جست‌وجوی استاد و پژوهشگر

Xu Chuمشاهده پروفایل

استادیار

• Data Management
• Machine Learning
• Data Science

Xu Chu is an Assistant Professor in the School of Computer Science at Georgia Tech's College of Computing. His research develops data management technologies to enhance machine learning usability while employing ML to solve data integration challenges, creating synergistic innovations across both domains. His work bridges data systems and machine learning through innovations in weak supervision, automated data cleaning, and differentiable pipeline optimization. Publications focus on entity matching, data preprocessing automation, and fair ML systems. Recent projects include SketchQL for visual video querying and techniques for improving data quality in ML pipelines. Awards include the Microsoft Research PhD Fellowship and Cheriton Fellowship for his contributions to data cleaning and integration research.

Joohee Kimمشاهده پروفایل

دانشیار

• Image and Video Signal Processing
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Joohee Kim is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, where she has been since 2009. She directs the Multimedia Communications Laboratory and focuses on research funded by U.S. Federal Agencies and the Korean Government. Education: Ph.D., Electrical and Computer Engineering, Georgia Institute of Technology (2003) M.S., Electrical Engineering, Yonsei University (1993) B.S., Electrical Engineering, Yonsei University (1991) Research interests include image/video signal processing, computer vision, machine learning, multimedia communication, and real-time 3D reconstruction . Her work spans applications in autonomous systems, robotics, and advanced driver assistance systems, emphasizing low-complexity algorithms and efficient coding techniques. Her recent articles highlight advancements in pedestrian detection via deep learning, 3D reconstruction fusion methods, and optimized depth map coding. These contributions address challenges in real-time systems, energy efficiency, and robust transmission over wireless networks. Awards: None explicitly listed. Grant Activity: Active in federal and international funding for projects like low-delay distributed video coding and error-resilient transmission. Advising: No students listed, though lab leadership implies mentorship roles. Labs/Teams: Director of the Multimedia Communications Lab, collaborating on 3D imaging, video coding, and intelligent systems integration.

• Computer Vision
• Robotics
• Image Processing
• +۴ مورد دیگر

Professor Sonya Coleman is a Professor of Vision Systems at Ulster University's School of Computing, Engineering and Intelligent Systems. She leads the Cognitive Robotics team within the Intelligent Systems Research Centre and has held roles including Head of the Research Graduate School since 2012. Her expertise spans image processing, robotics, computational intelligence, and financial engineering. She holds a BSc (First Class) and PhD in Mathematics from Ulster University (1999 and 2003, respectively). Research interests include advanced computer vision techniques, robotic systems, and industrial automation. Notable contributions include work on defect detection in additive manufacturing, neuromorphic computing frameworks, and energy consumption forecasting in smart manufacturing. Her research has been funded by EPSRC, the Nuffield Foundation, and the European Commission. Awards include the 2009 Distinguished Research Fellowship from Ulster University. She serves as Secretary of the Irish Pattern Recognition and Classification Society. Her work often integrates innovative solutions for industrial challenges, such as real-time monitoring systems and AI-driven quality control. Professor Coleman has authored numerous peer-reviewed articles, focusing on topics like spiking neural networks, PCB defect analysis, and glacier area change quantification using satellite remote sensing.

• Software Architecture
• Big Software Systems
• Applied Artificial Intelligence
• +۳ مورد دیگر

Arne Wiklund is an Assistant Professor and Head of the Bachelor Programme in Computer Engineering at the Department of Information and Communication Technology, University of Agder. He holds an M.Sc. in Information and Communication Technology from HiA (2003). His research focuses on software architecture, large-scale systems, and applied artificial intelligence with notable work in deep learning applications for environmental monitoring and species classification. He concurrently serves as Senior Researcher at Norce (since 2018) and has extensive industry experience at Kongsberg Oil & Gas Technologies (KOGT) including roles as Technical Coordinator and Senior Software Engineer. His academic contributions span fisheries classification systems and pattern recognition technologies. Key research affiliations include the CAIR – Centre for Artificial Intelligence Research. His recent publications emphasize AI-driven solutions for ecological challenges, such as frog and fish species identification using deep learning frameworks.

Simona Buettiمشاهده پروفایل

استاد پژوهشی

• Visual Attention
• Cognitive Psychology
• Eye Tracking
• +۳ مورد دیگر

Simona Buetti is a Research Professor in the Department of Psychology at the University of Illinois. She holds a PhD from the University of Geneva (Switzerland) and serves as a Faculty Fellow at the Center for Innovation in Teaching and Learning. Her research focuses on visual attention, employing methodologies such as behavioral psychophysics, eye-tracking, and computational modeling. Her work aims to understand how attention guides visual processing, particularly in complex scenes. Dr. Buetti has received prestigious awards including the 2019 NSF Grant (as PI) for developing the TCAS Toolbox, the 2016 BRIDGE Grant, and the 2014 NARSAD Young Investigator Grant. She teaches courses like Psyc230 (Perception and Sensory Processes) and Psyc396 (The Attentive Mind), integrating global educational practices through programs like the Engineering Research Summer School. Her research explores topics such as color-feature guidance in visual search, parallel processing dynamics, and the interplay between emotion and attention. Collaborations span institutions worldwide, with notable studies on online eye-tracking validation and reconciling emotional effects on memory. She leads the VisionLab, advancing tools and theories in visual cognition.

• Computational Linguistics
• Artificial Intelligence
• Image Understanding
• +۳ مورد دیگر

Margaret M. Fleck is a Teaching Professor at the Siebel School of Computing and Data Science , University of Illinois, Urbana-Champaign. She holds a Ph.D. in Electrical Engineering and Computer Science from MIT (1988), an M.S. from MIT (1985), and a B.A. in Linguistics from Yale University (1982). Her research focuses on computational linguistics, unsupervised word segmentation, prosodic features in language modeling, and computer vision. She has contributed to systems like Envision and Schwa , which integrate programming language support for AI and linguistic research. Teaching is central to her work, with extensive development of courses like Discrete Structures (CS 173) and Artificial Intelligence (CS 440) . She emphasizes scalable educational materials, including textbooks, video lectures, and automated assessments. Fleck received the Rose Teaching Award (2019) for her pedagogical innovations. Her former advisees include faculty members such as Ramesh Raskar (MIT), Sang-Kyun Kim (Myongji University), and Cassandra Jacobs (SUNY Buffalo). She has supervised numerous PhD, MS, and undergraduate projects across linguistics, computer vision, and AI. Fleck’s work also extends to interdisciplinary systems, such as Rememberer for museum visit tracking and Rememberer for annotating personal experiences.

Daniel Gollerمشاهده پروفایل

مدرس

• Economics of Education
• Labor Economics
• Causal Machine Learning
• +۴ مورد دیگر

Dr. Daniel Goller is a PostDoc and Lecturer at the University of Bern's Department of Economics, affiliated with the Research Center for the Economics of Education. He holds a Ph.D. in Economics and Finance from the University of St. Gallen (2021), with a specialization in Econometrics, following a Master’s in Quantitative Economics from the University of Kiel (with an exchange at Stockholm University) and a Bachelor’s in Economics from the University of Mannheim. His research focuses on Economics of Education , School-to-Work Transition , and Causal Machine Learning , with additional interests in Labor Economics, Microeconometrics, and applying sports data to economic questions. His work bridges theoretical econometric methods with applied policy analysis, particularly in education and labor markets. His recent publications explore topics such as AI’s impact on occupational choices, gender differences in investment reactions, and the effects of apprenticeship search during the pandemic. He has contributed to journals like the European Economic Review , Annals of Operations Research , and Labour Economics , and his research has been featured in media like SRF Tagesschau and Der Bund . Goller also serves as a Senior Researcher at the Center for Research in Economics of Education and has collaborated extensively with institutions like the Swiss Institute for Empirical Economic Research. His work often involves causal inference techniques and machine learning applications, reflecting his commitment to advancing quantitative methods in social sciences.

• Biometrics
• Computer Vision
• Face Recognition
• +۶ مورد دیگر

Chi Ho Chan is a Research Fellow at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP). His work focuses on biometrics, computer vision, and pattern recognition with emphasis on face recognition, 3D morphable models, and illumination-invariant systems. He has contributed to developing robust facial recognition techniques under varying lighting conditions and resolutions, as well as speaker authentication using lip dynamics. Research interests include: 3D face reconstruction and pose/illumination normalization Deep learning for cross-resolution face recognition Biometric performance analysis over time Kernel fusion of visual descriptors Optimization of 3D morphable models for low-resolution images Key publications explore resolution-aware 3D models (2012), NPT-loss for face recognition (2022), and adaptive biometric systems (2015). His work bridges theoretical computer vision with practical applications in surveillance and authentication systems.

• Computer Science
• Privacy Protection
• Computer Vision
• +۳ مورد دیگر

Riccardo Mazzon is a Researcher at the School of Electronic Engineering and Computer Science, Queen Mary University of London. As a CIS Research Manager, he focuses on privacy protection, computer vision, and machine learning applications. His work bridges technical innovation with ethical considerations, particularly in adversarial attacks, underwater imaging, and multi-target tracking. Research Interests: Privacy-Preserving Technologies Adversarial Machine Learning Computer Vision for Surveillance and Environmental Monitoring Image Quality Assessment and Enhancement Multi-Camera and Crowd Analysis Systems Publications highlight a focus on privacy challenges in digital systems, with recent contributions on adversarial reversibility and underwater image filtering. Earlier work explores crowd dynamics and multi-target tracking algorithms. No scientific awards or grants are explicitly listed in the provided data. Labs/Teams: Likely affiliated with Queen Mary’s Computer Vision and Security research groups, though specific lab affiliations are not detailed.

• Medical Imaging
• Machine Learning
• Temporal Data Analysis
• +۳ مورد دیگر

Svitlana Zinger is a Full Professor at Eindhoven University of Technology (TU/e), specializing in context-informed dynamic image analysis for clinical decision support. She leads research in medical image/video analysis for diagnosis/prognosis, with expertise in temporal data analysis, machine learning, and multimodal data processing. Her affiliations include the Center for Care & Cure Technology, Eindhoven MedTech Innovation Center (e/MTIC), NeuroPlatform, and EAISI Health. She holds a PhD from the École Nationale Supérieure des Télécommunications, France, and has extensive postdoctoral experience in image ontology and handwritten document analysis. Academic background: MSc in Computer Science (Dnepropetrovsk State University, Ukraine, 2000), PhD in 3D Data Interpolation (2004). Research focuses on neuroimaging, cardiovascular monitoring, and AI-driven medical decision support. Key projects include FORSEE (video-based adverse event detection), NEUROTREND (fMRI biomarkers for depression), and STRATUM (neurosurgical 3D tools). Research interests span brain network dynamics in autism/depression, hyperspectral imaging for tumor delineation, and camera-based physiological monitoring. She teaches courses like 'Image Analysis for Healthcare Technologies' and 'Medical Image Processing'. Awards include 2nd place in the CAMELYON17 challenge for metastasis detection. Supervises 32 research projects, collaborating with clinical and industrial partners globally. Her labs include the Biomedical Diagnostics Lab and Signal Processing Systems group. Current work explores biomarkers for accelerated cognitive aging in epilepsy and AI-driven tools for neurosurgery. Active in international collaborations across Europe and beyond.

• Computer Vision
• Sports Analytics
• Machine Learning
• +۳ مورد دیگر

Mehrnaz Fani is an Adjunct Assistant Professor specializing in computer vision and sports analytics. Her research focuses on developing advanced algorithms for player tracking, rink localization, and event recognition in sports videos, particularly in ice hockey and soccer. She employs deep learning techniques and multi-task learning frameworks to enhance video analysis accuracy. Her work emphasizes applications such as puck localization, jersey number recognition, and pose estimation of athletes. Fani's contributions bridge the gap between computer vision and sports science, enabling automated analysis of broadcast sports content. Notable research trends include the use of neural networks (e.g., Hourglass, BendedNet) for geometric and pose estimation tasks, as well as parallel feature fusion for video structure analysis. Her methodologies often integrate temporal analysis and data augmentation to improve model robustness. Despite no listed awards, her publications reflect a strong focus on practical sports video analysis challenges. She has advised no documented students and no grants are mentioned. Her work is part of a broader effort to automate sports data extraction and enhance fan engagement through advanced video processing.

• Labour Economics
• Behavioural Economics
• Organizational Behavior
• +۳ مورد دیگر

Dr. Arjan Non is an Assistant Professor at the Department of Economics, Erasmus School of Economics, Erasmus University Rotterdam. His research focuses on the effects of monetary/non-monetary incentives on employee motivation, incorporating behavioral economics principles such as personality traits and social preferences. He also examines peer effects on academic achievement and the role of corporate mission in job preferences. Key research interests include labor economics, behavioral economics, organizational behavior, and public policy experiments. His work employs experimental methods, including field experiments and stated-preference analyses, to explore topics like risk-taking, employee recognition, and STEM workforce dynamics. Recent studies highlight Dutch voters’ openness to policy experiments and the influence of peer personalities on academic performance. No notable scientific awards are listed. His research has been featured in media, such as a study on policy experiments with Prof. Robert Dur. No advising or grants are explicitly mentioned.

Annika Betkenمشاهده پروفایل

استادیار

• Long Range Mathematics
• Stochastic Volatility
• Simulation Study
• +۳ مورد دیگر

Annika Betken is an Assistant Professor in Mathematics of Operations Research. Her research focuses on advanced statistical methodologies including long-range dependence analysis, stochastic volatility modeling, and machine learning applications. Notable contributions include work on two-sample tests using Wilcoxon rank sums and label shift adaptation in neural networks. She actively engages in academic discourse through conference presentations and publications. Her recent research explores classification scenarios using kernel logistic regression and depth pattern analysis in time series. Betken has delivered invited talks on topics like class probability matching and change-point detection in time series analysis. Her work has been featured in peer-reviewed conferences and journals, contributing to 65 citations and an h-index of 5 as of 2025. Betken has collaborated internationally and contributed to the NAW article in February 2022. Her research spans theoretical advancements in statistical testing and practical implementations in adaptive machine learning systems.

• Pattern Recognition
• Artificial Neural Networks
• Expert Systems
• +۷ مورد دیگر

Maher Ahmed is an Associate Professor at the Faculty of Science, Wilfrid Laurier University. His research focuses on pattern recognition, artificial neural networks, and expert systems. He has contributed to diverse fields including robotics, medical informatics, network security, and computer vision. Key research interests include developing algorithms for shape representation, improving network anomaly detection, and applying machine learning to financial fraud prevention. His work bridges theoretical computer science with practical applications in healthcare and autonomous systems. Publications span robotics localization, medical literature reviews, encryption techniques, and sign language recognition, reflecting a cross-disciplinary approach. He currently holds no listed awards but maintains an active research agenda with a focus on applied artificial intelligence.

• Biomedical Imaging
• Optical Coherence Tomography
• Ophthalmic Diagnostics
• +۲ مورد دیگر

Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.