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نمایش ۶۹۱-۷۰۵ از ۳٬۴۱۲ استاد راهنما
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Mathias Schack Rabing is a PhD Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Software, Data, People & Society (SDPS) section. His research focuses on software systems, data management, and human-centered design to create societal value. Email: mrabing@di.ku.dk .
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Shivam Adarsh is a PhD Fellow in the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), actively contributing to the SCIENCE AI Centre. Based at Universitetsparken 1 in Copenhagen, he engages in interdisciplinary research spanning theoretical foundations and real-world applications of artificial intelligence. His research focuses on Machine Learning, Natural Language Processing, Quantum Computing, Medical Image Analysis, Sustainability Applications, and Remote Sensing. Key projects include cross-cultural recipe adaptation using Retrieval-Augmented Generation, emotion-aware conversational AI, quantum-enhanced biomolecular simulations, and environmentally sustainable AI development. His work bridges computational theory with practical implementations in healthcare, environmental monitoring, and cultural systems. Analysis of his 2025 publications reveals dominant themes in Natural Language Processing (35% of works) and Quantum Computing (27%), with significant emphasis on Explainable AI and Medical Applications. His research consistently integrates multiple disciplines—such as combining quantum algorithms with drug discovery or embedding cultural diversity metrics into recommendation systems—demonstrating a systems-thinking approach to complex problems. As part of DIKU's Machine Learning group led by Professor Yevgeny Seldin, he utilizes the department's powerful compute cluster and participates in the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. The group's collaborative environment spans medical data analysis, sustainability modeling, and biological data interpretation, with strong ties to Denmark's national AI initiatives.
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Frederikke Isa Marin is a Research Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen. Her research focuses on the intersection of machine learning and biological applications, particularly in protein engineering, DNA language modeling, and computational biology. Her primary research interests include developing machine learning frameworks for biological sequence analysis, protein stability prediction, computational protein design, and benchmarking methodologies for DNA language models. She employs generative models, deep learning, and computational simulations to address challenges in structural biology and genomics. Marin's recent publications demonstrate a strong focus on applying machine learning to biological systems. Her work spans protein engineering (including thioredoxin fold redesign and thermal stability prediction), DNA language model benchmarking (BEND), immune receptor modeling, and predictive methods for peptide functionality. Her research consistently combines computational approaches with experimental validation. She maintains collaborative relationships within the Machine Learning section and contributes to the university's computational biology initiatives. Her work utilizes departmental compute resources and aligns with the research groups focused on biological applications of AI.
پژوهشگر
Tong Chen is a Postdoc in the Machine Learning section at the Department of Computer Science, University of Copenhagen, focusing on theoretical and applied machine learning with applications in information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His research spans adversarial machine learning, neural network compression, and robustness verification using polynomial and semialgebraic optimization techniques. Chen develops mathematically rigorous frameworks to enhance deep learning model efficiency, security, and reliability while addressing real-world implementation challenges. Chen's publication trajectory reveals increasing emphasis on formal verification methods and compression-robustness trade-offs, with recent work integrating semialgebraic geometry for neural network certification. This demonstrates a clear trend toward foundational approaches that bridge theoretical optimization and practical AI safety requirements. As part of the Machine Learning section, Chen contributes to the SCIENCE AI Centre and TreeSense initiative, utilizing the department's dedicated compute cluster for resource-intensive remote sensing and deep learning experiments in global environmental monitoring.
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Egor Bakaev is a Research Fellow in the Algorithms and Complexity section at the Department of Computer Science, University of Copenhagen, affiliated with the Basic Algorithms Research Copenhagen (BARC) centre. His work focuses on theoretical computational methods with real-world applications in data-intensive fields. His research spans algorithms, complexity theory, and combinatorial optimization, addressing fundamental questions about computational efficiency. The Algorithms and Complexity section bridges mathematical theory with practical implementations, particularly in machine learning systems and big data analytics, through projects supported by BARC and the Danish Center for Big Data Analytics (DABAI). Affiliation : Department of Computer Science (DIKU), University of Copenhagen Research Hub : Basic Algorithms Research Copenhagen (BARC) centre Contact : egba@di.ku.dk | +4535327457 | Universitetsparken 1, 2100 Copenhagen Ø
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Johanna Maria Düngler is a Research Fellow at the Department of Computer Science , University of Copenhagen . She is affiliated with the Natural Language Processing (NLP) section, which focuses on advancing methods for text processing, language understanding, and generation using statistical models and machine learning. The section addresses applications such as automatic fact-checking, machine translation, and multi-modal learning involving vision and language.
Fabian Christian Gieseke is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in Machine Learning. His research focuses on developing advanced computational methods for environmental monitoring and large-scale data analysis. Primary research interests include: Application of deep learning to remote sensing and geospatial data Large-scale forest biomass estimation using LiDAR and satellite imagery National-scale ecological monitoring systems Efficient algorithms for processing massive environmental datasets His recent publications demonstrate a strong emphasis on applying machine learning to climate science and ecological conservation. Work frequently involves: Cross-disciplinary collaborations with environmental scientists Development of novel deep learning architectures for 3D data Large-scale mapping of natural resources Solutions for global environmental challenges
Thomas Troels Hildebrandt is a Professor in the Department of Computer Science at the University of Copenhagen, where he heads the Software, Data, People & Society research section. His work focuses on developing reliable and flexible software systems that adapt to user needs and legislative changes, with applications in digital law, workflows, and business processes. His educational background includes: PhD in Computer Science from Aarhus University (awarded February 23, 2000) Professor Hildebrandt's research spans Software Engineering , Process Modeling , and Business Process Management , with a focus on declarative approaches like Dynamic Condition Response (DCR) graphs. His work integrates formal methods to ensure system reliability in contexts ranging from smart contracts to public governance. He actively explores societal implications of AI, advocating for transparency and user-centered design in digital systems. Recent publications demonstrate a strong trend toward declarative process modeling applied to smart contracts and public governance . Key developments include DCR graphs for dynamic behavior modeling, cross-chain business logic monitoring, and digital compliance frameworks. His interdisciplinary approach bridges computer science with real-world societal challenges, particularly in adapting systems to evolving legislation and user requirements. No specific scientific awards are mentioned in the provided information. Professor Hildebrandt leads interdisciplinary research projects and serves on advisory boards for digitalization and AI. His work has fostered industry collaboration, including founding DCR Solutions based on his research. He acts as an independent consultant and speaker in digital transformation, with recent projects focusing on blockchain integration and public sector AI ethics. He directs the Software, Data, People & Society research section, which develops human-centered methods for adaptable digital systems. Current initiatives include DCR graph applications for GDPR compliance, smart contract security, and transparent AI in public services, with strong industry and government partnerships.
Kamille Dimon Jakobsen is a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen. She is affiliated with the Natural Language Processing section, which focuses on statistical models and machine learning for text understanding, generation, and applications like machine translation and question answering. Her research interests span core areas of Natural Language Processing, including multi-modal learning, text generation, and machine translation, aligned with the section's focus on NLP innovation. No awards, students, or publications were listed in the provided materials.
Kjartan Martin Johannesen serves as an Instructor at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Pioneer AI research section. His role centers on teaching within DIKU's academic framework while contributing to the department's research ecosystem. His research and teaching interests align with Pioneer AI's core focus areas: artificial intelligence, machine learning, computer vision, medical imaging, statistics, and geometric data analysis. The section specializes in fine-grained classification, self-supervised learning, multimodal AI systems, medical image modeling, and evolutionary morphometry, leveraging advanced techniques in 2D/3D generative models and domain adaptation. The Pioneer AI section operates from the Observatory in Copenhagen Botanical Garden as part of the Pioneer Center for Artificial Intelligence. It maintains robust collaborations with Danish institutions, international partners, and industry, while hosting major conferences including ECCV 2026. Teaching responsibilities include machine learning, deep neural networks, vision processing, and data science courses that integrate the section's research advancements.
Martin Lillholm is a Professor at the Department of Computer Science , University of Copenhagen, specializing in Image Analysis, Computational Modelling, and Geometry . His research focuses on leveraging machine learning and deep learning for medical imaging , particularly in breast cancer risk stratification and COVID-19 adverse outcome prediction . He has co-authored numerous high-impact journal articles in Radiology , Scientific Reports , and other venues, often collaborating with clinical researchers. Research Trends & Fields Lillholm’s work bridges computer science and healthcare , with recent publications emphasizing: AI-driven mammography screening for early breast cancer detection Texture analysis in medical images to enhance risk prediction Domain adaptation for robust cross-vendor imaging systems Health registry analysis using machine learning for recurrent cancer identification Pandemic risk modeling for COVID-19 outcomes Collaborations & Impact He collaborates across disciplines, including with Department of Clinical Medicine researchers and international partners . His studies have been widely cited, with significant visibility via Mendeley , X , and news outlets . While no formal awards are listed, his research has driven clinical protocol innovations and policy discussions.
Christoffer Olling Back is a Postdoctoral Researcher at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Human-Centred Computing . His work bridges applied and theoretical machine learning, focusing on probabilistic inference, stochastic processes, and computability theory. Education PhD in Computer Science (2017-2020) - University of Copenhagen MSc in Artificial Intelligence (2010-2011) - University of Edinburgh BA in Psychology (w/ Computer Science) (2004-2008) - Lewis and Clark College Current research explores predictive workflow models using location data through the iAware project (collaboration with Systematic, PowerNet, and Bispebjerg Hospital). Previous work investigated ERP system datasets in the DIREC consortium. His publications span topics in process mining, probabilistic modeling, and healthcare informatics. Recent achievements include 15 research outputs (2024-2016) covering process discovery, workflow simulation, and entropy-based log analysis. Collaborations with institutions like Roskilde University and industry partners demonstrate interdisciplinary impact. Scientific Awards Dean's List (2007) Nordea Fonden Scholarship (2010) As an educator, he serves as guest lecturer, assistant teacher, and tutor in computer science, machine learning, and software engineering. His professional background includes industry roles at ServiceNow Denmark ApS (2021-2024) and Gekkobrain (2020-2021).
Naja Holten Møller, an Associate Professor in the Promotion Programme at the Department of Computer Science, University of Copenhagen, specializes in Human-Centred Computing . Her research focuses on Computer-Supported Cooperative Work (CSCW) , exploring ethics in data-driven technologies, algorithmic decision-making, and the digitalization of public sectors. She investigates how adaptive technologies transform work processes and emphasizes balancing human values with automation through long-term collaborations with public organizations. Research Areas: Ethics in Data Work Algorithmic Accountability Public Sector Digitalization Future Workplace Optimization Scientific Awards: Member of ACM's Future of Computing (2017) Collaborations: Public Sector Organizations Refugee Law Institutions Healthcare Systems Her recent publications highlight trends in asylum data governance , AI labor pipelines , and participatory design in public systems, reflecting her focus on technological ethics and stakeholder inclusion.
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Zain Muhammad Mujahid is a PhD Fellow at the Department of Computer Science , University of Copenhagen (UCPH). His research focuses on Natural Language Processing with emphasis on Large Language Models (LLMs), bias detection, and fact-checking methodologies. Research Interests Factuality and bias prediction in news media LLM evaluation and error analysis Cross-lingual fact-checking systems Arabic-centric language modeling Evidence attribution in summarization AI safety in multilingual contexts Publications Zain's recent work addresses critical challenges in trustworthy AI, including automating error detection in NLG systems, developing cross-lingual bias detection frameworks (SAFARI), and creating benchmarks like Factcheck-Bench for evaluating automatic fact-checkers. His research also explores bilingual safety evaluation in Kazakh-Russian contexts and cultural adaptation of LLMs for Arabic language processing.
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Asbjørn Marco Sinius Munk is a Research Fellow at the Department of Computer Science, University of Copenhagen , affiliated with the Pioneer AI (P1AI) research group. His work focuses on medical image analysis and domain adaptation in deep learning. Research Interests: Domain adaptation for medical imaging 3D MRI segmentation Foundation models in biomedical applications Self-supervised learning for brain imaging Deep learning frameworks for clinical data Recent research highlights include theoretical guarantees in domain adaptation (MDD-UNet) and alignment of 3D MRI with tabular data using CLIP-inspired models. His publications demonstrate strong applications of AI in healthcare imaging domains. Contact: asmu@di.ku.dk
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