جست‌وجوی استاد و پژوهشگر

• Image Analysis
• Computational Geometry
• Computer Vision
• +۳ مورد دیگر

Tore Ziegler Hougaard Andersen serves as a Lecturer in the Department of Computer Science (DIKU) at the University of Copenhagen. He is affiliated with the Image Analysis, Computational Modelling, and Geometry research section, which focuses on interdisciplinary work spanning theoretical algorithms to real-world applications in science and industry. His research interests derive from the section's core themes, including medical/biological imaging, computer vision, robotics, and mathematical geometry. The section actively contributes to initiatives like the SCIENCE AI Centre and collaborates with facilities such as Max IV and the Danish Bioimaging Network. DIKU provides robust infrastructure for this work, including high-performance computing clusters and specialized labs. While specific advising or grant activities for Andersen are unreported in the source material, the department emphasizes cross-sector collaboration through networks like the Pioneer Centre for Artificial Intelligence and MADE (Manufacturing Academy of Denmark).

• Programming Language Technology
• Theory of Computation
• Compiler Technology
• +۴ مورد دیگر

Uffe Dalgas is an Instructor at the Department of Computer Science, University of Copenhagen. His work aligns with the Programming Languages and Theory of Computation (PLTC) section, which focuses on programming language technology, theoretical computation, and interdisciplinary applications including blockchain, fintech, and formal verification. Research Interests: Programming Language Theory and Applications Formal Verification and Secure Systems Compiler Technology and High-Performance Computing Distributed Ledger Technology and Fintech Contact: ufda@di.ku.dk

• Computer Science
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Eric Deng serves as a Lecturer in the Department of Computer Science (DIKU) at the University of Copenhagen, contributing to teaching and academic activities within Denmark's premier computing institution. Located at Universitetsparken 1 in Copenhagen Ø, DIKU operates under the Faculty of Science with robust infrastructure including high-performance computing clusters and specialized labs. His research spans core computer science disciplines with emphasis on Artificial Intelligence , Data Science , and Software Engineering . These fields align with DIKU's strategic focus areas including Machine Learning, Human-Centred Computing, and Pioneer AI initiatives. The department actively engages in cross-disciplinary projects addressing societal challenges through algorithmic innovation and computational modeling. DIKU hosts multiple research sections such as Image Analysis, Computational Modelling, and Geometry, supported by state-of-the-art facilities. Recent departmental achievements include €7 million in cancer research funding from the Novo Nordisk Foundation and ERC Consolidator Grants for AI-driven toxicity modeling, reflecting its leadership in computational science applications.

• Programming language semantics
• Computational effects
• Formal verification
• +۲ مورد دیگر

Andrzej Filinski is an Associate Professor at the Programming Languages and Theory of Computation section within the Department of Computer Science at the University of Copenhagen. His research focuses on applied programming-language semantics, particularly computational effects and machine-verifiable reasoning about programs. University: University of Copenhagen Department: Department of Computer Science Research Interests: Operational semantics of monadic effects, game-theoretic program contracts, formal verification, concurrency, distributed systems, and code certification. Recent Publications include work on information flow logic using partial equivalence relations (2024), functional approaches to regular expression matching (2021), and streaming nested data parallelism on multicore systems (2016). His research spans theoretical foundations and practical applications in programming languages and computational theory.

• Programming Languages
• Theory of Computation
• Computer Security
• +۳ مورد دیگر

Mareike Sibilla Glöss is a Part-Time Lecturer at the Department of Computer Science, University of Copenhagen. She contributes to teaching and research in programming language technology and theoretical computer science, aligning with the PLTC (Programming Languages and Theory of Computation) section's focus areas. Position: Part-Time Lecturer Department: Department of Computer Science University: University of Copenhagen Her work intersects with advanced research groups in the department, including decentralized systems, security & privacy, and probabilistic programming. The department emphasizes both theoretical and applied research, spanning compiler technology, quantum computing, and high-performance systems.

• Programming Languages
• Theory of Computation
• Blockchain Technology
• +۵ مورد دیگر

Rikke Tophøj Heinemann is an academic staff member at the Department of Computer Science (DIKU) , University of Copenhagen. She holds the academic rank of Lecturer and is affiliated with the Programming Languages and Theory of Computation section (PLTC). Her research interests align with the section’s focus areas, including programming language technology, theoretical foundations of computation, blockchain, distributed ledger technology, computer security, and privacy preservation.

• Programming Languages
• Compilers
• Type Systems
• +۴ مورد دیگر

Nikolaj Hey Hinnerskov is a PhD Fellow in the Programming Languages and Theory of Computation (PLTC) section at the Department of Computer Science, University of Copenhagen. His research is embedded within DIKU's PLTC group, focusing on foundational and applied programming language systems. His core research spans: Programming Language Theory and Type Systems Compiler Design for Array-Based Languages Automatic Differentiation Techniques GPU-Accelerated Algorithm Development Rank-Polymorphic Function Inference Time Series Analysis Optimization Recent work demonstrates a strong convergence of programming language theory and high-performance computing, particularly in advancing compiler techniques for functional array languages like Futhark and optimizing scientific computations on GPUs. His publications address critical challenges in type inference, automatic differentiation, and parallel time-series decomposition. No scientific awards are documented in available sources. As a doctoral researcher, he operates under faculty supervision without independent advisees or grant leadership. His contributions align with DIKU's PLTC projects, including language design and performance optimization initiatives. He actively collaborates within the PLTC research ecosystem, contributing to GPU-based computational methods and programming language innovations alongside DIKU faculty and international researchers.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Bulat Ibragimov is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on applying advanced computational techniques to medical imaging problems, particularly in radiology and diagnostic applications. His research interests span multiple domains of medical AI, including: Medical image analysis and computer vision for diagnostic applications Eye tracking analysis to understand radiologist decision-making processes Deep learning applications for disease detection and severity classification Explainable AI systems for medical image interpretation Computational geometry applications in medical imaging Dr. Ibragimov's recent publications demonstrate a strong focus on applying artificial intelligence to improve diagnostic accuracy and efficiency in medical settings. His work frequently bridges the gap between computer science and clinical applications, with particular emphasis on radiology, cardiology, dentistry, and gastroenterology. Many of his studies incorporate eye tracking data to better understand and augment human decision-making in medical imaging contexts. His notable contributions include work on: Cardiometric analysis from chest X-rays Dental image analysis for abnormality detection Ulcerative colitis severity classification Explainable AI for medical image models Prediction of radiological decision errors using eye tracking data

Thomas Philip Jensenمشاهده پروفایل

استاد

• Programming Languages
• Theory of Computation
• Formal Methods
• +۳ مورد دیگر

Thomas Philip Jensen is a Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Programming Languages and Theory of Computation research section. His work bridges theoretical computer science with practical security applications through formal methods and program verification. His core research interests include: Programming Languages Theory of Computation Formal Methods Program Verification Information Flow Security Type Systems Analysis of his 2020-2024 publications reveals sustained focus on security-critical program transformations, with key trends in constant-time enforcement against side-channel attacks, logical frameworks for information flow using partial equivalence relations, and refinement-type-based verification. His work consistently connects theoretical foundations to compiler security and secure workflow systems. No scientific awards are listed in the provided information. Details about student advising and research grants are not specified in the source materials. Professor Jensen operates within the Programming Languages and Theory of Computation section, which leverages the Department of Computer Science's infrastructure including high-performance computing clusters and specialized research laboratories.

• Machine Learning
• Natural Language Processing
• Medical Image Analysis
• +۲ مورد دیگر

Mariusz Zbigniew Matyja is a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Machine Learning Section. His research focuses on theoretical foundations and applications of machine learning across domains including natural language processing, medical image analysis, and biological data modelling. He contributes to the university's SCIENCE AI Centre initiative. Research interests span machine learning theory, NLP implementations for information retrieval systems, medical diagnostic imaging techniques, and computational biology applications. Current work emphasizes high-performance computing approaches to complex data modelling challenges in interdisciplinary contexts.

• Programming Languages
• Theory of Computation
• Formal Verification
• +۳ مورد دیگر

Atta Samiullah serves as an Instructor in the Department of Computer Science at the University of Copenhagen, affiliated with the Programming Languages and Theory of Computation (PLTC) section. His role encompasses teaching responsibilities across undergraduate and graduate computer science programs including BSc in Computer Science, Computer Science and Economics, Machine Learning and Data Science, and Communication & IT. His research profile aligns with PLTC's core focus areas: Programming language technology and formal verification methodologies Theory of computation with applications in distributed ledger systems Security and privacy preservation techniques in software systems Financial technology applications and blockchain infrastructure Compiler technology and high-performance computing architectures Quantum programming language design The PLTC section operates through specialized research groups including Decentralized Systems, Security & Privacy, HOT Lab, FUTHARK (Functional Technology for Modern Architectures), and Quantum Programming. These groups maintain active industry collaborations in fintech and blockchain sectors while developing open-source tools like Moscow ML and MLKit compilers. Research output spans probabilistic programming frameworks, Stein variational inference techniques, and applications in bioinformatics and vaccine design. No scientific awards or student supervision activities are documented in current institutional records. His work contributes to the department's mission in advancing programming language theory with practical implementations in secure financial systems and distributed computing environments.

Nick Rübner Papiorمشاهده پروفایل

پژوهشگر

• Quantum Transport
• Graphene
• Nanomaterials
• +۳ مورد دیگر

Nick Rübner Papior is a Special Consultant in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU) . His research focuses on quantum transport phenomena, nanomaterials, and computational modeling of electronic properties in graphene-based systems and molecular junctions. He has contributed significantly to the development of the SIESTA software package and organizes workshops on electronic structure simulations. Key research areas include carbon nanostructures (graphene, nanoribbons), spintronics, and sensor technologies for CO x detection. He has supervised PhD students such as Calogero G. and has been involved in projects funded by Danmarks Grundforskningsfond. His work spans theoretical investigations into topological phases in conjugated polymers, quantum interference effects, and current-induced mechanical stability in nanoconductors. Recent publications highlight advancements in robust quantum engineering at room temperature, mechanical/magnetic stability of carbon nanoconductors, and interferometry with graphene networks. His interdisciplinary approach integrates computational tools with experimental insights to advance nanoelectronics and sustainable materials science. Labs/Teams: Core contributor to SIESTA development and organizer of electronic structure software schools. Advising: Supervised PhD student Calogero G. in theoretical investigations of graphene devices (2015–2019). Grants: Principal investigator on projects exploring graphene nanostructures (2012–2016) and computational tools for transport properties (2015–2019).

Jin Hyun Changمشاهده پروفایل

دانشیار

• Electrochemical Energy Engineering
• Battery Materials
• Lithium-ion Batteries
• +۵ مورد دیگر

Jin Hyun Chang is an Associate Professor at the Department of Energy Conversion and Storage , Technical University of Denmark (DTU). His research focuses on advancing sustainable energy technologies, particularly in battery materials and electrochemical systems. He leads projects in high-throughput screening of catalytic materials, automated laboratory systems, and sustainable energy storage solutions aligned with UN Sustainable Development Goals. His work integrates computational methods, experimental techniques, and machine learning to accelerate material discovery for batteries and electrocatalysts. Key research areas include battery electrolyte design, CO₂ reduction catalysis, and automated synthesis robotics. Chang supervises multiple PhD students in interdisciplinary projects involving materials science, automation, and energy systems. Notable contributions include developing polymer-based battery separators, vision-based lab automation platforms, and high-throughput screening of hydrides for CO₂ reduction. His research emphasizes practical applications in green energy transition and sustainable materials. Chang collaborates internationally and has published extensively on topics like lithium-ion cathodes, magnesium batteries, and electrochemical interfaces. His lab, Autonomous Materials Discovery , pioneers robotic systems for accelerated material innovation.

Alexander Baggerمشاهده پروفایل

دانشیار

• Electrochemistry
• Catalysis
• Materials Science
• +۴ مورد دیگر

Alexander Bagger is an Associate Professor in the Department of Physics at the Technical University of Denmark (DTU). His research focuses on electrochemical catalysis, particularly in CO₂ reduction, nitrogen reduction, and the design of advanced catalytic materials. He leads projects on CO₂ electrolysis, non-aqueous electrosynthesis, and electrocatalytic pathways for sustainable energy production. His work contributes to the UN Sustainable Development Goals related to affordable and clean energy, climate action, and industry innovation. Key research areas include the development of novel catalysts (e.g., single-site M–N–C, high-entropy alloys), understanding reaction mechanisms in CO₂ valorization, and optimizing electrochemical processes for C₂−C₆ product formation. His group actively investigates descriptors for catalytic selectivity, anion effects, and the synergy between computational modeling and experimental validation. Bagger supervises multiple PhD students, including E. Wessel (CO₂ electrolysis), A. H. Høholdt (non-aqueous electrosynthesis), and A. Wuttke (atomistic modeling). Recent publications emphasize mechanistic insights into CO₂ electrolysis, NOₓ reduction, and urea synthesis through descriptor-based approaches. His work has been cited widely and disseminated through high-impact journals like ACS Energy Letters and Communications Chemistry . Labs/Teams: Surface Physics and Catalysis Center for Catalysis Theory, DTU Physics.

• Resource-Aware Machine Learning
• Data Processing on Resource-Constrained Hardware
• Storage Systems
• +۵ مورد دیگر

Pınar Tözün is an Associate Professor at the IT University of Copenhagen (ITU) since 2018, where she leads the Data, Systems, and Robotics section and the Resource-Aware Data Systems (RAD) team. Her research focuses on optimizing modern hardware (processors and storage) for data-intensive systems, with recent emphasis on resource-aware machine learning, hardware-constrained data processing, and advancements in SSD/CXL technologies. Before ITU, she was a research staff member at IBM Almaden Research Center (2015–2018), contributing to the IBM Db2 Event Store development. She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL, 2014), where she worked on the Shore-MT storage manager under Prof. Anastasia Ailamaki. She earned her bachelor’s degree in Computer Engineering from Koç University (Turkey, 2009), advised by Prof. Serdar Taşıran. Her research interests span Resource-aware machine learning, Data management systems, Edge computing, High-performance storage, and sustainable AI practices. Her work bridges hardware and software optimization, addressing challenges in distributed systems and IoT environments. She actively contributes to academic communities through workshops like DBTest and diversity initiatives in database conferences.