جست‌وجوی استاد و پژوهشگر

• Operator Algebras
• K-theory
• Symbolic Dynamics
• +۴ مورد دیگر

Søren Eilers is a Professor at the Department of Mathematical Sciences, University of Copenhagen, affiliated with both the QA (Analysis & Quantum) and AG (Algebra & Geometry) sections. He holds editorial roles at Journal of Mathematical Analysis and Applications and contributed to Springer's Operator Algebra and Dynamics proceedings. Education : M.S. (1993), PhD (1995) in Mathematics from the University of Copenhagen. Positions : Assistant Professor (1996–1999), Associate Professor (1999–2008), Professor (2008–present). Research Interests : Focus on operator algebras, particularly C*-algebras associated with discrete structures. Active in symbolic dynamics, K-theory, and experimental mathematics. His work bridges algebraic structures and dynamical systems, with notable contributions to the classification of graph C*-algebras and the LEGO counting problem. Recent efforts emphasize computer-aided methods in mathematical research. Key Contributions : Pioneered geometric classification of graph C*-algebras, explored flow equivalence of shift spaces, and authored Introduction to Experimental Mathematics (2017). His research has been supported by grants such as the Villum Fonden's Experimental Mathematics initiative (2012–2016). Teaching & Mentorship : Supervised 28 PhD theses and numerous projects in analysis, discrete mathematics, and experimental mathematics. Taught courses in functional analysis, dynamical systems, and experimental methods. Scientific Leadership : Organized major programs like the 2016 Mittag-Leffler Institute's classification of operator algebras. Served as President of the Danish Mathematical Society (2006–2008) and led NordForsk's Operator Algebras and Dynamics network (2009–2012). International Collaboration : Extensive visiting appointments at institutions like MSRI (Berkeley), Fields Institute (Toronto), and Institut Mittag-Leffler (Stockholm). Maintains global research networks in operator algebras and dynamics.

Izabela Ewa Nielsenمشاهده پروفایل

استاد

• Artificial Intelligence
• Operations Research
• Unmanned Aerial Vehicles
• +۵ مورد دیگر

Izabela Ewa Nielsen is a Professor at Aalborg University's Department of Materials and Production under The Faculty of Engineering and Science. Her research focuses on artificial intelligence applications in operations research, unmanned aerial vehicles (UAVs), genetic algorithms, and mobile robotics. She holds a degree from Warsaw University of Technology (25 Oct 2025). Research Interests: Her work integrates AI with logistics optimization, health data analysis, and sustainable supply chains. Notable projects include the EU-funded 'Operational Reliability Management System (ORMS)' and 'UAWORLD', exploring UAVs in industrial settings. Projects & Collaborations: Leading ORMS (2016-2019) to enhance operational reliability through AI-driven solutions. Contributing to TAPAS (2010-2014), advancing robotics in factory automation. Co-developing ValuePole (2008-2011) for SME performance optimization. Advising & Grants: Supervised projects such as the EU classification methodology study (2022-2023) and contributed to over 6 major research initiatives. Her work frequently involves interdisciplinary teams and industry partnerships. Labs & Teams: Active in AI for Operations Research labs, collaborating with robotics and logistics experts. Her group focuses on real-world applications of autonomous systems in manufacturing and healthcare.

Morten S. Risagerمشاهده پروفایل

استاد

• Analytic Number Theory
• Spectral Theory for Automorphic Forms
• Spectral Deformations
• +۷ مورد دیگر

Morten S. Risager is a Professor in the Department of Mathematical Sciences at the University of Copenhagen. His research focuses on intersections of analytic number theory , automorphic forms , and mathematical physics , particularly in quantum chaos and hyperbolic geometry . He has held positions at Aarhus University and the Institute for Advanced Study, Princeton. Education: Ph.D. in Mathematics (2003, University of Aarhus), M.Sc. in Mathematics (2001, University of Aarhus). Research interests include modular symbols , Eisenstein series , lattice counting problems , and Selberg's conjecture . His recent work analyzes equidistribution in hyperbolic groups and spectral deformations. Publications (2014–2025) cover topics like shifted convolution sums , iterated integrals of modular forms , and hyperbolic circle problems . Collaborations with Petridis and Nordentoft highlight trends in spectral theory and number theory. Scientific awards : Steno Research Fellow (2005–2008) at Aarhus University. Teaching areas encompass linear algebra , analysis , elliptic curves , and analytic number theory .

Joel Daniel Anderssonمشاهده پروفایل

پژوهشگر ارشد

• Differential Privacy
• Continual Observation
• Streaming Algorithms
• +۳ مورد دیگر

Joel Daniel Andersson is a Research Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Faculty of Science . He is a member of the Providentia group under the supervision of Rasmus Pagh , focusing on differentially private algorithms in the continual observation setting. His work bridges theoretical challenges in privacy-preserving data analysis with practical applications in machine learning and statistics. PhD in progress (third-year) at the University of Copenhagen Former Master's student in Engineering Physics (Lund University, 2020) Previous industry experience: Software Engineer at Ericsson (5G protocols) Research Interests Joel's research explores differential privacy in streaming algorithms , particularly for continual observation —a framework where private statistics must be released incrementally over time. He investigates optimal solutions for problems like binary continual counting, comparing noise mechanisms (Laplace vs. Gaussian), and designing efficient algorithms for dynamic data streams. Earlier work includes computational modeling of beam dynamics at CERN for high-energy physics applications. Recent Publications His publications span theoretical computer science and computational physics , appearing in venues like NeurIPS , ICML , FORC , and physics journals. Key themes include: Differential privacy in streaming and machine learning Algorithmic design for continual data release Computational modeling for particle accelerators Privacy expiration and noise mechanism trade-offs Affiliations Joel collaborates with: BARC (Basic Algorithms Research Copenhagen) group Providentia group Adam Smith's lab at Boston University (visiting researcher, Jan–Apr 2025)

Vivek Shendeمشاهده پروفایل

استاد

• Quantum Mathematics
• Symplectic Geometry
• Algebraic Geometry
• +۷ مورد دیگر

Vivek Shende is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads research in quantum mathematics, symplectic geometry, and mathematical physics. His work bridges deep theoretical mathematics with concepts from string theory and quantum field theory. His primary research interests include: Homological Mirror Symmetry Fukaya Categories and Wrapped Fukaya Categories Microlocal Sheaf Theory Topological Open Strings Lagrangian and Symplectic Geometry Algebraic Geometry and Higgs Bundles Shende's recent publications (2023–2025) in premier journals reflect a consistent focus on categorical and geometric structures in mirror symmetry, with frequent collaborations with leading mathematicians such as Sheel Ganatra, John Pardon, and David Nadler. His work often involves advanced tools from derived categories, homological algebra, and microlocal analysis to solve problems in symplectic topology and mathematical physics. He has been recognized with the Villum Investigator Grant (2021), a major Danish research award supporting his project Mathematics of the topological open string (2021–2027). He was also a participant in the DNRF Chair project (2021–2024), further affirming his status as a leading figure in mathematical research. Shende has no named advisees listed in the provided material, but his role as Principal Investigator indicates leadership in mentoring and guiding research. He is actively involved in large-scale collaborative research and has no indication of retirement or former status.

Ioana O. Berceaمشاهده پروفایل

استادیار

• Data Structures
• Randomized Algorithms
• Approximation Algorithms
• +۱ مورد دیگر

Ioana O. Bercea is a tenure-track Assistant Professor in the Computer Science Department at KTH Royal Institute of Technology, affiliated with the Division of Theoretical Computer Science. She holds a PhD in Computer Science from the University of Maryland (advised by Samir Khuller), a Master's from the same institution (advised by Aravind Srinivasan), and a BS in Mathematics and Computer Science from the University of Chicago. Her research focuses on Data Structures (dictionaries, Bloom filters), Randomized Algorithms (hashing, clustering), and Computational Geometry (Traveling Salesman Problem). Her recent publications emphasize scalable data structures, dynamic filters, and algorithmic efficiency, with applications in database systems and high-dimensional optimization. Awards & Honors: Best Artifact of ACM SIGMOD 2023 Future Digileader (Digital Futures, 2023) VR Starting Grant (2024) She advises PhD student Jonas Østergaard Klausen (University of Copenhagen) and collaborates with research groups including BARC (Copenhagen) and Digital Futures. Her VR Starting Grant funds the project "DataTech: Techniques for scalable data storage and analysis."

• Computational Complexity
• Counting Problems
• Parameterized Complexity
• +۵ مورد دیگر

Radu-Cristian Curticapean is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen. His research focuses on computational complexity theory, particularly in counting problems, homomorphisms, and parameterized complexity. He is actively involved in multiple research projects including CountHom (Counting with homomorphisms), CountBeyond, and DIREC initiatives. His research interests center around computational complexity with emphasis on counting problems, graph homomorphisms, and polynomial time algorithms. His work explores fundamental questions in counting complexity, complexity dichotomies, and the boundaries of efficient computation. His research combines theoretical computer science with mathematical approaches to understand the inherent difficulty of computational problems. Analysis of his publications reveals a strong focus on computational complexity theory, particularly in counting problems and homomorphisms. His research consistently addresses fundamental questions in complexity theory, exploring the boundaries between tractable and intractable problems. His work spans algebraic complexity, parameterized complexity, and structural graph theory, with applications to understanding the permanent, immanants, and other fundamental computational problems. Dr. Curticapean serves as Principal Investigator for the CountBeyond and CountHom projects, and as Co-Investigator for the BARC (Basic Algorithms Research Copenhagen) project. His research is supported by prestigious funding bodies including the European Commission, Villum Fonden, and Innovation Fund Denmark. He has presented his work at major theoretical computer science conferences including SODA, ESA, and FSTTCS.

Peyman Afshaniمشاهده پروفایل

دانشیار

• Geometric algorithms and data structures
• Algorithmics
• Algorithms for hierarchical memory
• +۳ مورد دیگر

Peyman Afshani is an Associate Professor in the Department of Computer Science at Aarhus University, Denmark. His research focuses on geometric algorithms, data structures, and algorithms for hierarchical memory systems. He maintains an active research profile with consistent publications since 2009, including 61 research outputs as documented. His primary research interests include: Geometric algorithms and data structures Algorithmics Algorithms for hierarchical memory Range Query optimization Computational Geometry Analysis of his publication trends since 2009 shows sustained output with 3-5 publications annually, peaking at 5 in multiple years (2011, 2012, 2014, 2017, 2019, 2021, 2023, 2024). His recent work (2023-2025) demonstrates continued focus on computational geometry problems including simplex range reporting, semialgebraic range reporting, and convexity in 3D search algorithms. His research shows strong theoretical foundations with practical applications in data structure design and query optimization. Collaboration networks indicate significant engagement with international researchers, particularly in computational geometry and data structure research domains. Professor Afshani supervises PhD students (at least one documented case) and maintains an active research program with consistent output across two decades.

Matteo Boniniمشاهده پروفایل

استادیار

• Algebraic Geometry
• Coding Theory
• Finite Fields
• +۸ مورد دیگر

Matteo Bonini is an Assistant Professor in the Department of Mathematical Sciences at Aalborg University, under the Faculty of Engineering and Science. He is also associated with the AAU BLUE – Marine & Maritime Research initiative, indicating interdisciplinary engagement in real-world applications of mathematics. His research lies at the intersection of algebraic geometry, finite fields, and coding theory. PhD in Mathematics – Specific institution and year not mentioned in text Possibly postdoctoral experience inferred from publication history and project role His research interests center on algebraic geometry , coding theory , and combinatorics , particularly focusing on AG codes, covering codes, rational points on algebraic varieties, irreducibility of polynomials, and weight distributions in linear codes. His work often applies algebraic methods to solve problems in information theory and error correction. The fingerprint analysis from his profile highlights recurring themes such as Polynomial Mathematics, Weight Distribution, Rational Points, Linear Codes, and Algebraic Geometry. The recent publications show a consistent trend in exploring structural properties of codes—especially covering codes in sum-rank metrics, minimal codewords, and connections between algebraic curves and code design. His studies frequently involve deep algebraic techniques over finite fields, with applications in cryptography and data transmission. He collaborates internationally, as evidenced by co-authorships across European institutions. Currently, there are no scientific awards mentioned in the provided text. Matteo Bonini serves as a Principal Investigator in the ongoing project Blue Math: Blue Mathematics at AAU , which aims to apply advanced mathematical methods to marine and maritime challenges. While no formal students are listed, his role as a PI and active researcher suggests mentorship responsibilities. No specific grants are detailed beyond this project. He is affiliated with the Mathematics group within the Department of Mathematical Sciences at Aalborg University, contributing to both theoretical and applied research, particularly through the AAU BLUE initiative that connects mathematical modeling with engineering and real-life systems.

• Coding Theory
• Algebraic Geometry Codes
• Finite Fields
• +۷ مورد دیگر

Hans Olav Geil is a Professor and Pro-dean at the Faculty of Engineering and Science , Aalborg University , Denmark, affiliated with the Department of Mathematical Sciences . He is actively engaged in research, academic leadership, and educational innovation, particularly in coding theory and engineering education. University: Aalborg University School: Faculty of Engineering and Science Department: Department of Mathematical Sciences Email: prodekan-eng-udd@aau.dk ORCID: 0000-0002-9666-3399 Website: http://people.math.aau.dk/~olav Geil's research centers on algebraic coding theory , with significant contributions to affine variety codes , quantum codes , network coding , and finite fields . His work bridges theoretical mathematics and practical applications in data transmission and cryptography. He has developed methods for code construction, decoding, and performance bounds, particularly using Gröbner bases and algebraic geometry. His recent publications (2020–2023) show a dual focus: (1) advanced coding techniques like Steane-enlargement of quantum codes and nested code pairs, and (2) educational research on project-based learning (PBL) , digital transformation, and microcredentials at Aalborg University. This reflects his leadership in both technical research and pedagogical innovation. The body of his work, including over 79 publications, demonstrates consistent contributions to IEEE Transactions on Information Theory , Designs, Codes and Cryptography , and Finite Fields and Their Applications . His research often involves collaborations with experts in coding and cryptography. Geil has supervised several PhD students and is involved in multiple research projects. Though no specific awards are listed in the text, his editorial roles and sustained publication record indicate recognition in the academic community. He leads initiatives like the Vision-in-Practice Project , aiming to transform engineering education through structured frameworks and continuous improvement. His work supports interdisciplinary, problem-based learning models central to Aalborg University’s educational philosophy.

Bernd Dammannمشاهده پروفایل

دانشیار

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +۳ مورد دیگر

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

• Financial econometrics
• Econometric time series analysis
• Bootstrap methods
• +۴ مورد دیگر

Anders Rahbek is a Professor at the Department of Economics, Faculty of Social Sciences, University of Copenhagen. He has held this position since 2007 and has also served as a visiting Professor at Oxford University during Hilary Terms 2011-2012. His academic career at the University of Copenhagen spans from Assistant Professor (1996-1999) to Associate Professor (1999-2007) and finally to Professor (2007-present). Education: PhD in Econometrics, Institute of Mathematical Sciences (IMF), University of Copenhagen, 1996 Cand.Scient.Oecon (M.Phil), Mathematics and Economics, IMF, 1992 MSc in Econometrics, London School of Economics, 1991 MA in Mathematics, University of Pennsylvania, 1988 Professor Rahbek's research focuses on financial econometrics and time series analysis , with particular expertise in bootstrap methods, GARCH and volatility modeling, cointegration analysis, duration modeling, and count models. His work bridges theoretical econometrics with practical applications in financial and macroeconomic data analysis. He has developed innovative approaches for analyzing time series with time-varying volatility and has made significant contributions to bootstrap methodology in econometrics. His recent publications (2020-2025) demonstrate a continued focus on boundary problems in statistical testing, bootstrap methodology for complex time series models, and applications to financial volatility modeling. Key themes include GARCH-X models, cointegration in high-dimensional settings, Hawkes processes, and threshold autoregressions. His work often involves collaboration with leading econometricians like Giuseppe Cavaliere, Heino Bohn Nielsen, and Rasmus S. Pedersen. Scientific Awards: NYKREDIT RESEARCH AWARD (2014) Research Prize 2012: Reinholdt W. Jorck and Wife's Foundation (2012) Professor Rahbek has secured significant research funding as Principal Investigator, including multiple DFF-Advanced Grants (2012-2026) focusing on bootstrap methods and duration models in econometrics. He serves as Associate Editor for Econometric Theory and has previously held editorial positions at Econometrics Journal, Scandinavian Journal of Statistics, and Journal of Time Series Analysis. His Google Scholar h-index stands at 30 (as of December 2023). He is actively involved in international research networks, having initiated the Econometric Time Series European Research Network (ETSERN) in 2008. His teaching includes Financial Econometrics, Advanced Econometrics, and introductory Econometrics courses, with focus on volatility models, cointegration, and likelihood-based methods.

Thore Husfeldtمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Graph Algorithms
• +۳ مورد دیگر

Thore Husfeldt is a Professor of Theoretical Computer Science at the IT University of Copenhagen, leading the Algorithms Research Group. He holds a PhD from the University of Aarhus (1997), a Docent title from Lund University (2007), and has been a Professor at the University of Lund since 2012. His research focuses on algorithms, with a concentration on exponential time algorithms, combinatorial optimization, and graph theory. Education Bachelor (B.Sc.) in Computer Science and Mathematics, University of Aarhus (1993) Cand. scient. (M.Sc.) in Computer Science, University of Aarhus (1994) Ph.D. in Science (Computer Science), University of Aarhus (1997) Docent, Lund University (2007) Professor of Computer Science, Lund University (2012) Research Interests Husfeldt’s work explores foundational aspects of algorithms, including exponential time algorithms for hard problems, graph algorithms, and combinatorial optimization. His recent contributions address topics like cycle detection, path enumeration, and efficient algorithm design for NP-hard problems. His research bridges theoretical computer science with practical algorithmic challenges. Awards Best Paper Award at ICALP 2014 (Track A: Algorithms, Complexity, and Games) Excellence in Teaching Award (2018) Grants & Projects Husfeldt leads major initiatives such as the BARC2: Basic Algorithms Research Copenhagen (Villum Foundation), focusing on theoretical algorithm research, and DIREC , developing algorithms education through animated videos. He also collaborates on projects like Unifying Theories for Graph Modification Problems (Independent Research Fund Denmark). Labs & Teams He oversees the Algorithms Research Group at ITU and contributes to the BARC (Basic Algorithms Research Copenhagen) initiative, fostering collaborations in algorithmic theory and applications.

• 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

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +۴ مورد دیگر

Nutan Limaye is a Professor at the Department of Theoretical Computer Science , IT University of Copenhagen , specializing in Algorithms , Computational Complexity , and Algebraic Circuits . She actively contributes to research on polynomial complexity, quantum computation, and lower bound techniques. Key Research Areas : Algebraic Circuit Complexity, Polynomial Computation, Graph Isomorphism, Boolean Satisfiability Current Projects : FLows : Formula complexity and lower bounds (2024-2026) DIREC: OnlineAlgo : Digital research initiatives (2022-2025) BARC2 : Basic Algorithms Research Copenhagen (2024-2029) Scientific Recognition includes the FOCS Best Paper Award (2022) . Her work frequently appears in top conferences like CCC , FSTTCS , and SIGACT News , with recent collaborations in Denmark and international institutions. She contributes to public understanding through media appearances on topics like basic computer science research and BARC's initiatives .