جست‌وجوی استاد و پژوهشگر

Martin Elsmanمشاهده پروفایل

استاد

• Programming Languages
• Theory of Computation
• Functional Programming
• +۵ مورد دیگر

Martin Elsman is a full-time Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science, University of Copenhagen (DIKU). He serves as head of the PLTC section and head of studies for the BSc education in Computer Science and Economics. Elsman is also an active maintainer of several software tools including the MLKit and SMLtoJs. Joined DIKU in 2012 after 4 years at SimCorp (2008-2012) and previous Associate Professorship at IT University of Copenhagen (2003-2008). Co-developer of Futhark, TAIL APL compiler, SMLtoJs, and SMLserver. Education: M.Sc. in Engineering, Technical University of Denmark Ph.D. in Computer Science, University of Copenhagen (DIKU), supervised by Mads Tofte. Research Interests: Elsman works on programming language design and implementation, with a focus on functional programming, module systems, domain-specific languages for financial contracts, region-based memory management, compilation techniques for parallelism, program optimization, and static type systems. His work spans both theoretical and applied domains, including blockchain-based financial contract execution, web technology, and GPU programming using functional languages. Publication Trends: His recent articles focus on functional programming, array programming, parallelism, and memory management. Topics include region inference, type systems for data-parallelism, program optimization techniques, and domain-specific compilation for financial and quantum computing. He frequently collaborates with Troels Henriksen and others on tools like Futhark and MLKit.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +۴ مورد دیگر

Jonas Vinther is a Research Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its intersections with quantum computing, medical data analysis, and sustainability. He is also an external PhD student in the Quantum Information Science & Technology program at the Niels Bohr Institute. Email: jonas.vinther@nbi.ku.dk , jonas.vinther@di.ku.dk Location: Universitetsparken 1, 2100 København Ø His research spans quantum machine learning , AI ethics , medical imaging , and environmentally sustainable AI , with recent publications on topics ranging from quantum neural networks to fairness in recommender systems . He contributes to the SCIENCE AI Centre and collaborates on initiatives like TreeSense for global tree resource monitoring.

Juan Maria García Lastraمشاهده پروفایل

استاد

• Lithium-ion batteries
• Energy Storage Materials
• Density Functional Theory (DFT)
• +۵ مورد دیگر

Juan Maria García Lastra is a Professor and Head of the Atomic Scale Materials Modelling group in the Department of Energy Conversion and Storage at the Technical University of Denmark (DTU). His research focuses on theoretical studies of solid-state and molecular materials using advanced computational methodologies such as Density Functional Theory (DFT), Many-Body Perturbation Theory (MBPT), and Non-Equilibrium Green’s Functions (NEGF). Education: Ph.D. from the University of Cantabria (Spain), specializing in DFT developments. Postdoctoral research focused on energy-related materials including photovoltaic systems and battery technologies. Research interests include: Lithium-ion batteries, metal-air batteries, organic photovoltaics, heterogeneous catalysis, and electronic/thermal transport in nanomaterials like carbon nanotubes and graphene. His work contributes to UN Sustainable Development Goals through energy storage and conversion advancements. He supervises multiple PhD projects on topics like battery recycling strategies, computational design of energy materials, and machine learning applications in materials science. Collaborations span global institutions, with recent activities in sodium metal batteries, magnesium-sulfur battery electrolytes, and electrode design optimization. Labs/Groups: Leads the Atomic Scale Materials Modelling group at DTU Energy, focusing on computational methods for energy materials discovery and characterization.

• Human-Computer Interaction
• Machine Learning
• Natural Language Processing
• +۱ مورد دیگر

Sarah Frances Homewood is an Assistant Professor (Tenure Track) in the Department of Computer Science at the University of Copenhagen, affiliated with the Human-Centred Computing research section. Her research focuses on the intersection of human-computer interaction and artificial intelligence, with applications in healthcare, natural language processing, and interpretable machine learning. Her diverse research interests span Human-Computer Interaction, Machine Learning, Natural Language Processing, and Artificial Intelligence. Recent investigations include interpretability of large language models, clinical NLP applications, fairness in recommender systems, and quantum natural language processing. Analysis of her recent publications reveals strong emphasis on NLP interpretability techniques, healthcare applications of AI, and theoretical foundations of machine learning. Her work frequently bridges fundamental computer science with practical applications in medicine and human-centered systems. Emerging research directions include quantum NLP and protein sequence modeling. Dr. Homewood's research contributes to the Machine Learning Section's focus on both theoretical foundations and applied domains including medical data analysis and information retrieval.

Andreas Pavlogiannisمشاهده پروفایل

دانشیار

• formal methods
• algorithmic verification
• automata theory
• +۶ مورد دیگر

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University. His research focuses on formal methods , algorithmic verification , automata theory , concurrency , static and dynamic program analysis , network diffusion , evolutionary graph theory , and evolutionary game theory . Teaching courses: Programming Languages (Bachelor) , Algorithmic Model Checking (Master) , and Program Analysis (Master) Service: Program committee member for POPL, ESOP, AAAI, IJCAI, CONCUR, OOPSLA, and organizer of CONFEST'25 His research has been supported by the Austrian Science Fund (FWF), VILLUM Foundation, Stibo Foundation, and Danish Council for Independent Research (DFF). He is actively recruiting PhD and PostDoc researchers. Recent publications span quantum computing , concurrent systems , evolutionary dynamics , and network science , with particular emphasis on symbolic algorithms , dynamic analysis , and graph-based models .

Hou Man Chinمشاهده پروفایل

استاد مهمان

• Quantum Physics and Information Technology
• Machine Learning in Photonic Systems
• Quantum Key Distribution
• +۳ مورد دیگر

Hou Man Chin is a Visiting Professor at the Technical University of Denmark (DTU), affiliated with the Department of Electrical and Photonics Engineering and the Department of Physics. His research spans Quantum Physics and Information Technology, focusing on Machine Learning in Photonic Systems. He explores applications in quantum key distribution, optical communication systems, and quantum cryptography, with a particular emphasis on securing network infrastructure through advanced quantum protocols. Key areas of research include squeezed light generation and recovery, digital signal processing for quantum communication, and overcoming technical challenges like phase noise and signal degradation in long-distance systems. His work addresses practical implementation of quantum technologies, such as composable security frameworks and real-world deployment of quantum encryption methods. Hou Man Chin collaborates with experts in quantum technologies, including Ulrik Lund Andersen and Darko Zibar, on projects related to quantum information processing and machine learning integration. His research group (qTReX) develops semi-autonomous quantum key distribution systems and investigates vulnerabilities in modulation leakage and carrier recovery mechanisms.

• Particle physics
• High-energy physics
• Beyond Standard Model
• +۷ مورد دیگر

Jørgen Beck Hansen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen, specializing in Experimental Subatomic Physics . His career spans roles at CERN and NBI, focusing on particle physics detectors, high-energy collisions, and computational methods. Education: Ph.D. in Particle Physics (1996) and M.Sc. in Physics (1993) from the University of Copenhagen. Research interests include two-boson physics at the ATLAS detector, precision measurements within the Standard Model, effective Lagrangian densities, and searches for new physics beyond the Standard Model (e.g., Higgsless theories, extra dimensions). He also works on GRID computing and distributed data analysis. Recent trends in his research involve Higgs boson studies, vectorlike top quarks, photonuclear collisions, and detector trigger optimization. Collaborative efforts with the ATLAS Collaboration and Danish NORDUGRID team highlight his interdisciplinary approach. Scientific awards : Skou Stipend (Assistant Professor) from the Danish Natural Science Council Teaching and supervision include mentoring 5 summer students, 2 Ph.D., 4 Master's, and 10 Bachelor's students. He has contributed to popular science through Danish Cosmic Rays at Schools and public lectures. Labs and teams : Actively involved in the ATLAS Collaboration and the Danish NORDUGRID team for distributed computing infrastructure.

• Machine Learning
• Quantum Computing
• Computational Biology
• +۲ مورد دیگر

Lasse Bjørn Kristensen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning with a focus on quantum computing applications. Research Interests His work bridges quantum computing, machine learning, and computational biology, with contributions to: Quantum neural networks and spiking neurons Quantum error correction and circuit robustness Quantum chemistry simulations Information flow in parametrized quantum systems Notable Research Trends Kristensen's publications reveal a strong emphasis on quantum-classical hybrid models, entanglement-enhanced devices, and computational methods for chemistry and physics. His recent work explores error-driven learning paradigms and quantum eigensolvers. Contact Email: lakr@di.ku.dk Address: Universitetsparken 1, 2100 Copenhagen Ø

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

Darko Zibarمشاهده پروفایل

استاد

• Machine Learning in Photonic Systems
• Optical Communication
• Quantum Communication
• +۳ مورد دیگر

Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).

Tejs Veggeمشاهده پروفایل

استاد

• Autonomous materials discovery
• Energy conversion and storage
• Battery materials
• +۴ مورد دیگر

Tejs Vegge is a Professor and Head of the Section for Autonomous Materials Discovery (AMD) at the Department of Energy Conversion and Storage, Technical University of Denmark (DTU). His research focuses on accelerating the discovery of clean energy materials through integrated computational and experimental approaches. Key areas include battery materials, hydrogen/ammonia storage, and electrocatalysts for sustainable fuels. Vegge leads major initiatives like the Pioneer Center CAPeX and the Villum Center V-Sustain, and has received awards such as the August-Wilhelm Scheer Visiting Professorship and the Ellen and Hans Hermers Award. His projects include the CAPeX Center (2023–2036) for P2X materials, the DELIGHT project (2021–2024) on green catalysis, and collaborations like Battery2030+. He supervises multiple PhD students in topics like battery electrolytes, autonomous workflows, and machine learning for materials design. Research Themes: Autonomous materials discovery, AI-driven experiments, sustainable energy systems Key Tools: Density Functional Theory (DFT), high-throughput screening, self-driving labs Recent Trends: Focus on strain engineering for ion conductivity, CO₂ reduction catalysts, and quantum computing applications Publications span over 300 articles, emphasizing interdisciplinary methods like reinforcement learning and Bayesian optimization. Vegge advocates for open-access research infrastructure and has pioneered lab automation frameworks like Finales.

Thomas Willum Hansenمشاهده پروفایل

استاد

• Transmission Electron Microscopy
• Catalyst Materials
• Nanoparticle Analysis
• +۳ مورد دیگر

Thomas Willum Hansen is a Professor at the Technical University of Denmark (DTU), affiliated with the National Centre for Nano Fabrication and Characterization. His primary role involves advancing atomic-scale materials dynamics through aberration-corrected environmental TEM (ETEM). He oversees the ETEM facility and trains users in electron microscopy techniques. Hansen holds a PhD from DTU (2002–2006), followed by a Postdoc at the Fritz Haber Institute (2006–2008) and a Research Assistant position at Haldor Topsøe A/S (2001–2002). His research focuses on catalyst materials characterization, nanostructured materials, and microscopy innovations. Key areas include transmission electron microscopy (TEM), nanoparticle dynamics, and in-situ TEM studies. Recent work explores beam-induced heating effects, machine learning in microscopy, and solar degradation mechanisms in energy materials. Hansen supervises multiple PhD projects, including studies on quantum emitters in 2D materials, pyrolytic carbon for energy storage, and high-resolution TEM of catalytic surfaces. His contributions align with sustainable development goals through advanced material science.

Ashutosh Dhar Dwivediمشاهده پروفایل

استادیار

• Cybersecurity
• Post-Quantum Cryptography
• Blockchain Security
• +۴ مورد دیگر

Ashutosh Dhar Dwivedi is an Assistant Professor in the Cybersecurity Group at Aalborg University, Copenhagen, Denmark. He specializes in blockchain security, applied cryptography, post-quantum cryptography, and advanced cybersecurity. His interdisciplinary research spans cryptography, IoT security, and AI-driven security analytics. Education: PhD in Cryptography, with postdoctoral research at institutions including the University of Waterloo, Technical University of Denmark, and the Polish Academy of Sciences. His pedagogical focus includes professional upskilling in cyber defense and post-quantum resilience. Research interests include post-quantum cryptographic protocols, privacy-preserving blockchain systems, and machine learning for security. His work has yielded over 50 peer-reviewed papers, including contributions to high-impact journals and conferences. Notable achievements: 2023 and 2024 Stanford University Top 2% Scientist ranking. Contributions: Editorial roles in international journals, program committees for premier conferences, and leadership in academic-industry collaborations like the Quantum Communication Infrastructure (QCI) consortium. Active in developing quantum-secure systems for national and industrial infrastructure.

Steen Lysgaardمشاهده پروفایل

پژوهشگر

• Computational Materials Science
• Genetic Algorithms
• Battery Technology
• +۵ مورد دیگر

Steen Lysgaard is a Scientific Software Developer at the Department of Energy Conversion and Storage , Technical University of Denmark . His work focuses on computational materials modeling for energy applications, particularly in battery technology and nanoalloy stability . He actively employs genetic algorithms and machine learning to accelerate materials discovery. His research contributes to UN Sustainable Development Goals in clean energy and climate action . Research Trends The 15 most recent publications highlight his expertise in: Computational methods : Integration of Bayesian evolutionary multitasking , genetic algorithms , and DFT simulations for materials design. Battery technology : Studies on aluminum batteries , zinc-air batteries , and ammonia storage systems. Nanomaterials : Structural stability analysis of nanoalloys , metal halides , and charge transport mechanisms in energy storage compounds. Professional Activities He has presented at conferences on topics such as: Atomic Simulation Environment software (2017) Zinc-air battery materials (2018) Strontium ammines (2010) NH3 diffusion in Mg(NH3)6Cl2 (2010) Contact: stly@dtu.dk

• Computational Chemistry
• Quantum Chemistry
• Theoretical Chemistry
• +۲ مورد دیگر

Thorsten Hansen serves as Associate Professor in the Department of Chemistry at the University of Copenhagen, Denmark, with his office located at Universitetsparken 5, 2100 København Ø. His research profile includes 63 publications comprising 60 journal articles, 1 book chapter, 1 conference proceeding, and 1 poster, reflecting sustained scholarly activity through 2024. Hansen's research spans computational chemistry, quantum chemistry, and theoretical chemistry with specialized focus on molecular modeling of photosynthetic systems and photochemical processes. He employs advanced methodologies including density functional theory, multireference approaches, and machine learning to investigate electronic structures, energy transfer mechanisms, and reaction dynamics in complex molecular systems. His work bridges chemistry and physics with applications in renewable energy materials and molecular electronics. Analysis of his 2021-2024 publications reveals consistent emphasis on computational modeling of photosynthetic complexes, organic semiconductors (particularly Subphthalocyanine derivatives), and surface-molecule interactions. Key thematic trends include quantum mechanical studies of energy level variations, machine learning applications for predicting electronic properties, and photochemical reaction mechanisms. His collaborative network spans international institutions with co-authorship patterns indicating strong cross-disciplinary engagement in physical chemistry and materials science.