جست‌وجوی استاد و پژوهشگر

Thomas Bakمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Assistive Technology
• +۵ مورد دیگر

Thomas Bak is Professor and Dean of The Technical Faculty of IT and Design at Aalborg University, Denmark, where he maintains an active research profile while leading the faculty. His office is located at Fredrik Bajers Vej 1, A.2.23, 9220 Aalborg East. Professor Bak's research primarily focuses on robotics, particularly assistive robotics for individuals with disabilities. His work spans exoskeleton design, computer vision-based control systems, and rehabilitation engineering. Key research areas include human-robot interaction, control systems, and AI applications in assistive technologies. His research has significant practical applications, especially in developing technologies that improve quality of life for people with severe disabilities. Analysis of his recent publications reveals a strong emphasis on upper-limb exoskeletons for individuals with tetraplegia, with a focus on clinical validation, user-centered design, and semi-autonomous control systems. His work bridges engineering, computer science, and rehabilitation medicine, demonstrating interdisciplinary collaboration across multiple fields. Recent trends show increasing integration of AI and probabilistic methods in robotics applications. Professor Bak has secured significant research funding, including grants from the Louis-Hansen Foundation for rehabilitation robotics and AAU strategic grants for projects like EXOTIC (Assistive personal robotics platform with an exoskeleton using the tongue for intelligent control). He has collaborated extensively with researchers across multiple disciplines, including medical professionals and engineers. He serves as principal investigator or supervisor for numerous PhD students and research projects. His leadership extends to institutional roles, including chairing committees related to AI strategy at Aalborg University, as evidenced by media coverage of the 'Ny stor AI-satsning på Aalborg Universitet' (New major AI initiative at Aalborg University). Professor Bak is actively involved with robotics research centers and collaborates with institutions like FORCE Technology. His work on the Center for Rehabilitation Robotics demonstrates his commitment to translating research into practical applications that benefit individuals with disabilities through technologies like the EXOTIC exoskeleton platform.

Andrzej Wąsowskiمشاهده پروفایل

استاد

• Software Engineering
• Formal Methods
• AI and Robotics
• +۸ مورد دیگر

Andrzej Wąsowski is a Professor in the Computer Science Department at IT University of Copenhagen. His research focuses on software engineering, formal methods, AI/robotics, and safety-critical systems. He leads projects like REMARO (Marine Robotics reliability) and INSIGHT (program repair). His work emphasizes model-driven engineering, probabilistic systems, and open-source collaboration. Research Interests Formal Verification of Autonomous Systems Probabilistic Programming and Privacy Analysis Software Product Line Engineering Robotics and Cyber-Physical Systems Recent Work Recent articles explore symbolic execution for probabilistic programs, risk-aware marine robotics planning, and privacy quantification in genetic data. He has contributed over 200 ROS bug findings and developed tools like Clafer for model-based design. Teaching & Community Teaches courses in probabilistic programming, advanced software analysis, and model-driven development. Active in open-source communities, including ROS and Clafer. Supervised 20+ PhD students who now hold academic and industry positions. Labs & Projects Leads the SQUARE Group and VBDB bug database. Coordinates the REMARO EU training network. Previously worked on ROSIN (robot software quality) and EBA (Linux kernel analysis).

• Programming Languages
• Formal Methods
• Logic in Computer Science
• +۹ مورد دیگر

Dmitriy Traytel is an Associate Professor in the Department of Computer Science at the University of Copenhagen, affiliated with the Software, Data, People & Society research section. His work bridges formal methods, programming languages, and runtime verification, with a strong emphasis on correctness and efficiency. His research focuses on the logical and formal foundations of programming systems, including syntax with bindings, higher-order logic, type systems, and temporal logics. He develops verified tools and frameworks for runtime monitoring, policy enforcement, and query evaluation, often using interactive theorem provers like Isabelle/HOL. His recent publications demonstrate a consistent contribution to top venues such as POPL, CAV, and TACAS. The trends in his recent articles show a deep engagement with runtime verification , particularly in developing explainable , efficient , and first-order monitoring techniques. He combines theoretical rigor with practical tool building, as seen in systems like WHYMON and TimelyMon. His work often intersects with security policies, functional programming, and formal semantics. He has not been mentioned in the text as receiving scientific awards, but his publication record in premier venues indicates high scholarly impact. While no students are explicitly listed, his role as an Associate Professor and active researcher suggests involvement in advising. There is no mention of specific grants, but his participation in a Promotion Programme implies institutional support. He is part of a vibrant research environment within the Department of Computer Science, which is involved in the SCIENCE AI Centre, suggesting interdisciplinary collaboration potential. Traytel maintains a personal website and ORCID profile, and his contact information is publicly available. He is actively contributing to the research output of the department, with 59 recorded publications, including journal articles and conference proceedings.

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.

Teddy Grovesمشاهده پروفایل

پژوهشگر

• Metabolic Networks
• Bayesian Regression
• Cell Metabolism
• +۱۲ مورد دیگر

Teddy Groves is a Tenure Track Researcher at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU), focusing on quantitative modeling of cell metabolism and computational methods. His work bridges systems biology, machine learning, and biotechnology. Research Themes : Quantitative cell metabolism, neurovascular coupling, CRISPR-based apoptosis resistance, and data-driven bioreactor optimization. Supervision : Actively supervises PhD students in projects related to human glycolysis, multiscale modeling, and cyanobacteria enzyme allocation. Key Contributions : Developments in Bayesian regression, high-dimensional pathway visualization, and CRISPR knockouts in CHO cells.

Christian Schillingمشاهده پروفایل

دانشیار

• Formal Verification
• Hybrid Systems
• Neural Networks
• +۳ مورد دیگر

Christian Schilling is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. He holds a PhD in Computer Science from the University of Freiburg (2018) and leads research in distributed, embedded, and intelligent systems. His research focuses on formal verification techniques for hybrid systems, neural network control systems, and quantum computing applications. Key methodologies include reachability analysis, policy synthesis, and benchmark development for continuous-time systems. Recent publications demonstrate a consistent focus on verification of AI-controlled systems, with emerging work in quantum circuit equivalence checking. His articles frequently appear in top formal methods venues and benchmark reports. Schilling coordinates multiple research projects including: EQuaL: Quantum circuit verification via tensor decision diagrams Cosyne: Safe control systems with neural networks STORM_SAFE: Safety verification for critical infrastructure He maintains active collaborations across Europe and leads the development of JuliaReach tools for set-based computations.

J. Eduardo Vera-Valdésمشاهده پروفایل

دانشیار

• Econometrics
• Time Series Analysis
• Climate Econometrics
• +۵ مورد دیگر

J. Eduardo Vera-Valdés is an Associate Professor in Mathematics-Economics at the Department of Mathematical Sciences, Aalborg University, and Research Group Leader for the Time Series Analysis and Econometrics Group. He holds a PhD in Econometrics from Aarhus University (2016) and is affiliated with the CoRE Center for Research in Energy: Economics and Markets. His expertise spans econometrics, climate modeling, and statistical learning, with a focus on long memory processes and their applications in climate and financial systems. Education: PhD in Econometrics (2016, Aarhus University), Certified GitHub Campus Advisor (2020). Research interests include climate econometrics, financial volatility, and energy systems. He has published in top journals like the Journal of Econometrics and organizes events such as the Earth Day Climate Change Research conferences. His projects include analyzing climate policy impacts (e.g., Paris Agreement), energy optimization (FOREFRONT), and the economic repercussions of pandemics. Key activities include serving as a referee for major journals, organizing the Long Memory Conference, and leading research on sustainable energy systems. He collaborates on datasets like the Residential Household Dataset (Zenodo, 2025) and contributes to open-source tools like LongMemory.jl.

Rasmus Ejlers Møgelbergمشاهده پروفایل

استاد

• Denotational semantics of programming languages
• type theory
• category theory
• +۵ مورد دیگر

Rasmus Ejlers Møgelberg is a Professor at the IT University of Copenhagen, where he leads the Section for Theoretical Computer Science and coordinates the Programming, Logic and Semantics research group. His academic career includes prior postdoctoral positions at DISI (Universita di Genova) and LFCS (University of Edinburgh). His research spans denotational semantics of programming languages , type theory , category theory , guarded recursion , and domain theory , with applications in modeling computational effects and parametric polymorphism. Current work focuses on guarded type theory for reactive and probabilistic programming systems. Recent publications (2019-2025) demonstrate consistent innovation in guarded recursion frameworks, including denotational models for clocked type theory, guarded dependent types, and modal logics for reactive programming. Key trends involve bridging category theory with practical programming language features to handle recursion, concurrency, and probabilistic choice. He has supervised three PhD students to completion: Magnus Baunsgaard Kristensen (2022, Cubical Modal Type Theories), Christian Uldal Graulund (2021, Type Theories for Reactive Programming), and Marco Paviotti (2016, Denotational Semantics in Guarded Type Theory). Postdoctoral researchers include Maaike Zwart (current), Andrea Vezzosi, Niccolo Veltri, Bassel Mannaa, and Patrick Bahr (now ITU associate professor). His research is funded by the Independent Research Fund Denmark project "Algebraic Effects and Guarded Recursion" (2022-2025). As coordinator of the Programming, Logic and Semantics group, he directs theoretical research on programming language foundations, emphasizing denotational semantics and type systems for next-generation language design.

Andrzej Wasowskiمشاهده پروفایل

استاد

• Software Engineering
• Software Product Lines
• Model Driven Development
• +۵ مورد دیگر

Andrzej Wasowski is a Professor and research group leader at the IT University of Copenhagen , specializing in Software Engineering and Software Quality Research (SQUARE) . He leads projects involving underwater robotics , AI verification , and probabilistic programming , with a focus on model-driven development and variability in systems . Key Collaborations: Innovation Fund Denmark, European Commission, Danmarks Frie Forskningsfond Research Trends include underwater image segmentation , safety analysis of autonomous vehicles , and symbolic methods in probabilistic programs . His work integrates formal methods with real-world applications in marine technology. Projects like VARIETE and DIREC explore dependable systems and verifiable AI . He has contributed datasets to ZENODO and participated in workshops on domain-specific language design and software testing .

• Bayesian Statistics
• Gaussian Processes
• Variational Inference
• +۵ مورد دیگر

Michael Riis Andersen is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute , Technical University of Denmark. His research focuses on Bayesian statistics, Gaussian processes, variational inference, and probabilistic programming, with applications in computer vision, natural language processing, and spatio-temporal modeling. His recent work includes: (1) Bayesian optimization with uncertainty-aware frameworks, (2) recommender systems balancing accuracy with editorial constraints, (3) geometry-aware transformers for geospatial analysis, and (4) scalable inference methods for neural networks and Gaussian processes. Many articles explore variational inference and probabilistic programming to enhance computational reliability and efficiency. Research keywords : Bayesian Statistics, Variational Inference, Machine Learning, Computer Vision, Probabilistic Programming, Spatio-Temporal Modeling.

• Natural Language Processing
• Multimodal Understanding
• Machine Translation
• +۳ مورد دیگر

Israfel Salazar is a Research Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Natural Language Processing research group . His work focuses on multimodal understanding and representation, bridging natural language processing, machine translation, computer vision, genomics, and historical data analysis. Email : israfel.salazar@di.ku.dk Phone : +4535337068 Research interests include: Culturally aware multimodal machine translation Massively multilingual vision evaluation Genomic offset statistics Temporal data extraction from historical documents His recent publications highlight interdisciplinary approaches combining language and vision systems, statistical modeling for biological data, and historical record analysis. He contributes to benchmarking frameworks and evaluation methodologies for modern AI applications. Israfel's work spans both foundational and applied research domains, with a focus on cross-modal learning, computational methods in bioinformatics, and automated analysis of historical archives. His projects often require hybrid techniques integrating linguistic and visual modalities. He operates within the Natural Language Processing section at DIKU, which also hosts research into probabilistic programming applications in bioinformatics, computer vision integration with NLP, and energy-efficient computational frameworks. Collaborations with other DIKU groups (e.g., FUTHARK for GPU acceleration, quantum programming) may inform his technical implementations.

• Probabilistic Machine Learning
• Deep Probabilistic Programming
• Statistical Structural Bioinformatics
• +۵ مورد دیگر

Thomas Wim Hamelryck is a Professor at the Department of Computer Science (Programming Languages and Theory of Computation) and the Department of Biology (Computational and RNA Biology) at the University of Copenhagen . With a PhD in Protein Crystallography from the Free University of Brussels (VUB), he specializes in Bayesian modeling , probabilistic machine learning , and statistical structural bioinformatics , focusing on protein structure prediction, evolution, and non-Euclidean data representation.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +۲ مورد دیگر

Nicklas Boserup serves as an Instructor at the Department of Computer Science (DIKU) at the University of Copenhagen, located at Universitetsparken 1 in Copenhagen Ø. His academic appointment places him within the institution's research and teaching structure, contributing to computer science education and research activities. Boserup's research interests center on machine learning applications in medical imaging, with particular focus on self-supervised learning techniques for histopathology image analysis. His work bridges computer vision and healthcare applications, developing algorithms that can process and segment medical images without requiring extensive labeled training data. This research direction addresses critical challenges in digital pathology where annotation by medical experts is time-consuming and costly. Analysis of Boserup's publication record shows a consistent focus on contrastive learning approaches for medical image segmentation, with his 2022-2023 work establishing foundational techniques in patch-based contrastive learning for histopathology. His 2024 publication represents an expansion into parameter inference methods using differentiable diffusion models, indicating a broadening research scope into statistical computing and probabilistic modeling. The trajectory suggests increasing sophistication in handling complex medical imaging data through advanced machine learning techniques. Boserup maintains an active research profile with publications appearing in both preprint repositories and conference proceedings, demonstrating engagement with the academic community. His email contact (nibos@di.ku.dk) provides a direct channel for academic collaboration and professional communication within the university framework.

• 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
• 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.