جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۳ مورد دیگر

Ana Valeria Gonzalez is a researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning with a focus on human-centered natural language processing. Her work bridges computational linguistics and cognitive science within the department's Machine Learning research section. Her research investigates bias mitigation in multilingual systems, interpretable AI evaluation frameworks, and affect-aware dialogue generation. Key contributions include developing testbeds for gender bias analysis in coreference resolution, novel methodologies for evaluating model interpretability through reverse Turing tests, and retrieval-based approaches for goal-oriented conversational agents. She employs techniques spanning attention mechanisms, BiLSTMs, and domain adaptation to address challenges in low-resource NLP settings. Analysis of her 2019-2021 publications reveals a cohesive research trajectory toward building transparent, equitable NLP systems that account for human cognitive factors. Her work consistently addresses real-world deployment challenges, particularly in dialogue systems requiring emotional intelligence and cross-lingual fairness. Collaborations with Anders Søgaard, Isabelle Augenstein, and other Copenhagen researchers demonstrate strong integration within the university's AI ecosystem. Gonzalez completed her Ph.D. in 2021 with the dissertation "Towards Human-Centered Natural Language Processing". She maintains active research in the Machine Learning group, though specific advising roles and grant details aren't documented in the available materials. Her publication record shows significant impact with multiple Scopus-cited works and substantial reader engagement across platforms.

Qiwei Pengمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Multilingual NLP
• Cross-lingual Understanding
• +۳ مورد دیگر

Qiwei Peng is a Postdoctoral Researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in natural language processing with emphasis on multilingual applications and cross-lingual understanding. His research spans semantic similarity across languages, multilingual concept understanding, and practical implementations in diverse linguistic contexts. Key contributions include advancing multilingual large language model alignment, developing code generation benchmarks, and analyzing tokenization vulnerabilities. Analysis of Peng's 2024 publications reveals concentrated efforts on multilingual NLP challenges, particularly concept space alignment in LLMs, cross-lingual code generation, and subword robustness. His collaborative work extends to multimodal cultural understanding through datasets like FoodieQA for Chinese food culture. Peng actively contributes to the NLP community through publications in premier venues including EMNLP and LREC-COLING, with research networks spanning multiple international institutions as indicated by his co-authorship patterns.

Bjarne Kjær Ersbøllمشاهده پروفایل

استاد

• Applied Statistics
• Data Analysis
• Image Analysis
• +۴ مورد دیگر

Bjarne Kjær Ersbøll is a Professor in Statistics and Data Analysis at the Department of Applied Mathematics and Computer Science, DTU Compute, Technical University of Denmark (DTU). He has held academic positions since 1983, progressing from Research Assistant (1983-1986) to Full Professor (2010-present). His research focuses on applied statistics, data analysis, and their applications in industrial and medical projects, emphasizing collaborative solutions with industry and institutions. He holds M.Sc. (1983) and Ph.D. (1990) degrees from DTU. Key research interests include image analysis, multivariate statistics, and big data methodologies. He has supervised numerous master’s and Ph.D. theses and organized conferences on image analysis and statistics. Notable projects include studies on environmental contaminants in food, cardiovascular mortality linked to drinking water magnesium, and interventions in childhood dietary habits. His work contributes to UN Sustainable Development Goals related to health, sustainable cities, and responsible consumption. Recent publications highlight trends in public health (e.g., dietary interventions), environmental science (e.g., PFAS in eggs), and machine learning applications (e.g., deep learning for quality assessment). He actively participates in academic activities, including lecturing on applied statistics and supervising interdisciplinary projects.

Nicolaj Søndberg-Jeppesenمشاهده پروفایل

مدرس پاره‌وقت

• Bayesian Networks
• Cyber-Physical Systems
• Healthcare Informatics
• +۴ مورد دیگر

Nicolaj Søndberg-Jeppesen is a Part-Time Lecturer at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Bayesian networks, cyber-physical systems, and their applications in healthcare, industrial diagnostics, and decision support systems. Key research interests include: Development of Bayesian network models for predictive maintenance and health monitoring in industrial systems Integration of operational data into continuous improvement frameworks Application of probabilistic graphical models in healthcare logistics and emergency management Design of smart manufacturing architectures for diagnostics and predictive maintenance Notable projects include: Animated Computer Games (ongoing since 2010) Extending the scope of graphical models for decision problems (ongoing since 2010) His recent work emphasizes: Patient flow prediction in emergency departments Risk assessment in home care deliveries Object-oriented Bayesian networks for component-level diagnostics Publications span applications in healthcare informatics, industrial engineering, and game theory, with a focus on practical implementations of probabilistic models.

Kai Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Time Series Analysis
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Kai Zhao is a Research Fellow in the Department of Computer Science at the Technical Faculty of IT and Design, Aalborg University, Denmark, specializing in time-series analytics and machine learning applications. Research Interests: Time Series Analysis Machine Learning Data Mining Contrastive Learning Graph Neural Networks Anomaly Detection His recent publications (2023-2025) demonstrate a cohesive focus on graph-based and contrastive learning techniques for time-series challenges, particularly addressing few-shot scenarios, multivariate forecasting, and anomaly detection in IoT contexts. Methodological innovations include dynamic graph modeling and generative contrastive frameworks. Scientific Awards: No awards mentioned in available documentation. As Principal Investigator, he led the project 'Time Series Analyse with Few-shot Learning' (2022-2023), advancing few-shot learning applications for time-series analysis. His collaborative network spans multiple institutions, evidenced by co-authorships with researchers from Aalborg University and international partners. His research integrates theoretical frameworks with practical implementations, showing strong emphasis on robustness and adaptability in real-world time-series applications.

Siavash Arjomand Bigdeliمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Diffusion Models
• +۷ مورد دیگر

Siavash Arjomand Bigdeli is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he conducts research in Visual Computing. His work spans computer vision, machine learning, and AI applications in medical imaging and food science. He is actively involved in interdisciplinary research and supervises multiple PhD projects. Research Interests: Dr. Bigdeli's research focuses on deep learning for visual data, particularly diffusion models, image synthesis, and privacy-preserving AI. He investigates how AI models can be made more robust, explainable, and fair, especially in sensitive domains like healthcare. His work also extends to texture analysis, scale-space theory, and structural modeling in both biological and food materials. The recent publications reveal a strong trend toward diffusion models , membership inference attacks , and counterfactual generation , indicating expertise in both generative modeling and AI security. These works are applied in domains such as medical imaging and computer vision, showing a balance between theoretical innovation and practical impact. Scientific Supervision and Projects: Supervisor in Human-XAI Collaboration for Improved Fetal Ultrasound Imaging (2025–2028) Supervisor in Bias and Fairness in Medical Imaging (2023–2026) Project Participant in EXCHEQUER: Examining Cheese Quality Elements and Reactions (2021–2025) He collaborates closely with researchers such as Aasa Feragen, Anders B. Dahl, and Jeppe R. Frisvad. His lab is part of DTU Compute’s Visual Computing section, contributing to both fundamental algorithms and real-world applications in AI-driven imaging.

Lezhong Wangمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Lezhong Wang is a postdoctoral researcher at the Department of Applied Mathematics and Computer Science (DTU Compute), specializing in Visual Computing at the Technical University of Denmark. His work bridges computational imaging, material science, and generative modeling. Education : Ph.D. in Material Appearance and Perception: Estimating and Generating Properties Across Scales (2025, DTU) Research Interests : Dr. Wang focuses on image synthesis , diffusion models , and noninvasive material characterization . His projects include anisotropy estimation in cheese , stereo image generation , and machine learning applications for optical imaging . Publication Trends : His recent work emphasizes training-free generative models , material anisotropy , and cross-scale property estimation . Themes include computer vision , diffusion-based image synthesis , and machine learning for material analysis . Projects : He completed a Ph.D. project titled Computer Vision for Estimating Microstructure Using Optical Models (2022-2025), supervised by Jeppe Frisvad and Anders Dahl.

Kenichi Ohkiمشاهده پروفایل

استاد

• Visual Neuroscience
• Functional Brain Mapping
• Neural Circuit Development
• +۳ مورد دیگر

Kenichi Ohki is a Professor in the Department of Physiology at the Graduate School of Medicine, The University of Tokyo, and serves as Deputy Director and Principal Investigator at the International Research Center for Neurointelligence (IRCN). His research focuses on visual neuroscience and functional brain mapping, with particular emphasis on understanding how the visual cortex processes information at cellular resolution. Education: 1990-1996: Medical degree (MD) from Faculty of Medicine, The University of Tokyo 1996-2000: PhD in Medicine from Department of Physiology, The University of Tokyo 1996: Visiting scholar at Department of Brain and Cognitive Sciences, MIT Dr. Ohki is renowned for developing single-cell resolution functional mapping with two-photon calcium imaging in 2005, which revolutionized the understanding of visual cortex architecture. His work has revealed fundamental principles of how orientation selectivity emerges in visual neurons and how developmental programs interact with neural activity to shape cortical function. His laboratory continues to investigate the interplay between innate developmental circuits and neuronal activity in determining cortical function using advanced imaging and computational techniques. Analysis of Dr. Ohki's publications shows a consistent progression from foundational work on visual cortex organization to understanding developmental mechanisms and more recently, integrating neuroscience with artificial intelligence approaches. His recent work demonstrates increasing sophistication in analyzing neural circuits, with growing emphasis on computational modeling and cross-disciplinary approaches that bridge neuroscience and AI. Scientific Recognition: Multiple high-impact publications in Nature, Nature Neuroscience, and other top journals Development of innovative techniques for functional brain mapping Leadership as Deputy Director of IRCN, a major international research center Selection for Beyond AI Institute's mid- to long-term research project Regular recognition of laboratory members with research awards Dr. Ohki actively mentors a diverse research team including project assistant professors, postdoctoral researchers, and graduate students. His laboratory has received significant institutional support for research on visual neuroscience and neural circuit development. Several of his students have received prestigious awards at major neuroscience conferences in Japan, demonstrating his effective mentorship and the quality of research conducted in his laboratory. The Ohki Laboratory operates within the Department of Physiology at the University of Tokyo and is affiliated with the International Research Center for Neurointelligence (IRCN). The lab utilizes advanced techniques including in vivo two-photon calcium imaging, optogenetics, and computational modeling to investigate visual information processing in the mammalian brain. The research team consists of scientists with diverse expertise in neuroscience, imaging technology, and computational analysis, creating a highly collaborative and interdisciplinary research environment focused on understanding the fundamental principles of visual processing in the brain.

Line Clemmensenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning
• Data Science
• +۶ مورد دیگر

Line Clemmensen serves as Associate Professor at DTU Compute (Department of Applied Mathematics and Computer Science), Technical University of Denmark, where she has held faculty positions since 2010. Her interdisciplinary work bridges statistical learning, machine learning, and real-world applications in mental healthcare, biotechnology, and agricultural informatics. Her academic credentials include: Ph.D. in Image Analysis and Computer Graphics from DTU (2010), thesis: "High-dimensional sparse data analysis" M.Sc. in Applied Mathematics from DTU (2006) Exchange studies at Universitat Politecnica de Catalunya, Barcelona (2004) Mathematical graduate from Falkonergården upper secondary school (2000) Clemmensen's research centers on machine learning and statistical learning with emphasis on low-resource modeling, representation learning, and AI evaluation methodologies. She applies these techniques to mental health (developing biosensor-based OCD monitoring systems), biotechnology (spectral data analysis for pharmaceutical quality control), and environmental science (crop health assessment via remote sensing). Her work consistently addresses challenges in data scarcity and model interpretability. Analysis of her recent publications reveals dominant trends in computational psychiatry (e.g., detecting OCD episodes through physiological signals and oxytocin biomarkers) and agricultural AI (linking soil microbiome composition to crop health via machine learning). Methodologically, she pioneers interpretable deep learning frameworks for low-resource settings and robust time-series analysis techniques for physiological data. She has supervised 10 PhD students to completion (including Jacob Søgaard Larsen on NIR management and Gudmundur Einarsson on psychiatric motion quantification) and mentored 13 Master's/Bachelor's students. Current funding includes the LundbeckFonden LF-Experiment grant for the FAST project (Fast Assessment of psychiatric Symptoms to Transform Mental Health Care). Within DTU Compute's Section for Statistics and Data Analysis, Clemmensen leads collaborations with Novo Nordisk (biostatistics), the Danish Meat Industry, and clinical psychiatry teams, focusing on real-world AI deployment in mental healthcare and industrial applications.

Benjamin Lippمشاهده پروفایل

استادیار

• Science and Technology Studies
• Human-Machine Interfacing
• Digital Health
• +۸ مورد دیگر

Benjamin Lipp serves as Assistant Professor at the Technical University of Denmark (DTU) within the Department of Technology, Management and Economics, specializing in Science and Technology Studies. He joined DTU in 2023 following a Marie Curie Postdoctoral Fellowship at Cornell University and the University of Hamburg. Dr. Lipp earned his PhD in Science and Technology Studies from the Technical University of Munich in 2019, with a dissertation examining assistive care robots in Europe, and holds a Diploma in Sociology from Ludwig Maximilian University of Munich (2013). His research program investigates the socio-material dimensions of human-machine interfaces in healthcare contexts through the lens of Science and Technology Studies and sociology. Key research themes include: Socio-technical aspects of care robotics implementation User experiences with digital pain management technologies Co-creation processes involving patients and healthcare professionals Policy implications of healthcare technology adoption Discursive effects of synthetic data in AI development Dr. Lipp's recent publications demonstrate a clear trajectory examining interface theory, digital health technologies, and the social implications of AI in healthcare settings. His work spans theoretical contributions to STS while maintaining strong empirical grounding in healthcare contexts, particularly chronic pain management and elderly care robotics. His scholarly achievements include publications in high-impact venues such as Nature, Social Studies of Science, and Science, Technology & Human Values, along with the prestigious Marie Curie Postdoctoral Fellowship recognition. Dr. Lipp actively supervises PhD research, notably as supervisor for the "Socially Sustainable Algorithms for Chronic Pain" project (2024-2027). He has organized significant public engagement initiatives including a 2017 care robots event at TUM and a 2023 Cornell workshop on digital pain technology, facilitating dialogue between researchers, industry, practitioners, patients, and citizens.

• Cybersecurity
• Biometrics
• Cryptography
• +۳ مورد دیگر

Christoph Busch serves as an External Lecturer at the Department of Applied Mathematics and Computer Science (DTU Compute) within the Technical University of Denmark, specializing in Cybersecurity Engineering. His academic role centers on biometric security systems and machine learning applications. His research spans Cybersecurity and Machine Learning, with emphasis on biometric authentication protocols resistant to offline attacks, cryptographic key exchange mechanisms, and generative adversarial networks for image reconstruction. Specific interests include fuzzy vault schemes, zero-effort attack mitigation, and network inversion techniques for enhancing biometric recognition accuracy. Publication analysis reveals a cohesive interdisciplinary trajectory: recent work merges cryptographic principles with deep learning to develop robust authentication frameworks (e.g., BRAKE protocol) and advanced GAN-based inversion methods (EGAIN), addressing critical gaps in secure digital identity systems and visual information processing.

Gozde Unalمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Gozde Unal is a Full Professor in the Department of Computer Engineering at Istanbul Technical University's Faculty of Computer and Informatics Engineering, where she also serves as director of the ITU Vision Lab and founding professor of the AI&Data Engineering Department. She previously held faculty positions at Sabancı University and conducted industry research at Siemens Corporate Research. Her educational background includes a PhD in Electrical and Computer Engineering with a Mathematics minor from North Carolina State University (2002), following postdoctoral work at Georgia Institute of Technology. Professor Unal's research pioneers intersections of Artificial Intelligence and Computer Vision, with significant contributions to medical imaging, 3D reconstruction, and architectural heritage preservation. Her work on diffusion models, adversarial point cloud attacks, and training-free segmentation demonstrates technical innovation while addressing real-world challenges in healthcare diagnostics and cultural conservation. The integration of multi-modal dynamics and evidential deep learning reflects her forward-looking approach to uncertainty quantification in AI systems. Analysis of her 2023-2025 publications reveals dominant trends in point cloud processing for historical reconstruction, medical image segmentation using Bayesian frameworks, and continual learning architectures. These works consistently bridge theoretical advances with applications in architectural heritage documentation and clinical diagnostics, particularly through novel diffusion model adaptations. Her scientific leadership has been recognized through prestigious awards: L’Oreal Turkey’s Female Scientist Award in Life Sciences (2010) Distinguished Young Scientist Award from TUBA (GEBIP) Marie Curie Alumni Association Career Award (2017) As technical program co-chair for MICCAI 2016 and MIDL 2019, she has shaped international discourse in medical image computing. Her lab direction indicates active graduate supervision, though specific student names aren't documented in the source material. The ITU Vision Lab under her leadership drives cutting-edge research in medical image analysis and 3D scene understanding, while her role in founding the ITU-AI Research Center establishes institutional frameworks for interdisciplinary AI innovation across the university.

• Machine Learning
• Natural Language Processing
• Computer Science
• +۳ مورد دیگر

Vésteinn Snæbjarnarson is a Research Fellow at the Department of Computer Science , University of Copenhagen , affiliated with the Pioneer AI (P1AI) research group. His work spans Natural Language Processing and Machine Learning , focusing on language model interpretability, sentiment analysis, and efficient model architectures. Research Interests: His research examines the intersection of language model steering , emotional response analysis , and resource-constrained AI . Key areas include adversarial robustness, open-set recognition, and sentiment detection in Icelandic text. Recent Trends: 2024 publications highlight advancements in quantized model optimization , multilingual sentiment datasets , and neural network security , with applications to both technical AI (computer vision, language modeling) and human-centric AI (emotional impact of geohazards).

Xiufeng Liuمشاهده پروفایل

پژوهشگر

• Energy Systems
• Data Analytics
• Renewable Energy
• +۵ مورد دیگر

Xiufeng Liu is a Senior Researcher at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on smart meter data analytics, big data, and energy systems, contributing to UN Sustainable Development Goals related to affordable and clean energy. He holds a PhD from Aalborg University (2012) and has held positions at IBM Canada, the University of Waterloo, and Åbo Akademi University. Dr. Liu’s expertise spans energy economics, climate policy modeling, and data-driven methodologies. His work integrates machine learning with energy systems, addressing challenges such as wind power forecasting, solar cell optimization, and heat load prediction. Key projects include OPTIX (optimizing positive-energy districts) and ANSWER (wind-solar energy prediction models). His research outputs emphasize sustainable energy solutions, including federated learning frameworks for privacy-preserving data sharing and anomaly detection in energy grids. Collaborations span global institutions, reflecting his commitment to interdisciplinary energy solutions. Dr. Liu has supervised numerous projects and contributed to 148 publications. His work bridges technical innovation with policy implications, aiming to advance climate-compatible energy strategies globally.

Paolo Burelliمشاهده پروفایل

مدرس

• Game AI
• Player Modeling
• Machine Learning
• +۵ مورد دیگر

Paolo Burelli is a Lecturer and Head of the brAIn Lab at the IT University of Copenhagen. He also serves as a Senior Data Scientist at Tactile Entertainment A/S since 2016. His research focuses on Game AI, Player Experience, Machine Learning, and Neuroscientific approaches to gaming. Key affiliations include the Creative AI Lab and The Maritime Hub. Research interests span adaptive game systems, player modeling, and the intersection of neuroscience with game design. Notable projects include the Pioneer Centre for Artificial Intelligence (2021–2034), ALGO (2019–2022), and CREATE (2023–2025). Projects emphasize creative AI applications in education, difficulty modeling in games, and maritime safety through alarm-handling practices. Publications highlight work on LLM emotion generation, EEG-based neural decoding, and player frustration tolerance. Collaborations include institutions like Springer and the Danish National Research Foundation. His datasets, such as the Uncanny Valley Face Questionnaire, contribute to facial perception studies. Labs under his leadership include the brAIn Lab, exploring AI ethics, game analytics, and human-centered computing. Projects emphasize practical applications of AI in education and industry.