جستجوی استاد راهنمای دانشگاهی

Hans-Jörg Schulzمشاهده پروفایل

دانشیار

• Visual Analytics
• Information Visualization
• Graph Visualization
• +۴ مورد دیگر

Hans-Jörg Schulz serves as an Associate Professor in the Department of Computer Science at Aarhus University, Denmark, specializing in visual analytics and information visualization. His research bridges computer science with interdisciplinary applications in food science, neuroscience, and material engineering. He earned his Doctorate in Computer Science (2010) and Diplom (Master's equivalent, 2004) from the University of Rostock, focusing on explorative graph visualization and visual data mining for complex structures. His academic trajectory reflects deep expertise in transforming complex data into actionable visual insights through innovative methodological frameworks. Schulz's research centers on advancing visual analytics methodologies, particularly progressive visual analytics where data processing and user interaction occur simultaneously. His work establishes foundational techniques for visual guidance systems, explainable AI interfaces, and specialized visualization tools for domains like EEG analysis and food rheology. Recent publications demonstrate a strategic expansion into haptic feedback integration, agent-based visualization design, and cross-disciplinary applications requiring novel visual fingerprinting techniques. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Human-centered progressive analytics with focus on trust calibration and cognitive load management, (2) Domain-specific visualization frameworks for complex data like anisotropic food structures and EEG artifacts, and (3) Novel interaction paradigms incorporating force feedback and malleable interfaces. His work consistently emphasizes practical usability while pushing technical boundaries in visual representation. Schulz's contributions have been recognized with significant awards including IEEE InfoVis Best Poster (2010), EuroVis 3rd Best Paper (2012), VDA Best Paper (2015), Cybercartography Competition 1st Place (2022), and IEEE SciVis Contest 2nd Place & Most Innovative Work (2023). These accolades highlight both theoretical innovation and practical impact in the visualization community. As an educator, he supervises PhD candidates and teaches core visualization courses including Data Visualization, Information Visualization, and Visual Analytics. His current ArtiPlex project (2024-present) develops multiplex analytics for EEG artifact detection, securing active research funding while demonstrating his commitment to translating visualization research into domain-specific solutions. Collaborative patterns across his 79 publications indicate strong partnerships with food scientists, neuroscientists, and HCI researchers.

Rune Dodensig Kjærsgaardمشاهده پروفایل

پژوهشگر

• Data Representation
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Rune Dodensig Kjærsgaard serves as a Consultant in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), with office location at Richard Petersens Plads, Building 324, 2800 Kgs. Lyngby. He completed his PhD at DTU in January 2024 under main supervisor Line Clemmensen, following a research trajectory focused on interdisciplinary machine learning applications. His professional profile integrates computer science with astronomy and maritime engineering, positioning him as an emerging researcher in explainable and domain-specific AI systems. His research program centers on Data Representation and Machine Learning, with specialized expertise in Neural Networks, Anomaly Detection, and Clustering. Key contributions include the TAU framework for telluric correction in astronomical spectroscopy, self-explainable autoencoders for maritime anomaly detection (SEAuAIS), and fair soft clustering algorithms. He addresses critical challenges in making AI systems interpretable while maintaining performance, particularly for observational data with high noise levels in astronomy and maritime contexts. His work consistently bridges theoretical machine learning advancements with practical domain applications. Analysis of his 7 publications (2023-2025) reveals a strong interdisciplinary trajectory: 30% in astronomy applications (e.g., solar spectra analysis), 20% in maritime security, and 50% in core machine learning methodology. Key thematic trends include explainability in deep learning systems, robust anomaly detection for sparse data, and fairness-aware clustering. His recent publications in Ocean Engineering (2025) and Astronomy & Astrophysics (2023) demonstrate successful translation of methods across domains. No scientific awards are documented, but his PhD project 'Extracting Essential Information and Making Inference from Data' (2020-2024) established his research foundation. Current work appears supported through his DTU consultant role and collaborative projects, with evidence of international co-authorship across multiple institutions. As a recent PhD graduate, he does not yet supervise students but maintains active research collaborations. Prospective collaborators should note his focus on practical AI implementations with domain-specific constraints and strong publication momentum in top venues (AAAI, AISTATS).

Esmaeil Nadimiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Esmaeil Nadimi is a Professor and Head of Unit at The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, where he leads the Applied AI and Data Science research unit. He holds multiple concurrent positions including Visiting Professor at Harvard University, Chair of Data Analytics at SDU eScience Center, and research roles with Innovationsfonden and Swiss National Science Foundation (SNSF) in Medicine & Biology and Health & Well-being domains. His educational background includes a Ph.D. in Electrical and Computer Engineering from Aalborg University (2008) and an M.Sc. in Electrical Engineering (Control Theory) from Sharif University of Technology (2004). Research focuses on foundational AI methodologies including Physics-Infused Learning, Meta & Transfer Learning, with applied research in healthcare (medical imaging, cancer detection, diabetic neuropathy), energy systems, and optical technologies. Core expertise spans machine learning, computer vision, statistical modeling, and medical AI systems development. Publications demonstrate strong emphasis on AI applications in medical diagnostics, particularly capsule endoscopy, cervical/colorectal cancer detection, and diabetic complication screening. Recent work increasingly addresses explainable AI frameworks and practical implementation pathways for clinical adoption. Leadership includes supervision of industrial PhD/postdoc programs and development of academic courses in AI/Data Science. Current grants involve Innovationsfonden and SNSF-funded projects. Leads multiple research teams focused on medical AI systems at SDU Applied AI and Data Science unit.

• Artificial Intelligence
• Medical Robotics
• Embedded Systems
• +۵ مورد دیگر

Jan-Matthias Braun is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. His research bridges artificial intelligence, robotics, and medical device engineering. Current projects focus on explainable AI integration in colon capsule endoscopy Development of real-time FPGA-based systems for colorectal diagnostics Biomechanical modeling for adaptive orthotic devices His work emphasizes cross-disciplinary applications of machine learning in healthcare, particularly for gastrointestinal disease detection and assistive robotics. Publications demonstrate expertise in deep neural networks, hardware acceleration, and smart environment control systems. Teaching responsibilities include: Advanced cybersecurity courses Deep learning applications in epilepsy detection Mentorship in capsule endoscopy image analysis

• Natural Language Processing
• Artificial Intelligence
• Explainable AI

Yingming Wang is a Lecturer in the Department of Computer Science at the University of Copenhagen, located at Universitetsparken 1, 2100 København Ø. His research focuses on Natural Language Processing and Artificial Intelligence, particularly developing methods for trustworthy AI explanations. Wang's sole recent publication (2025) centers on improving faithfulness in AI-generated explanations through self-critique techniques, indicating a research emphasis on transparent and reliable NLP systems. No awards, supervised students, or team collaborations are documented.

• Computational Linguistics
• Corpus Linguistics
• Lexicography
• +۳ مورد دیگر

Sussi Olsen is an Academic Research Staff member (Researcher) in the Department of Nordic Studies and Linguistics at the University of Copenhagen, employed at the Center for Sprogteknologi (CST) since 1997. Her work centers on developing and validating linguistic resources for language technology applications. Education: Masters degree in Spanish and Computational Linguistics, University of Copenhagen Her research spans technological language resources, morphological/syntactic/semantic annotation, computational lexicography, corpus linguistics, and resource validation. She specializes in Danish language processing and EU-level language technology infrastructure, with expertise in historical Danish (19th century) and multilingual resource integration. Her work bridges theoretical linguistics and practical NLP applications. Recent publications (2023-2025) reveal a strategic focus on semantic resource development for Danish, including formal lexicons (COR.SEM), language model evaluation frameworks, and sign language-spoken language integration. These works demonstrate convergence between lexical semantics, benchmarking methodologies, and European language equality initiatives, particularly through EU-funded projects like the European Language Equality agenda. She actively contributes to EU language technology projects including the Central Word Register for Danish (COR), European Language Grid, Federated TermBank, and ELEXIS. Her project portfolio shows sustained engagement with terminology management, semantic annotation standards, and resource validation since 2008. She has presented extensively on historical Danish language processing and semantic annotation methodologies. Olsen is affiliated with the Center for Sprogteknologi and participates in the DigHumLab project. She holds governance roles as Member of the Board of Representatives and Board of Directors of the Danish Language Council, and formerly chaired the Society of Lexicographers in Denmark.

Frans van der Sluisمشاهده پروفایل

دانشیار

• human-information interaction
• human interest and curiosity
• epistemic emotions
• +۴ مورد دیگر

Frans van der Sluis serves as an Associate Professor in the Department of Communication at the University of Copenhagen, where he conducts interdisciplinary research at the intersection of human cognition and information systems. His work focuses on designing search and filtering technologies that align with human learning processes and curiosity-driven exploration. Education: BSc in Psychology (2006) BSc in Business Information Technology (2006) MSc in Psychology (2008) PhD in Interactive Information Retrieval and Filtering (2013) Van der Sluis investigates how information systems can be optimized to transform raw data into meaningful knowledge through understanding epistemic emotions, user uncertainty, and cognitive processing during information interactions. His methodology frequently incorporates eye-tracking and empirical user studies to measure comprehension, interest, and engagement beyond traditional accuracy metrics. Analysis of his recent publications (2023-2025) reveals a consistent trajectory toward conversational approaches in information quality assessment, with growing applications in sustainability domains like wind energy training networks and ethical consumer behavior. His work bridges theoretical HCI frameworks with practical implementations across diverse contexts from social media to specialized research environments. No scientific awards were documented in the source materials. While specific advising details remain unspecified, his collaborative projects—including the EU-funded Train2Wind initiative for offshore wind power researchers—demonstrate active grant management and cross-institutional leadership. His professional network spans major European universities and industry research centers. Van der Sluis maintains active collaborations with University of Twente, Glasgow University, Leiden University, and industrial partners including Philips' Innovation Lab, reflecting his position at the nexus of academic research and real-world information system design.

• Cardiology
• Artificial Intelligence in Medicine
• Electrocardiogram Analysis
• +۳ مورد دیگر

Vajira Lasantha Bandara Thambawita serves as an External Researcher in the Department of Biomedical Sciences at the University of Copenhagen's Faculty of Health and Medical Sciences, focusing on the physiology of circulation, kidney, and lung with specialized expertise in AI-driven cardiovascular diagnostics. His research interests include: Development and validation of AI systems for cardiology Electrocardiogram (ECG) analysis using deep learning techniques Medical data privacy implications of generative models Explainable AI for clinical decision support Retinal image analysis for cardiovascular risk prediction Privacy-preserving methods in medical AI Analysis of his 2021-2025 publications reveals a critical focus on AI validation frameworks in cardiology, with significant contributions to ECG interpretation explainability and retinal image-based cardiovascular diagnostics. His work consistently addresses the tension between AI innovation and clinical safety, particularly regarding privacy vulnerabilities exposed by synthetic medical data generation. Dr. Thambawita operates within the University of Copenhagen's cardiovascular research ecosystem, contributing to interdisciplinary projects that bridge computational science and physiological medicine through the Heart, Renal and Circulation research group.

Mario Lovricمشاهده پروفایل

پژوهشگر

• Food Microbiology
• Gut Health
• Fermentation
• +۳ مورد دیگر

Mario Lovric is a Guest Researcher in the Department of Food Science at the University of Copenhagen's Faculty of Science, specializing in Food Microbiology, Gut Health, and Fermentation. His research integrates machine learning approaches with biochemical and microbiological investigations across multiple interdisciplinary domains. His primary research areas include: Food Microbiology and Gut Health Metabolomics and Metabolite Bioactivity Machine Learning applications in Chemistry and Medicine Indoor Air Quality and Fungal Communities Early Childhood Inflammation and Developmental Outcomes Lovric's publication record demonstrates a consistent focus on applying advanced computational methods to solve complex problems in food science and microbiology. His recent work shows increasing emphasis on explainable AI techniques for analyzing biological systems, particularly in mother-child metabolome transfer and childhood health outcomes. The publications reveal strong international collaboration patterns across medical, biochemical, and computational disciplines. Several of Lovric's publications have received significant attention in the scientific community, with some picked up by multiple news outlets and widely shared on academic platforms like Mendeley. His work on molecular representation methods has accumulated 21 Scopus citations, indicating substantial impact in the field of chemical informatics.

• Big Data Psychology
• Machine Learning
• Explainable AI
• +۵ مورد دیگر

Rosa Ellen Lavelle-Hill serves as an Assistant Professor of Social Data Science and Psychology within the Faculty of Social Sciences at the University of Copenhagen since March 2023. Her academic appointments include postdoctoral research at the University of Tübingen (Germany) and the Alan Turing Institute (UK). She holds a PhD in "Big Data Psychology" from the University of Nottingham. PhD: Big Data Psychology, University of Nottingham Postdoc: University of Tübingen, Germany Postdoc: Alan Turing Institute, UK Her research integrates psychological theory with big data and machine learning methodologies to analyze human behavior in social good contexts. Key focus areas include sustainable development, environmental psychology, health applications, and migration studies. She specializes in Explainable AI techniques that challenge and refine existing social science theories through data-driven insights. Her methodological expertise spans longitudinal machine learning, meta-analysis of educational outcomes, and analysis of non-traditional data sources like mobile money records. Recent publications demonstrate consistent application of machine learning to educational psychology (2024-2025), migration risk assessment (2022), and consumer behavior (2020). Her work shows increasing emphasis on ethical AI applications in social science with publications in high-impact journals including Psychological Bulletin , PNAS , and Journal of Educational Psychology . Research collaborations span Tanzania, Germany, and the UK, with particular focus on urban migration risks and educational data science. Her work has been referenced in policy sources and gained significant attention across academic social media platforms including X and Bluesky.

• Computational Chemistry
• Cheminformatics
• Quantum Chemistry
• +۵ مورد دیگر

Maria Harris Rasmussen is a Postdoctoral Researcher in the Department of Chemistry at the University of Copenhagen, where she conducts cutting-edge research at the intersection of computational chemistry, cheminformatics, and artificial intelligence. Her work focuses on developing and applying computational methodologies to solve complex chemical problems, with particular emphasis on reaction discovery, molecular representation, and catalyst design. Her research interests span multiple domains of computational chemistry, with primary focus on Cheminformatics where she develops algorithms for molecular representation including SMILES notation for transition metal complexes. In Quantum Chemistry , she investigates photoinduced electron transfer processes and reaction mechanisms using advanced simulation techniques. Her work in Machine Learning for Chemistry includes developing explainable AI methods for molecular property prediction and uncertainty quantification in chemical data sets. She also contributes significantly to Catalysis Research through computational approaches for de novo catalyst discovery and reaction screening. Analysis of her publication record reveals strong trends in developing computational tools that bridge theoretical chemistry with practical applications. Her recent work shows increasing integration of machine learning with traditional quantum chemical methods, particularly in the areas of reaction space exploration and catalyst discovery. The interdisciplinary nature of her research connects chemistry with computer science, physics, and data science, reflecting the evolving landscape of modern computational chemistry. Maria maintains active research collaborations with prominent scientists including Jensen J.H., Mikkelsen K.V., and several international researchers as evidenced by her publication record. Her computational methodologies have gained attention across academic and research communities, with multiple publications receiving significant readership on platforms like Mendeley and social media engagement.

• Epidemiology
• Public Health
• Infectious Disease Modeling
• +۳ مورد دیگر

Helen Louise Coupland is a Guest Researcher at the Department of Public Health, University of Copenhagen, affiliated with the Section of Epidemiology and Copenhagen Health Complexity Center. Her work focuses on computational epidemiology and public health research using advanced modeling techniques. Her research spans multiple domains: Epidemiological Modeling: Developing computational frameworks for infectious disease transmission Public Health Analytics: Applying data science to health policy evaluation AI Applications: Utilizing deep learning for longitudinal health analysis Complex Systems: Investigating health through complexity science approaches Her publication record demonstrates extensive work in pandemic response analysis, particularly COVID-19 modeling across multiple countries and populations. Recent research explores the intersection of artificial intelligence and life course epidemiology, indicating a methodological evolution toward more complex computational approaches in public health. Her collaborative works frequently appear in high-impact journals including Science, Nature Communications, and The Lancet Global Health. Helen contributes to the Copenhagen Health Complexity Center, focusing on systems approaches to public health challenges. Her work involves large-scale collaborative projects analyzing epidemiological patterns across different geographic and demographic contexts.