جستجوی استاد راهنمای دانشگاهی

Manex Aguirrezabal Zabaletaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Poetry Analysis
• +۴ مورد دیگر

Manex Aguirrezabal Zabaleta is an Associate Professor in the Department of Nordic Studies and Linguistics at the University of Copenhagen. He previously held positions as a Postdoc (2017-2019) and Assistant Professor at the same institution. His educational background includes: PhD in Natural Language Processing from the University of the Basque Country (UPV/EHU), conducted at the IXA NLP group. Master's degree in Natural Language Processing from UPV/EHU. Bachelor's degree in Computer Science (5 years) from UPV/EHU. Dr. Aguirrezabal's research focuses on the computational analysis of poetry , particularly stress patterns in English. He explores whether computers can effectively analyze poetic structures, a field with roots in the 1980s but revitalized by modern techniques. Additionally, he investigates language generation , computational morphology and phonology , and finite-state methods . His work bridges traditional linguistic inquiry with cutting-edge natural language processing. Recent publications (2023-2024) demonstrate a diverse engagement with computational linguistics, including poetry generation, multimodal corpus development, clickbait analysis, and fact-checking. His research often employs zero-shot learning and language models, reflecting current trends in AI-driven linguistic analysis. He has contributed to international collaborations such as ParlaMint (multilingual parliamentary corpora) and the GEHM Zoom corpus. While specific grant details are not provided, his active publication record indicates ongoing research support. Dr. Aguirrezabal maintains a strong connection to his Basque heritage, having pursued his early education in the Basque language.

• Multimodal Learning
• Multilingual NLP
• Vision-Language Models
• +۴ مورد دیگر

Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.

John Bagterp Jørgensenمشاهده پروفایل

استاد

• Model Predictive Control
• Numerical Optimization
• Process Control
• +۵ مورد دیگر

John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.

James A. Evansمشاهده پروفایل

استاد

• Computational Social Science
• Collective Intelligence
• Science of Science
• +۱۲ مورد دیگر

James A. Evans is the Max Palevsky Professor of Sociology and Data Science at the University of Chicago, where he is a faculty member in the Department of Sociology within the Division of the Social Sciences. He is the director of Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science . He holds additional affiliations as an External Professor at the Santa Fe Institute , External Faculty at the Complexity Science Hub, Vienna , and Visiting Faculty Researcher at Google . Education: B.A. in Anthropology, Brigham Young University (1994) M.A. in Sociology, Stanford University (1999) Ph.D. in Sociology, Stanford University (2004) His research centers on the collective system of thinking and knowing , exploring how ideas emerge, spread, and evolve through social and technical systems. He investigates innovation, collective intelligence, and the science of science , using large-scale data modeling, machine learning, generative AI, and network analysis to study knowledge creation. His work spans domains including science, technology, law, and religion, with a focus on how AI is reshaping discovery processes. The most recent publications highlight trends in AI and scientific discovery , with a strong emphasis on innovation, knowledge systems, and human-machine intelligence . His research increasingly explores AI as a transformative agent in science , including the concept of 'alien intelligence' and the development of complementary AI to augment human capacity. Projects like the $20M NSF-funded APTO initiative aim to build language models that predict technological outcomes by analyzing historical data. Scientific Recognition and Funding: Research supported by the National Science Foundation (NSF) , National Institutes of Health (NIH) , Air Force Office of Scientific Research (AFOSR) , and philanthropic sources Work published in Nature, Science, PNAS , and leading social science journals Featured in The New York Times, The Economist, The Atlantic, Wired, NPR, BBC, Le Monde , and others James Evans advises on science policy and funding strategies, emphasizing the importance of diversity, interdisciplinary collaboration, and demographic balance in fostering innovation. He critiques current academic incentives and proposes alternative discovery regimes. He leads Knowledge Lab , a collaborative research environment that conducts seminars, grants, and employment opportunities in computational social science and AI.

Ankit Kariryaaمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Geospatial Analysis
• +۴ مورد دیگر

Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.

Daniel Hershcovichمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Cultural Adaptation in NLP
• +۴ مورد دیگر

Daniel Hershcovich is a Tenure Track Assistant Professor at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Natural Language Processing and Machine Learning . His research focuses on cross-cultural adaptation of language models, integrating human values into AI, and analyzing food-related cultural narratives for sustainable diets. Education: Ph.D. in Computational Neuroscience from Hebrew University of Jerusalem B.Sc. in Mathematics and Computer Science from Open University of Israel Recent publications highlight his work on multimodal models (haptic captioning, visual assistants for the blind), historical text analysis (Danish/Norwegian literature, euphemism detection), and cross-cultural NLP (recipe adaptation, cultural value alignment, climate awareness). His projects frequently combine AI ethics with domain-specific applications like food studies, historical linguistics, and accessibility research. Key collaborative networks include institutions in Denmark, Israel, and international partnerships through conferences like ACL, EMNLP, and workshops on cross-cultural NLP. The NLP section at DIKU serves as his primary affiliation for these efforts.

• Computational Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

Yang Chengمشاهده پروفایل

دانشیار

• Supply Chain Management
• Manufacturing Strategy
• Manufacturing Network
• +۵ مورد دیگر

Yang Cheng is an Associate Professor at the Department of Materials and Production, Aalborg University, Denmark. He holds a PhD in Mechanical Engineering from the same institution (2011), focusing on manufacturing strategy and network dynamics. His research spans supply chain management, sustainability, and global operations, with a focus on integrating technology and environmental policies into manufacturing systems. He leads or participates in high-impact projects like MAASive (2024–2026) and the Sino-Danish Center Research Project (2011–present), addressing resilience in value networks and global operations innovation. Research Interests: Supply Chain Management & Integration Sustainability & Green Technologies Manufacturing Strategy & Networks Technology Policy & Digitalization Global Operations & Cross-Border Collaboration Recent Work Trends: Prof. Cheng's 2025 articles emphasize blockchain in sustainable supply chains, green technology investments under carbon policies, and digitalization's ethical implications. His 2024 research explores smart factories, EU battery regulations, and robotization in manufacturing. These studies blend quantitative models with case-based analysis to address real-world challenges. Awards: 2024 Emerald Literati Awards – Outstanding Reviewer Advising & Grants: As PI for multiple Global Operations Management PhD programs (2019–2025), he guides research on digital transformation and university-industry collaboration. His projects receive funding from Danish and international grants, focusing on innovation and resilience in manufacturing networks. Labs/Teams: Collaborates with the Center for Industrial Production at Aalborg University and engages in international partnerships through the Sino-Danish Center. Active editorial roles include Production Planning & Control and Journal of Manufacturing Technology Management .

Alfred Taudesمشاهده پروفایل

استاد

• Operations and Supply Chain Management
• Cryptoeconomics
• Blockchain Technology
• +۷ مورد دیگر

Alfred Taudes is a Full Professor at the Department of Information Systems and Operations, Institute for Production Management, Vienna University of Economics and Business (WU Vienna). He holds a doctoral degree and a Habilitation from WU Vienna in Business Administration and Management Information Systems, and a Magister degree from Vienna University. He has held assistant professorships at WU and visiting professorships at Augsburg, Münster, Essen, and Tsukuba University, Japan. He joined WU permanently in 1993 and served as head of the Department of Information Systems and Operations from 2010 to 2016. His research spans Operations and Supply Chain Management , Marketing Engineering , Knowledge Management , and the impact of Big Data and Blockchain on production systems. Using Complexity Science and Cryptoeconomics , he investigates digital production, integrated value chains, and market designs. He teaches undergraduate and graduate courses including Operations Strategy, Data Science, and IT seminars in WU’s International Supply Chain Master program, and has also taught at Japanese universities. His recent publications focus on blockchain privacy (e.g., CoinJoin analysis), CBDCs, MiCAR regulation, decentralized federated learning, and digital custody, reflecting a strong trend toward cryptoeconomics and blockchain-based systems in operations and finance. These works appear in top journals and conferences in information systems, security, and operations research. Cooperation Officer of the Year 2013/14 Distinguished Paper Award WI 2009 VHB Best Paper Award Nomination VHB Best Paper Award Dr. Wolfgang Houska - Recognition Award Alfred Taudes has coordinated major research projects such as the WWTF-project “Integrated Demand and Supply Chain Management” and the Special Research Area Adaptive Models in Economics and Management Science. He currently leads the research group on Cryptoeconomics at WU and chairs the scientific board of the Austrian Internet Offensive . His leadership extends to project management in initiatives like the Austrian Blockchain Center and research on decentralized finance and digital assets. He is actively involved in academic service, including organizing conferences like DEXA 2022, serving on editorial boards, and advising on research policy. His lab and research group focus on blockchain applications, digital transformation in operations, and the societal implications of big data.

Kim Bjergeمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Kim Bjerge serves as Associate Professor and Group Leader in Aarhus University's Department of Electrical and Computer Engineering, specializing in computer vision and machine learning applications for ecological monitoring. His research bridges engineering and environmental science to develop innovative solutions for insect biodiversity assessment and sustainable agriculture. His core research interests include computer vision, deep learning, and edge computing systems for real-world ecological monitoring. Dr. Bjerge develops time-lapse camera pipelines and deep learning models specifically for insect population tracking in natural environments, with emphasis on agricultural applications like black soldier fly farming and biodiversity conservation. His work integrates signal processing techniques with biological data to create field-deployable monitoring systems. Recent publications reveal a strong trend toward practical implementations of computer vision in entomology, particularly focusing on edge processing for camera traps, automated trait prediction in insect farming, and biodiversity monitoring systems. Key research areas include nocturnal insect monitoring, floral environment analysis, and developing specialized datasets like AMI for insect identification in wild settings. He leads multiple significant research projects funded through competitive grants: MAMBO: Modern Approaches to Monitoring Biodiversity (2022-2026) FLYgene: Sustainable Insect Production for Livestock Feed (2022-2026) Automatisk monitering af nataktive insekter: Automatic nocturnal insect monitoring (2024-2029) Pilotprojekt for automatisk registrering af invasive plantearter: Invasive species monitoring (2020-2021) As head of the Signal Processing and Machine Learning research group, Dr. Bjerge directs interdisciplinary teams developing computer vision solutions for biological monitoring systems. His laboratory focuses on creating robust field-deployable technologies including scanner-based arthropod imaging systems, time-lapse camera networks for floral environments, and edge AI processors for real-time insect monitoring in agricultural settings.

• Natural Language Processing
• Explainable AI
• Fact-Checking
• +۴ مورد دیگر

Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.

Jesper Linigerمشاهده پروفایل

دانشیار

• Marine Growth Engineering
• Power Fluid Engineering
• Wind Turbine Engineering
• +۶ مورد دیگر

Jesper Liniger is an Associate Professor at AAU Energy within the Faculty of Engineering and Science at Aalborg University. He works in the Esbjerg Energy Section focusing on Offshore Renewable Energy Systems and is affiliated with AAU BLUE – Marine & Maritime Research. His office is located at Niels Bohr Street 8, 6700 Esbjerg, Denmark. Research Interests Marine Growth Engineering and automated cleaning solutions for offshore structures Underwater robotics including Remotely Operated Vehicles (ROVs) and autonomous inspection systems Wind turbine engineering with emphasis on hydraulic pitch systems and fault detection Fluid power engineering applications in marine environments Development of robotic solutions for offshore renewable energy infrastructure Research Trends Dr. Liniger's recent publications demonstrate a strong focus on developing robotic solutions for offshore renewable energy infrastructure. His work bridges theoretical control systems with practical marine applications, particularly addressing marine growth (biofouling) challenges on offshore structures. The research shows increasing interdisciplinary collaboration, combining robotics, fluid mechanics, and wind energy systems to create integrated solutions that improve operational efficiency and reduce maintenance costs in offshore environments. Scientific Awards Innovation Project of the Year (2024) - For underwater robotics development Esbjerg Universitetspris (2018) - University award recognizing research excellence Advising and Research Leadership Dr. Liniger actively supervises PhD students and serves as principal investigator or supervisor on multiple major projects including "NextGen Robotics" for offshore wind farms and "Towards Enhancing Perception and Navigation for Autonomous Underwater Inspection Drone." His research portfolio includes collaborations with industry partners like Vattenfall and Business Center Funen, demonstrating strong industry-academia connections focused on practical applications with economic impact. Research Teams and Facilities Liniger is part of AAU BLUE – Marine & Maritime Research, which provides specialized facilities for marine robotics testing and development. His work involves close collaboration with researchers in control systems, fluid mechanics, and renewable energy. The research group has developed experimental frameworks for testing underwater and surface vehicle operations, with recent media coverage highlighting their innovative approaches to solving marine growth challenges on offshore structures.

Nicola Dragoniمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.

Rasmus Bjørkمشاهده پروفایل

استاد

• Energy Conversion
• Materials Science
• Magnetocaloric and Elastocaloric Cooling
• +۴ مورد دیگر

Rasmus Bjørk is a Professor at the Technical University of Denmark (DTU) in the Department of Energy Conversion and Storage. His research focuses on advanced materials for energy systems, particularly in magnetocaloric and elastocaloric cooling, magnetic materials, and additive manufacturing for functional devices. His work contributes to the UN Sustainable Development Goals, especially in affordable and clean energy. PhD Supervision: Active projects include energy storage using topological spin textures, magnetothermal waste heat harvesting, and bio-magnetometers. Key Research Areas: Magnetic refrigeration, energy harvesting, and freeze-casting of functional materials. Recent advancements include 3D-printed elastocaloric coolers and studies on magnetoresistive devices. His team develops novel techniques for optimizing magnetic systems and energy conversion processes. He has published over 160 articles and led projects on regenerator design, magnetic bearings, and sensor technologies. Collaborations span multiple countries and disciplines. Notable contributions include pioneering work on freeze-casting for biomaterials and the MagTense micromagnetic framework. His research bridges theoretical modeling and practical applications in sustainable energy solutions.

Borja Valverde Pérezمشاهده پروفایل

دانشیار

• Microalgae Biotechnology
• Resource Recovery
• Life Cycle Assessment
• +۳ مورد دیگر

Borja Valverde Pérez is an Associate Professor in the Department of Environmental and Resource Engineering at the Technical University of Denmark (DTU). His research advances sustainable water resource recovery through biotechnology innovations. Research focuses on microalgae cultivation, microbial protein production, and bioelectrochemical systems for water treatment. Key projects explore nutrient recovery from waste streams and greenhouse gas mitigation in biological processes. Recent publications demonstrate innovative approaches to wastewater valorization, including microalgae-bacteria consortia for pollutant degradation and inorganic bioelectric systems for low-temperature denitrification. Work integrates experimental validation with life cycle assessment. He coordinates projects on industrial symbiosis and circular economy applications in water systems. Current initiatives investigate decarbonization pathways for water resource recovery facilities through energy-flexible operation.