Find Academic Supervisors

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +7 more

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

• Computer Vision
• 3D Reconstruction
• Image Processing
• +3 more

Margrit Gelautz is an Associate Professor at TU Wien, leading the research group on Image and Video Analysis & Synthesis. She holds a PhD from Graz University of Technology (1997) and conducted postdoctoral research at Stanford University's Radar Interferometry Group. Her work focuses on 3D reconstruction, stereo analysis, and applications in autonomous driving and robotics. She directs the Doctorate College 'TrustRobots' and has pioneered projects like the AlphaMatting.com evaluation system. Key roles include heading the 'Precision Livestock Farming' unit and serving as a university lecturer in Computer Vision. Education: PhD in Computer Science, Graz University of Technology (1997) Postdoctoral Research, Stanford University (1999–2001) Research Interests: 3D scene reconstruction, stereo analysis, image matting, autonomous driving systems, and robotics. Her group develops tools for 3D film production, novel view synthesis, and real-time depth estimation using structured light. Current projects include CarVisionLight (autonomous driving vision systems) and Precise3D (cost-efficient 3D scanning workflows). Grants & Awards: Interactive TV Award 2009 Microsoft Research Stipend 2007 Funding for 10 PhD positions in the 'TrustRobots' Doctorate College Labs & Teams: Her research group collaborates on EU initiatives like 3D-ConTourNet and maintains the AlphaMatting.com platform. Projects span dynamic vision sensors, event-based stereo systems, and semantic SLAM for robotics.

• Combustion Diagnostics
• Laser-induced Incandescence
• Thermometry
• +3 more

Qian Wang is a Researcher at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Their research focuses on combustion diagnostics, laser-based measurement techniques, and advanced imaging systems for fluid dynamics analysis. Key areas include soot characterization, flame tomography, and the development of cost-effective diagnostic tools. Research interests encompass combustion kinetics, thermometry, and particle dynamics, with applications in propulsion systems and material science. Recent work emphasizes the use of machine learning and deep learning for data-driven diagnostics, particularly in turbulent combustion environments. Publications highlight innovations in volumetric reconstruction, time-resolved imaging, and optimization of optical systems under constrained conditions. No scientific awards are mentioned in the provided text. Qian’s work involves collaboration with engineering teams to advance combustion diagnostics through interdisciplinary approaches, though specific grants or advising roles are not detailed here.

• Algorithms
• Data Structures
• String Algorithms
• +3 more

Panagiotis Charalampopoulos is a Lecturer (Assistant Professor) in the Department of Informatics at King's College London, part of the School of Computing and Mathematical Sciences. He holds a PhD in Computer Science from King's College London, an MSc in Advanced Computing from the same institution, and a BA in Mathematics from the University of Cambridge. Previously, he was a Lecturer at Birkbeck, University of London and a postdoctoral fellow at Reichman University and the University of Haifa. His research focuses on algorithms and data structures, particularly string algorithms (e.g., pattern matching, indexing) and planar graph algorithms (e.g., shortest path computations). Applications of his work include bioinformatics, text mining, and medical imagery analysis. He teaches modules such as 'Software and Programming I' and 'Problems in Mathematics.' His research has produced impactful contributions in both theoretical and applied domains, addressing challenges in efficient algorithm design and practical implementations. His work often bridges foundational computer science principles with real-world applications. Dr. Charalampopoulos' publications span topics like resilient pattern mining, efficient string processing, and planar graph optimization, reflecting his dual focus on algorithmic efficiency and practical utility. His academic journey includes research internships at the University of Warsaw and IDC Herzliya, highlighting his international collaboration and expertise in computational theory.

• Data Mining
• Machine Learning
• Computational Sustainability
• +5 more

Stefan Kramer is a Full Professor at the Institute of Computer Science, Johannes Gutenberg University Mainz. He holds leadership roles including Spokesperson of the DeCoDeML network and involvement in initiatives like the Plattform Lernende Systeme. His research focuses on data mining, machine learning, computational sustainability, and bioinformatics, with over 200 peer-reviewed publications. Kramer has supervised numerous theses and secured significant funding from EU, DFG, BMBF, and industry. Awards include Best Paper recognitions at KDD, ICDM, and ICBK conferences. He actively contributes to academic governance and interdisciplinary collaborations, leading the Institute of Computer Science from 2016–2018. His work emphasizes privacy-preserving techniques, interpretable models, and applications in social good. Education: PhD in Technical Sciences (Dr. techn.) from TU Wien (1999) Positions at Austrian Research Institute for Artificial Intelligence, Siemens AG, and TU München (2003–2011). Research Interests: Kramer’s work spans structured data mining (sequences, graphs), privacy-aware algorithms, computational sustainability, and bioinformatics applications. Notable projects include CheS-Mapper for chemical data visualization and enviPath for biodegradation pathway prediction. Grants & Collaborations: Over 30 projects funded by EU, DFG, BMBF, and industry. Key collaborations include OpenTox Euro, the Predictive Toxicology Challenge, and interdisciplinary networks like the Rhein-Main Universities’ DeCoDeML initiative. Labs/Teams: Leads the Data Mining Group at Mainz, contributing to tools like CheS-Mapper and enviPath. Active in organizing conferences (ECML/PKDD 2021) and editorial roles for Machine Learning journals.

Ekaterina A RapinchukView profile

Assistant Professor

• Machine Learning
• Graph-Based Algorithms
• Data Classification
• +5 more

Ekaterina A Rapinchuk is an Assistant Professor in the Departments of Mathematics and Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. Her research focuses on developing graph-based algorithms and spectral methods for machine learning applications, particularly in small data classification, molecular science, and hyperspectral imaging. She holds a joint appointment across two departments, reflecting her interdisciplinary work at the intersection of mathematics and computational science. Research Interests : Graph algorithms for data classification and clustering Spectral graph theory and Laplacian-based methods Machine learning for molecular and biomedical data Optimization in high-dimensional spaces Applications of topological data analysis Publications Trends : Recent work emphasizes addressing data scarcity through graph-based techniques, integrating transformer networks with spectral methods, and correcting labeling errors in training datasets. Her research bridges theoretical mathematics with practical applications in sensor data analysis, chemical informatics, and biomedical imaging. Grants & Advising : While specific grant details are not listed, her work aligns with NSF-funded research areas in computational mathematics and data science. No advising information is currently available.

• Extreme Multi-label Classification
• Machine Learning
• Algorithms
• +3 more

Erik Schultheis is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. His research focuses on extreme multi-label classification, algorithm design, and optimization challenges in machine learning. He holds a Master of Science in Physics from Georg-August-Universität Göttingen (2019). Key research interests include scalable classification systems, label correlation learning, and addressing long-tail and missing label problems in large-scale datasets. His work emphasizes practical solutions for commodity hardware limitations and memory efficiency in training models with millions of labels. Notable contributions include the Dismec++ software tool for extreme classification, and he has presented at conferences such as NeurIPS, ICML, and KDD. His 2021 NeurIPS Outstanding Reviewer Award highlights his peer-review contributions to the field. Recent publications explore dynamic sparsity in large output spaces, online algorithm generalizations, and label calibration in extreme classification scenarios. His research bridges theoretical algorithm development with practical implementation challenges in real-world systems.

David SchinaglView profile

Researcher

• Computer Vision
• Autonomous Driving
• 3D Object Detection
• +3 more

David Schinagl is affiliated with the Institute of Visual Computing at TU Graz. His research focuses on LiDAR-based 3D object detection , explainable AI , and autonomous driving technologies . He has contributed to methods like OccAM's Laser for model interpretability and MAELi for large-scale LiDAR processing. His work emphasizes sensor data analysis and geometric confidence enhancement. Publications span top venues including CVPR, ICCV, and WACV (2018–2024). His GitHub repository demonstrates implementations like the OccAM's Laser demo for LiDAR attribution maps.

• web mining
• social mining
• recommender systems
• +2 more

Dr. Le Duc Trong is an Assistant Professor (equivalent to Lecturer) at the University of Engineering and Technology (UET), Vietnam National University (VNU) in Hanoi, where he currently serves as Head of the Computer Science Department . He holds a PhD from Singapore Management University (2019) and a Bachelor's in Information Technology from VNU-UET (2011). He leads the Reliable Machine Learning research group , focusing on AI robustness and real-world applications. His research spans web/social mining, recommender systems, multimodal learning, and reliable ML , with emphasis on fairness-aware algorithms, emotion recognition, and healthcare AI. Recent work integrates graph networks, causal inference, and curriculum learning to address challenges like modality imbalance and catastrophic forgetting. Publication analysis (2019-2025) reveals three dominant themes: (1) Multimodal emotion recognition (5+ papers), advancing fusion techniques for conversational/clinical settings; (2) Reliable recommendation systems addressing fairness, causality, and user intent modeling; (3) Medical/healthcare AI including bronchoscopy datasets and mental disorder detection frameworks. His work consistently appears in premier venues (e.g., ACM MM, IJCAI, AAAI). Awards & Service: IJCAI Student Travel Award (2017) Program Committee: AAAI, IJCAI, PAKDD (2020-2022) Co-Session Chair: KSE 2023 Administrative Roles: Head, Computer Science Department (2022–present) Deputy Head, Computer Science Department (2022)

Galadrielle Humblot-RenauxView profile

Research Fellow

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +4 more

Galadrielle Humblot-Renaux is a Research Fellow at Aalborg University's Technical Faculty of IT and Design, affiliated with the Department of Architecture, Design and Media Technology and the Section for Media Technology in Aalborg, Denmark. Her work focuses on AI-driven solutions for computer vision, robotics, and uncertainty quantification in machine learning systems. Key Research Areas Out-of-Distribution Detection and Robustness Testing 3D Semantic Segmentation and Point Cloud Processing Uncertainty Quantification in Renewable Energy Systems Human-Robot Interaction and Speaker Identification Marine Ecology Image Analysis via Multi-Annotator Datasets Scientific Contributions She has created two influential datasets: JAMBO (2024) for underwater benthic habitat classification and Why Talk to People When You Can Talk to Robots? (2021) for far-field speaker identification challenges. Her publications across 2018-2025 demonstrate interdisciplinary expertise bridging AI theory with practical applications in robotics, automotive systems, and ecological monitoring.

Ning ZhangView profile

Professor

• Edge Computing
• Internet of Vehicles (IoV)
• Cybersecurity
• +7 more

Prof. Ning Zhang is a tenured Professor in the Department of Electrical and Computer Engineering at the University of Windsor's Faculty of Engineering. He holds a Tier 2 Canada Research Chair in Edge Computing and the Internet of Vehicles (since 2022) and has been recognized as a 2024 Clarivate Highly Cited Researcher. His research focuses on edge computing, vehicular networks, cybersecurity, federated learning, and 6G systems. He is affiliated with the Centre for Engineering Innovation, where cutting-edge labs were recently established under his leadership. Dr. Zhang's academic achievements include membership in the Royal Society of Canada's College of New Scholars (2024) and the IEEE Rising Star Award (2021). His work bridges theoretical innovations with practical applications in autonomous systems, digital twin technology, and metaverse infrastructure. He advises students like Pegah Mansourian and Mina Zamanirafe, whose research on automotive cybersecurity won international acclaim. His lab explores edge computing optimizations for low-light video analytics, federated learning frameworks, and secure vehicular communication systems. Key Research Themes: V2X security, digital twins, 6G edge infrastructures, AI-driven network optimization Lab Affiliations: Centre for Engineering Innovation Grants: Canada Research Chairs Program funding, NSERC grants (implied via research outputs)

Jacob WoodView profile

Associate Professor

• International Trade Negotiation
• Non-Tariff Barriers in Trade
• Financial Performance Analysis
• +5 more

Associate Professor Jacob Wood serves as Associate Dean of Research at James Cook University (Singapore Campus) and specializes in international trade negotiation, non-tariff barriers, and employee engagement in HRM. He holds a PhD in International Studies from Sogang University, a Master of Management (Banking) from Massey University, and dual Bachelor degrees from Otago University. Doctor of Philosophy (PhD), Sogang University Master of Management (Banking), Massey University Bachelor of Commerce (Management), Otago University Bachelor of Tourism, Otago University Graduate of Australian Institute of Company Directors (GAICD) His research examines the intersection of economic development and sustainability in the Tropics, with a focus on trade policy impacts, digital transformation in tourism, and carbon emission mitigation. His recent work includes studies on low-carbon tourism models, blockchain applications in fishery supply chains, and trade war effects on global value chains. Key trends in his publications include: Trade Policy Analysis (US-China, Belt and Road Initiative) Sustainable Tourism and Carbon Pricing Technology Adoption in Logistics Digital Transformation in Manufacturing Environmental Resilience in Developing Economies Trade Facilitation and Economic Growth Previously, he held academic roles at Korea University of Technology and Education and Chungnam National University (South Korea). His work appears in journals such as Journal of Cleaner Production , Sustainability , and World Trade Review .

• Computer Vision
• Medical Imaging
• Biometrics
• +3 more

Dr. Bryan Williams is a Senior Lecturer in the Department of Computing and Communications at Lancaster University. His research focuses on advanced computer vision techniques applied to medical imaging and biometric identification. Key areas include AI-driven glaucoma diagnosis, hand-based biometric systems, and optical coherence tomography (OCT) applications in healthcare. He leads projects like H-Unique, exploring anatomical hand variation for forensic identification, and has contributed to improving pharmaceutical quality control using terahertz imaging. Williams has authored over 60 publications, with recent works addressing knuckle recognition, glaucoma detection via fundus imaging, and continual learning in computer vision. His work bridges clinical medicine and engineering, emphasizing real-world applications. Awards include the FHLS Public Engagement Award and Staff Award for Outreach, reflecting his commitment to public science engagement. He collaborates widely, engaging in conferences and workshops on AI in healthcare and forensic biometrics. Williams supervises PhD students Wayil Alanazi and Zhaonian Zhang, and is affiliated with research groups like the Artificial Intelligence Digital Health Group and the Lancaster Intelligent, Robotic and Autonomous Systems Centre. His research spans medical diagnostics, biometric security, and advanced imaging technologies, with grants supporting projects until 2024.

Thomas PockView profile

Professor

• Computer Vision
• Image Processing
• Mathematical Optimization
• +7 more

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

Urszula Markowska-KaczmarView profile

Professor

• Deep Learning
• Machine Learning
• Neural Networks
• +4 more

Prof. Urszula Markowska-Kaczmar is a distinguished academic at the Faculty of Information and Communication Technology , Wroclaw University of Science and Technology , where she has been active since 1988. Her research focuses on deep learning , neural networks , and computational intelligence , with applications in computer vision , medical diagnostics , and natural language processing . Current role: Full Professor in Department of Artificial Intelligence Key collaboration: Organized Poland's first deep learning workshop DArViN2015 with speakers like Ruslan Salakhutdinov Editorial work: Guest editor for Applied Artificial Intelligence (2014) and managing editor for International Journal of Computational Intelligence Research (2007-2008) Her research explores neural network interpretability through evolutionary algorithms, semi-supervised learning frameworks, and multi-modal data analysis . She pioneered deep learning applications in Poland and maintains active involvement in international conferences like IEEE IJCNN and ACIIDS . The 15 most recent publications reveal trends in medical imaging analysis (2025), generative adversarial networks (2022), style transfer (2021), and foundational neural architecture comparisons (2015). Her work balances theoretical advancements in learning algorithms with real-world implementations in healthcare and document analysis. Key grants include: Framework for Visual Information Retrieval (2008-2011) Adaptive Problem-Solving System (POIG.01.01.02-14-013/09-00, 2010-2013) ENGINE European Research Centre (2013-2015) She supervises PhD and master's students in neural network implementations and maintains active program committee memberships for conferences like IEEE IJCNN and ICPRAM . Her lab focuses on deep learning for healthcare and intelligent document systems .