen Georgeمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +۶ مورد دیگر

Yasmeen George is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology. She holds a PhD in Medical Image Processing from the University of Melbourne (2018) and a Master's in Computer Science from the University of Ain Shams (2013). Her research focuses on AI applications in healthcare, particularly medical image analysis for conditions like cancer, glaucoma, and psoriasis. She co-founded the AIM for Health Lab at Monash IT and is a research affiliate at the Victorian Institute of Forensic Medicine. Education: PhD (Medical Image Processing), University of Melbourne (2018); Master of Computer Science (Medical Image Analytics), University of Ain Shams (2013). Research interests include AI-driven medical image analysis, machine learning, and cross-domain healthcare analytics in radiology, dermatology, and ophthalmology. Her work addresses challenges in automated disease detection, lesion segmentation, and severity assessment across modalities like 2D/3D imaging and text data. Recent articles focus on kidney segmentation, glaucoma detection, federated learning for breast cancer, and AI frameworks for hazardous waste policy. Her research has led to patents with IBM and grants from MRFF and NHMRC. Awards: Recipient of the 2023 Heidelberg Laureate Foundation Alumni Award. Collaborations include projects on clean energy sustainability and hazardous waste management through advanced AI techniques. She actively advises PhD students and engages in interdisciplinary research teams across Monash and industry partners. Labs/Teams: Co-founder of the AIM for Health Lab, affiliated with the Victorian Institute of Forensic Medicine.

Arya Farahiمشاهده پروفایل

استادیار

• Statistical/Machine Learning
• Algorithmic Fairness
• Astroinformatics
• +۳ مورد دیگر

Arya Farahi is an Assistant Professor of Statistics and Data Sciences at the University of Texas at Austin since 2021. His research bridges astroinformatics, urban informatics, and AI ethics, focusing on mitigating algorithmic bias and uncertainty quantification in real-world applications. He holds PhDs in Physics and Scientific Computing from the University of Michigan, where he was a Data Science Fellow at the Michigan Institute for Data Science. Farahi's work includes collaborations with international projects such as the Dark Energy Survey (DES), COsmostatistics Initiative (COIN), and XMM-XXL Consortium. He leads the D3 Lab, which develops AI tools for scientific discovery and societal challenges, emphasizing interdisciplinary collaboration. His open-source contributions include TATTER, KLLR, and PoPE for statistical analysis and visualization. Key awards include the Best Student Paper Award at KDD’18 and a $400k+ grant. He is a volunteer with Statistics Without Borders and actively involved in projects like the Fire and Smoke Digital Twin for urban resilience. His research spans cosmology, healthcare AI, and urban economics, with a focus on trustworthy models and equitable AI systems.

• Computer vision
• Machine learning
• Statistical data analysis
• +۳ مورد دیگر

Ceyhun Burak Akgül is a Part-Time Lecturer specializing in Computer Vision, Machine Learning, and Statistical Data Analysis. His research focuses on interdisciplinary applications of visual data processing, including medical imaging and 3D object recognition. He maintains a personal website at cba-research.com and can be contacted at cb.akgul@gmail.com . His work spans topics such as image captioning, visual dictionaries, and symbolic feature detection. Key contributions include developing algorithms for action recognition using depth cameras and frameworks for leaf and object recognition. His research also integrates medical applications, such as analyzing Alzheimer’s patient movements and automated diagnosis using imaging data. Akgül’s publications frequently address challenges in 3D shape descriptors, feature selection, and interdisciplinary methodologies. His recent work includes exploring visual dictionaries and improving image processing techniques through model-driven approaches. His academic contributions are evident in journals like the Journal of Visual Communication and Image Representation, and he has participated in competitions like SHREC. Despite his extensive publication record, no formal awards or grants are explicitly mentioned in the provided data.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Dr Tianning Li is a Lecturer in Computing at the University of Southern Queensland's School of Mathematics, Physics and Computing. Affiliated with the School of Agriculture and Environmental Science, their research focuses on biomedical engineering, signal processing, and machine learning applications in clinical settings. Dr Li holds a PhD from USQ, an MAccFin from Adelaide, and a BISM from Nanjing. Core research interests include EEG signal analysis for anesthesia monitoring and epilepsy detection, with emphasis on developing novel signal processing techniques like spectral entropy analysis, synchroextracting transforms, and federated learning approaches. Their work bridges machine learning innovations with clinical diagnostics, addressing challenges in real-time medical signal interpretation and healthcare data efficiency. Recent publications (2021-2025) emphasize advancements in anesthesia depth assessment algorithms, seizure prediction methodologies, and lossless signal compression. Research trends reflect a strong focus on integrating neural networks (CNN-LSTM, 1D CNN) with traditional signal analysis frameworks to enhance clinical decision-making accuracy. No scientific awards are listed, but Dr Li maintains active research collaborations through affiliations with multiple departments. Supervision activities are not detailed in available records, though their work likely involves student contributions to biomedical computing projects. ORCID: 0000-0001-5142-8654 .

Alain Trouvéمشاهده پروفایل

استاد

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
• +۳ مورد دیگر

Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.

Mingyang Luمشاهده پروفایل

استادیار

• Computational Systems Biology
• Gene Regulatory Networks
• Single Cell Genomics
• +۵ مورد دیگر

Mingyang Lu is an Assistant Professor in the Department of Bioengineering at Northeastern University. Previously, he led an independent research lab at The Jackson Laboratory since 2016. He holds a PhD in Biochemistry and Molecular Biology from Baylor College of Medicine (2010) and a BS in Physics from Fudan University (2003). His research focuses on computational systems biology, integrating mathematical modeling and bioinformatics to study gene regulatory networks, single-cell genomics, and epithelial-mesenchymal transitions. Key projects include developing tools like Gene Circuit Explorer (GeneEx) and investigating microRNA roles in stochastic networks. He has received the Maximizing Investigators’ Research Award (MIRA) from the National Institute of General Medical Sciences. Education : PhD in Biochemistry and Molecular Biology (Baylor College of Medicine, 2010); BS in Physics (Fudan University, 2003) Research Labs : Lu Lab (Northeastern), Center for Theoretical Biological Physics Dr. Lu has advised students including Kaitlyn Ramesh (Goldwater Scholar), Maya De Los Santos, and others. His work bridges computational methods with biological systems, emphasizing stochasticity and heterogeneity in gene expression. Recent grants include NSF support for genome-editing studies on microRNA roles and collaborative projects with the University of Maine.

• Eye Tracking Methodology
• Human-Computer Interaction
• Computer Graphics
• +۵ مورد دیگر

Andrew T. Duchowski is a Professor at Clemson University, specializing in Eye Tracking Methodology, Human-Computer Interaction, and Computer Graphics. His work spans over two decades with significant contributions to gaze-based interaction systems, foveated rendering, and cognitive load measurement. He authored three editions of the influential textbook Eye Tracking Methodology (Springer, 2003/2007/2017). Duchowski's research integrates eye movement analysis with applications in virtual reality, medical imaging (e.g., colonography viewers), and aviation safety. He actively collaborates with institutions globally and serves on editorial boards for journals like Proceedings of the ACM on Human-Computer Interaction . His recent projects include developing real-time gaze analytics pipelines and exploring entropy-based metrics for visual attention analysis. Publications (selected 15 recent): Focus on advancing gaze interaction in immersive environments, optimizing 3D visualization, and measuring cognitive load through pupillary activity and microsaccades. Key co-authors include Krzysztof Krejtz, Matias Volonte, and Donald House.

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +۴ مورد دیگر

Francisco R. Ortega is a researcher at Colorado State University specializing in Virtual Reality (VR) , Augmented Reality (AR) , and Human-Computer Interaction (HCI) . His work focuses on improving user experience through multimodal interaction, cognitive load theory, and immersive analytics. Recent publications explore diverse applications including: Stress reduction via VR forest bathing Instruction methods for AR-based autism support systems Annotation design in extended reality AR notifications in cooking environments Gender-swapping VR for stereotype threat mitigation Comparative studies between AR and VR for data-driven storytelling His research integrates principles from: Computer Science Psychology Human Factors Education Technology Collaborations span institutions such as University of Florida, University of Seville, and technical teams across North America. While specific educational details remain undisclosed, his 20+ peer-reviewed publications since 2018 demonstrate deep contributions to VR/AR methodology, particularly in: Unconstrained gesture elicitation Spatial transformation challenges Immersive training systems Hybrid display environments Imperfect machine learning integration

• Co-design
• Healthcare Design
• Data Visualization
• +۴ مورد دیگر

Ian Gwilt is a Research Professor in the Department of Design at UniSA Creative, University of South Australia. His work focuses on co-design methodologies, healthcare innovation, data visualization, and museum experience design. He has authored/co-authored numerous articles on topics such as human-robot interaction, sustainable energy practices, and participatory exhibition development. His research emphasizes translating complex data into tangible experiences and fostering stakeholder collaboration in complex systems. Affiliations: UniSA Creative (Faculty/School) Department of Design MOD. (Museum of Discovery) Australian Research Centre for Interactive and Virtual Environments (IVE) Research Interests: Gwilt’s primary areas include co-design frameworks for healthcare systems, data physicalization, museum visitor engagement, and the application of visual communication in interdisciplinary research. He explores how design thinking can address challenges in sustainability, chronic disease management, and human-technology interaction. Key Contributions: Developed protocols for wearable activity trackers in hospital settings through co-design. Authored influential work on graphical signage to reduce anxiety in human-robot collaboration. Co-created the “data souvenir” concept to bridge physical and digital museum experiences. Received the International Federation of Hospital Engineering Building Award 2018 for healthcare architecture analysis. Grants & Projects: His work is supported by grants such as the Medical Research Future Fund (MRFF). Projects include developing patient experience toolkits and exploring midwifery practices through co-design. Labs/Teams: Active in MOD. (Museum of Discovery) and collaborates with interdisciplinary teams in healthcare, architecture, and digital innovation.