جست‌وجوی استاد و پژوهشگر

Hengqi Tianمشاهده پروفایل

استادیار

• Digital Innovation
• Digital Platforms
• Ground Truth Data Labeling in AI Development
• +۵ مورد دیگر

Hengqi Tian is an Assistant Professor of Information Systems at the CU Denver Business School. She holds a PhD in Management Science and Engineering from China University of Geosciences (Wuhan) with joint training at Georgia State University’s Center for Digital Innovation. Prior to joining CU Denver in 2023, she was a postdoctoral researcher at Umeå University’s Swedish Center for Digital Innovation. Education: PhD, Management, China University of Geosciences, Wuhan (201?–201?, with 3 years at Georgia State University) BS, Information Systems and Information Management, China University of Geosciences, Wuhan Research Focus: Dr. Tian investigates digital platform innovation, human-AI collaboration in AI development (specifically ground truth data labeling), and quantitative methodologies like econometrics and qualitative comparative analysis (QCA). Her work bridges digital innovation theory with empirical studies on platform competition and AI ethics. Publications & Awards: Her research appears in journals like Information and Management and Internet Research , with conference presentations at AOM, PACIS, and INFORMS. Awards include the Best Student Paper at AOM (2021) and Outstanding Contribution in Reviewing (EJIS, 2023–2024). Expertise: Digital platforms, AI ethics, NLP, and experimental research methods.

• Database Systems
• Graph Databases
• Route Planning
• +۴ مورد دیگر

Dr. Theodoros Chondrogiannis is a Post-doctoral Researcher at the University of Konstanz's Database and Information Systems group. He holds a PhD from the Free University of Bozen-Bolzano (2017) and previously worked as a postdoc there until joining Konstanz. His research focuses on route planning, graph database query processing, and spatial network analysis. He leads a DFG-funded project on multi-criteria path queries and teaches 'Graph Data Management and Analysis' annually. Education: BSc (Computer Science, University of Peloponnese, 2009), MSc (Advanced Information Systems, National Kapodistrian University of Athens, 2013), PhD (Free University of Bozen-Bolzano, 2017). Research interests include algorithm design for transportation networks, graph database systems, and geosocial network analysis. His work bridges theoretical foundations with practical applications in urban mobility and query optimization. He will assume an Associate Professor role at NTNU's Department of Computer Science (IDI) starting January 2025. Notable service roles include publicity chair for SSTD 2023 and co-chair for ACM SIGSPATIAL Student Research Competitions (2019-2020).

Xiangnan Kongمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۵ مورد دیگر

Xiangnan Kong is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), serving as the Graduate Coordinator of the Data Science Program. His academic journey includes a BS and MS from Nanjing University (2006, 2009) and a PhD from the University of Illinois at Chicago (2014). He maintains an active research program with significant contributions to data mining and machine learning. Dr. Kong's primary research interests focus on data mining and machine learning, with emphasis on addressing data science problems in biomedical and social applications. His work specifically targets data variety issues across multiple domains including neuroscience, biomedical informatics, social networks, and business intelligence. He has published extensively in top-tier conferences and journals such as KDD, ICDM, SDM, WWW, WSDM, CIKM, and TKDE. His recent publications demonstrate a strong trend toward interdisciplinary applications, with significant work in neuroimaging analysis, healthcare applications, urban traffic estimation, and efficient deep learning architectures. His research bridges theoretical advances in graph mining, heterogeneous information networks, and deep learning with practical applications in biomedical domains and social computing. Dr. Kong has successfully advised numerous PhD and Master's students, with former students now working at prominent organizations including Facebook Research, VISA Research, MIT CSAIL, and Amazon AI. His research is supported by multiple grants including NSF awards, Adobe Gift Grant, Huawei Research Grant, and WPI TRIAD Grant.

Hairong Qiمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Remote Sensing
• +۳ مورد دیگر

Dr. Hairong Qi is the Gonzalez Family Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. Her research focuses on collaborative signal processing, computer vision, machine learning, and remote sensing. She directs the Advanced Imaging and Collaborative Information Processing (AICIP) Lab. Dr. Qi holds a PhD in Computer Engineering from North Carolina State University (1999), and MS/BS degrees in Computer Science from Northern JiaoTong University, China. Her work has been funded by NSF, DARPA, IARPA, ONR, NASA, and DHS. Her research contributions include over 200 technical papers and two co-authored books in computer vision. Notable awards include the IEEE Fellow (2018), NSF CAREER Award (2005), and GRSS Highest Impact Paper Award (2012). She has led projects in smart grid resilience, AI-driven healthcare, and hyperspectral imaging. Professional service includes editorial roles for IEEE Transactions on Image Processing and IEEE Multimedia Magazine, and leadership in conferences like ICPR, ICDSC, and WHISPERS. She has mentored numerous students and advised on interdisciplinary initiatives like the UT Cluster Hiring Initiative in Foundational AI. Dr. Qi’s work spans academic, industrial, and societal impacts, with innovations in sensor networks, cybersecurity, and agricultural robotics.

Sven Hertlingمشاهده پروفایل

استاد

• Knowledge Graph Generation
• Ontology Matching
• Knowledge Graph Integration
• +۳ مورد دیگر

Dr. Sven Hertling is a Substitute Professor for Data Science at the University of Mannheim, affiliated with the Data and Web Science Group. His research focuses on Knowledge Graph Generation, Ontology Matching, and Knowledge Graph Fusion. He teaches courses such as Knowledge Graphs, Data Mining, and Database Technologies. Notable awards include the Best Demo at ESWC 2021 and Best Paper at the Knowledge Graphs and Semantic Web Conference (KGSWC) 2022. Education: PhD in Computer Science (implied by role). His research interests span ontology alignment, large language model integration, and scalable knowledge graph systems. Recent work includes frameworks like KGrEaT for knowledge graph evaluation and tools like MELT for ontology matching analysis. He has contributed to over 50 publications, with a focus on ontology alignment evaluation (OAEI) and semantic web technologies. Key projects include the Software Campus initiative's GUI search engine and DBkWik, integrating knowledge from thousands of Wikis. Grants: €100,000 from Software Campus for the SuGraBo project. Advising: Coordinates OAEI challenges and chairs ESWC 2023. He actively serves on program committees for ISWC, ESWC, and other conferences, emphasizing community engagement in semantic web research.

• Machine Learning
• Data Science
• Health Informatics
• +۳ مورد دیگر

Henry Chu is a Professor at the University of Louisiana at Lafayette's School of Computing and Informatics, serving as the Lockheed Martin/Louisiana Board of Regents Professor of Computer Science/Computer Engineering and Executive Director of the Informatics Research Institute. He holds leadership roles in state initiatives like the Taking Aim at Cancer Program and Louisiana Optical Network Initiative. Dr. Chu earned B.S.E. and M.S.E. degrees from the University of Michigan, and a Ph.D. from Purdue University. His research focuses on machine learning and data science applied to societal challenges such as healthcare system optimization and disaster recovery planning. He leads the Louisiana Department of Health's data analytics projects. His awards include the Distinguished Professor Award (2000) and recognitions from Phi Kappa Phi and Louisiana Board of Regents. He is a Life Senior Member of IEEE and serves on multiple state-level science committees.

Kaixiong Zhouمشاهده پروفایل

استادیار

• Machine Learning
• Graph Representation Learning
• Language Models
• +۴ مورد دیگر

Kaixiong Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State). He previously held a postdoctoral position at the Institute for Medical Engineering and Science (MIT) and earned his Ph.D. in Computer Science from Rice University in 2023. His academic journey includes a Master’s in Electrical Engineering from University of Science and Technology of China (USTC, 2018) and a Bachelor’s in Electrical Engineering from Sun Yat-Sen University (2015). Awards : ORAU Ralph E. Powe Junior Faculty Enhancement Award (2025), Student Travel Award (KDD 2022), Outstanding Graduate Awards (USTC 2018), National Scholarship (2017). His research focuses on graph representation learning , language models , and AI for science & engineering , with applications in synthetic biology, drug discovery, and network analysis. Recent work spans graph neural networks, large language models (LLMs), and privacy-preserving AI, emphasizing efficiency and trustworthiness. Publications in ICML, NeurIPS, and ICLR highlight innovations in structured pruning, knowledge editing, and fairness-aware systems. He actively mentors prospective students and collaborates on interdisciplinary projects.

Lina FAHEDمشاهده پروفایل

دانشیار

• Data Mining
• eXplainable Artificial Intelligence (XAI)
• Time Series Analysis
• +۵ مورد دیگر

Lina FAHED is an Associate Professor in Computer Science at IMT Atlantique, part of the DECIDE research team within the Lab-STICC laboratory in Brest, France. Her primary research focuses on data mining in complex data, eXplainable Artificial Intelligence (XAI), time series analysis, event sequence prediction, and anomaly detection. She holds a PhD in Computer Science from Lorraine University (2016) and a Master’s degree in Software and Knowledge Engineering from Strasbourg University (2012). Her academic journey includes roles such as Temporary Lecturer and Research Assistant at Lorraine University (2015–2016) and a postdoctoral position at IMT Atlantique (2016–2019). Her work emphasizes interdisciplinary applications of AI, particularly in enhancing transparency and ethical considerations in machine learning systems. Key research interests include: Explainability in AI systems Bias detection and mitigation Pattern mining in sequential data Algorithmic approaches for early event prediction Her publications (15 most recent listed) span topics like automata-based explainable representations, XAI reviews, and event prediction algorithms, reflecting her contributions to both theoretical and applied AI research.

Xixi Luمشاهده پروفایل

استادیار

• Process Mining
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Xixi Lu is an Assistant Professor in the Department of Information and Computing Sciences at Utrecht University’s Faculty of Science. Her research focuses on Process-Aware Data Science, Machine Learning, and Artificial Intelligence, with applications in healthcare, IT, banking, and auditing. She holds a Ph.D. and M.Sc. from Eindhoven University of Technology, followed by a postdoctoral position at Vrije Universiteit Amsterdam. Her work emphasizes aligning organizational processes with practical adaptations made by practitioners, such as optimizing care home operations or improving patient data anonymization. She teaches in the Information Science Bachelor’s and Business Intelligence Master’s programs, fostering interdisciplinary collaboration and student-driven innovation. Lu advocates for team science, leveraging diverse perspectives within her group to tackle complex challenges, such as developing algorithms that bridge theoretical foundations and real-world applications. Her recent research includes reinforcement learning for healthcare process optimization, predictive process monitoring, and explainable AI. She emphasizes societal relevance, striving to ensure her work contributes to tangible improvements in efficiency and ethical practices across industries.

Jeremy Barnesمشاهده پروفایل

استادیار

• Cross-lingual Methods
• Transfer Learning
• Sentiment Analysis
• +۵ مورد دیگر

Jeremy Barnes is an Assistant Professor in the IXA Group at the University of the Basque Country (UPV/EHU), part of the HiTZ Centre. He holds a PhD in Computational Linguistics and has extensive postdoctoral experience at the University of Oslo. His research focuses on cross-lingual methods, sentiment/emotion analysis, weak supervision, and under-resourced language resources. He has contributed to projects like NorDial and NorLM, developing tools like skweak. Barnes teaches courses in computational syntax, text mining, and NLP. Education: PhD (Pompeu Fabra University), M.A. and B.A. in Linguistics Key Projects: LUMINOUS (multimodal LLMs), SANT (Norwegian sentiment analysis) His work spans structured sentiment analysis, cross-lingual transfer learning, and dialectal feature identification. He has received awards for his book Sonic Ethnography (2021) and photography (2020). Barnes advises numerous Master's theses and collaborates internationally on NLP challenges.

Brent MacRaeمشاهده پروفایل

عضو هیئت علمی

• Networking
• IT Security
• User Authentication
• +۳ مورد دیگر

Brent MacRae is an Associate Teaching Professor in the Networking and IT Security department at Ontario Tech University, affiliated with the Faculty of Business and Information Technology. He serves as Program Director for the Bachelor of Information Technology (BIT) program (NITS) and manages the university's Networking Lab. MacRae holds a Master of Science in Computer Science (2016) and a Bachelor of IT (2013), both from Ontario Tech University. His expertise includes IT security, networking, and human-factors in user authentication, with active research contributions in these areas. He is certified as a Cisco Certified Network Professional (CCNP) and is pursuing additional certifications in networking and security. MacRae’s research focuses on enhancing network infrastructure and security through practical applications, such as optimizing electric vehicle charging stations and improving supply chain resilience. His work bridges theoretical models (e.g., Benders decomposition for supply chain design) with real-world challenges like disaster recovery and environmental reporting. He collaborates with industry on projects such as scheduling decision support systems and rail hazmat shipment planning. His publications span transportation logistics, supply chain management, and cybersecurity, emphasizing risk mitigation and optimization techniques. MacRae contributes to experiential learning through the Networking Lab and the Faculty of Business and IT’s Experiential Learning initiatives (FBIT XO). He is involved in curriculum development for the BIT program and mentors students in applied IT projects.

Markus Barthمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Neuroimaging
• +۳ مورد دیگر

Professor Markus Barth is a Full Professor in Biomedical Engineering at the University of Queensland (UQ), specializing in MRI physics and medical imaging. He holds roles as Deputy Head of School (Research) in the Faculty of Engineering, Architecture and Information Technology, Director of the National Imaging Facility – Queensland Node, and member of the ARC College of Experts. His academic journey includes a Doctorate in Technical Physics from Vienna University of Technology (1999) and prior appointments at the Medical University Vienna, Donders Institute, and Erwin L. Hahn Institute. His research focuses on advancing MRI techniques for high-resolution anatomical and functional imaging, including susceptibility-weighted imaging (SWI), quantitative susceptibility mapping (QSM), and ultra-high field MRI applications. He has pioneered methods to enhance MRI sensitivity for neurodegenerative diseases, cancer, and cognitive neuroscience studies. Barth’s work integrates cutting-edge technologies like deep learning and parallel transmission to improve image quality and clinical utility. His contributions include developing open-source tools (e.g., QSMxT) and large-scale datasets (e.g., HumanBrainAtlas) for reproducible neuroimaging research. Education: Bachelor of Technical Physics, Vienna University of Technology (1995) Doctorate in Technical Physics, Vienna University of Technology (1999) Research Interests: Optimizing MRI for ultra-high field applications, neuroimaging of brain function and connectivity, SWI/QSM for disease markers, and translational imaging techniques for clinical diagnostics. His work addresses topics like layer-specific fMRI, brain-computer interfaces, and decoding neural activity. Grants & Awards: ARC Future Fellowship (2014), leadership in national imaging initiatives, and contributions to MRI software frameworks. Labs/Teams: Leads the Ultra-high Field Human MR Research program at UQ’s Centre for Advanced Imaging and collaborates with interdisciplinary teams in biomedical engineering and neurology. His lab develops novel MRI protocols and open-source tools for global research impact.

• Data Science
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Patrick Zhang is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland (UQ). His research focuses on applying advanced machine learning techniques to real-world challenges in domains such as medical imaging, environmental monitoring, and structural analysis. Key collaborators include Liangchen Liu, Kun Zhao, Arnold Wiliem, and Brian Lovell. His research interests revolve around data science , with a strong emphasis on computer vision and deep learning . Specific topics include: Wild animal detection and pose estimation Medical slide quality assessment and computational pathology Thermal-to-visible face recognition systems Multi-modal clustering for unsupervised learning Low-resolution image processing and boundary-guided translation Infrastructure corrosion assessment using neural networks Patrick Zhang has produced a steady stream of publications since 2018, demonstrating growth in applying deep learning to healthcare and engineering. His recent work (2022) addresses cell labelling challenges in medical diagnostics, while earlier contributions explored foundational GAN-based approaches and cross-modal recognition systems. The trajectory highlights both technical innovation and cross-domain application of AI. No scientific awards have been listed for Patrick Zhang in the provided information. His advising and grant details are not explicitly mentioned, though his collaborative projects suggest involvement in interdisciplinary research teams.

• Medical Imaging
• Artificial Intelligence in Healthcare
• Deep Learning
• +۳ مورد دیگر

Dr. David Ng is an academic researcher affiliated with the Faculty of Science at the University of Sydney. His work focuses on advancing artificial intelligence and deep learning techniques for medical imaging applications, particularly in areas such as radiomics, multimodal data fusion, and healthcare diagnostics. He contributes to innovations in MRI, CT, PET, and endoscopic imaging analysis, developing algorithms for disease detection, segmentation, and predictive modeling. His research interests span medical image reconstruction, federated learning, motion artifact reduction, and dynamic epidemiological modeling. He has published extensively on topics such as neural network architectures for biomedical applications, multimodal data integration, and AI-driven diagnostic tools. Key contributions include advancements in sleep staging via multimodal networks, scleral deformation analysis using OCT images, and automatic segmentation techniques for cardiac and tumor imaging. His work emphasizes practical applications in oncology, cardiology, and public health, aiming to improve clinical decision-making through computational methods. Dr. Ng’s research also explores the intersection of virtual/augmented reality with medical education and intervention. He collaborates on interdisciplinary projects involving biomedical engineering, computer science, and clinical practice to address real-world healthcare challenges.

Irina Voiculescuمشاهده پروفایل

دانشیار

• Medical Imaging
• 3D Geometric Modelling
• Artificial Intelligence
• +۴ مورد دیگر

Irina Voiculescu is an Associate Professor and Stipendiary Lecturer in the Department of Computer Science at the University of Oxford. Her research focuses on 3D geometric modeling, medical imaging analysis, and AI-driven clinical decision support. She leads the Medical Imaging research group, developing tools like OxMedIS for automated segmentation of MRI, CT, and ultrasound data. Her work emphasizes GPU-accelerated algorithms and reinforcement learning to reduce reliance on manual annotations. Education: PhD in Constructive Solid Geometry (CSG) from the University of Bath (1999). Research interests include medical image segmentation, GPU computing, cephalometric landmark detection, and infant hip dysplasia screening. Collaborates with orthopaedic clinicians and industry partners. Notable contributions: Developed novel evaluation metrics for segmentation accuracy, pioneered GPU-based watershed algorithms, and advanced AI applications in hip and knee imaging. Her team’s work on automated Graf classification for infant hip screening was recognized at ECCV 2024. Awards: Best Paper Award at DEMI 2023, Best Workshop Paper at HSI 2009. Editorial roles include Associate Editor for SPIE Journal of Medical Imaging. Advising: Supervises PhD students in medical AI, including NIHR-funded researchers. Past students include Nanqing Dong (federated learning) and Ziyang Wang (transformer networks). Runs the Spatial Reasoning group, known for the Oxford Robot Games and robotic sheepdog projects. Publications: Over 80 peer-reviewed papers since 2008, with recent focus on few-shot learning, domain adaptation, and clinical validation of AI models.