جست‌وجوی استاد و پژوهشگر

Søren Hvidkjærمشاهده پروفایل

استاد

• Empirical Asset Pricing
• Market Microstructure
• Behavioral Finance
• +۳ مورد دیگر

Søren Hvidkjær is a Professor of Finance and currently serves as the Dean of Research at Copenhagen Business School (CBS). His academic career includes roles as an Associate Professor at INSEAD and the R.H. Smith School of Business at the University of Maryland before joining CBS in 2008. He is actively involved in research, policy analysis, and academic leadership, focusing on empirical asset pricing, market microstructure, and behavioral finance. His work has been published in leading journals such as the Journal of Finance and the Review of Financial Studies, earning accolades like the Journal of Finance Smith Breeden Distinguished Paper Prize. As Dean of Research, he advocates for reforms in PhD programs and addresses systemic challenges in academic funding and governance. His research explores liquidity dynamics, informed trading, and ESG investing, with notable contributions to understanding market efficiency and investor behavior. Beyond academia, he serves on boards for financial institutions and advises on investment strategies. His interdisciplinary approach bridges finance theory with practical policy solutions, addressing societal challenges through collaborative research initiatives. Research Interests: Empirical asset pricing, market microstructure, behavioral finance, liquidity management, and ESG investing. Awards: Journal of Finance Smith Breeden Distinguished Paper Prize (2002). Professional Activities: Board member at Danske Invest Management A/S, expert witness, and contributor to policy reports on financial regulation and educational equity. His articles span topics like co-illiquidity management, policy impacts of PhD program structures, and the role of universities in societal problem-solving. Hvidkjær’s work emphasizes quantitative analysis and real-world applications, making him a prominent figure in financial academia and policy circles.

• Financial Mathematics
• Quantitative Finance
• Interest Rate Modelling
• +۶ مورد دیگر

Rolf Poulsen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His work focuses on financial theory, derivative assets, and mathematical finance with applications to mortgage markets, portfolio selection, and model risk assessment. Institution: University of Copenhagen Department: Department of Mathematical Sciences Email: rolf@math.ku.dk Phone: +4535320685 Research Interests: Specializing in interest rate and exchange rate modeling, optimal mortgage and pension choices, and model risk analysis. His work intersects financial mathematics, stochastic processes, and computational finance, particularly in derivative pricing and market volatility dynamics. Recent Publications: Active in exploring connections between classical financial models (Black-Scholes, Merton) and modern machine learning approaches, while critically examining market efficiency and model reliability in crisis scenarios.

• Pure Mathematics
• Applied Mathematics
• Algebraic Geometry
• +۳ مورد دیگر

Alexander F Taveira Blomenhofer is a Postdoctoral Fellow at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on algebraic geometry and computational algebra. Research Interests: Power sum decomposition Tensor decomposition Algebraic statistics Optimization problems Publication Trends: Recent work in SIAM Journal on Applied Algebra and Geometry explores polynomial optimization and tensor decomposition algorithms. Keywords include Algebraic Geometry, Signal Processing, and Machine Learning.

Kimmo Kansanenمشاهده پروفایل

استاد مدعو

• Wireless Communications
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Kimmo Kansanen is an Adjunct Professor at Aalborg University , affiliated with the Department of Electronic Systems within the The Technical Faculty of IT and Design . His research focuses on cutting-edge wireless communication technologies, including Reconfigurable Intelligent Surfaces (RIS), Ultra-Reliable Low-Latency Communications (URLLC), and their applications in industrial and sensing systems. He has collaborated internationally on projects involving machine learning integration in wireless networks and industrial robotics. Main Research Themes : Wireless Sensing and Localization Reconfigurable Intelligent Surfaces (RIS) URLLC Optimization Machine Learning in Telecommunications Industrial IoT and Robotics His recent work explores statistical reliability in wireless systems and the potential of large intelligent surfaces (LIS) for high-resolution sensing in industrial environments. He has published in top conferences like IEEE ICC and journals like IEEE Open Journal of the Communications Society. Lab/Team Affiliation : Part of the Connectivity Section within the Department of Electronic Systems, focusing on advanced wireless systems and their practical implementations.

Andrea Vandinمشاهده پروفایل

دانشیار

• Formal methods
• Qualitative and quantitative system modeling and analysis
• Domain Specific Languages
• +۷ مورد دیگر

Andrea Vandin serves as an Associate Professor in the Formal Methods for Safe and Secure Systems section at DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark. Her research integrates theoretical computer science with practical system analysis, focusing on formal verification methodologies for complex systems. Her primary research interests include: Formal methods for system verification Qualitative and quantitative modeling techniques Domain-specific language development Large-scale performance analysis Model reduction algorithms Chemical reaction network analysis Statistical model checking Recent publications demonstrate expertise in constrained lumping of chemical systems, network embedding through equitable partitions, and stochastic conformance checking. Her work bridges computer science, mathematics, and systems biology with applications in sustainable system design. She actively contributes to UN Sustainable Development Goals through formal verification approaches for safe and secure systems. Her research shows strong trends toward applying model reduction techniques to chemical reaction networks and agent-based systems, with increasing focus on statistical verification methods and probabilistic modeling. The integration of MultiVeStA with Mesa for Python agent-based models represents a significant contribution to verification capabilities in simulation environments.

• Deep Learning
• Federated Learning
• Data Analysis on Graphs
• +۹ مورد دیگر

Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.

Tianbao Guمشاهده پروفایل

پژوهشگر

• Energy Thermochemical Conversion
• Computational Fluid Dynamics
• Kinetic Modeling
• +۴ مورد دیگر

Tianbao Gu is a postdoctoral researcher at Aalborg University's Faculty of Engineering and Science, affiliated with the Thermal Engineering department. His work focuses on energy thermochemical conversion, computational fluid dynamics (CFD), and kinetic modeling of biomass and waste utilization. He contributes to UN Sustainable Development Goals through advancements in green ammonia production and hydrogen storage. External Position: Visiting Postdoc at Eindhoven University of Technology (2023–2024) Collaboration: Chalmers University of Technology (2020–2021) Research Interests Gu's research addresses bioenergy systems, hydrogen storage via ammonia synthesis, and waste-to-energy conversion using advanced modeling techniques. His expertise spans municipal solid waste incineration, biomass pyrolysis, and catalytic reaction optimization. Recent Publications His recent work includes CFD modeling of ammonia synthesis, parametric studies of evaporative coolers, and co-simulation frameworks for power-to-X systems, emphasizing experimental validation and process optimization. Projects HySTrAm (2022–2025): Hydrogen storage and transport using ammonia Advanced modeling of biomass/waste pyrolysis and combustion (2018–2021)

Manfred Jaegerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +۵ مورد دیگر

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

Julie Thøgersenمشاهده پروفایل

استادیار

• Actuarial Science
• Insurance Mathematics
• Risk Management
• +۴ مورد دیگر

Julie Thøgersen is an Assistant Professor at the Department of Economics and Business Economics, Aarhus University. She specializes in actuarial science, particularly insurance mathematics, with a focus on optimal premium selection, market competition, and risk modeling. Primary Affiliation: Department of Economics and Business Economics, Aarhus University Education: PhD in Mathematics, Aarhus University Her research integrates stochastic control , game theory , and Bayesian statistics to analyze insurance market dynamics, product design, and experience rating. Recent work addresses credibility premium rules, capital requirements, and welfare implications of insurance deductibles. Julie has published extensively in journals like Insurance: Mathematics and Economics and ASTIN Bulletin . Key trends in her work include non-life insurance strategies , stochastic modeling , and market equilibrium analysis .

Orimar Sauri Arreguiمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Stochastic Processes
• Nonparametric Estimation
• +۷ مورد دیگر

Orimar Sauri Arregui is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, Denmark. His research lies at the intersection of mathematical statistics, stochastic processes, and financial modeling. Research Interests: His work focuses on ambit fields , trawl processes , Lévy and infinite divisible random fields , and nonparametric estimation in continuous time. He investigates asymptotic behavior, limit theorems, and statistical inference for complex stochastic models, with applications in financial market microstructure and energy flux modeling. The analysis of his recent publications reveals a strong trend in theoretical statistics and probability, particularly in developing and analyzing models driven by non-Gaussian noise and long-range dependence. His work often involves high-frequency data and contributes to the foundations of spatiotemporal modeling. Scientific Contributions: Developed mathematical frameworks for financial market microstructure. Advanced theory for nonparametric estimation of trawl processes. Derived asymptotic error distributions for numerical schemes in stochastic delay equations. Proved local limit theorems for energy fluxes in random fields. Advising and Research Activity: He has been involved in PhD supervision and maintains an active research output, primarily through preprints on arXiv and SSRN. His collaborations span topics in financial econometrics and statistical physics. Though specific grants are not listed, his consistent publication record suggests ongoing research funding. Laboratory and Teams: While no formal lab is mentioned, his work is part of the broader research network in mathematical statistics and financial mathematics at Aalborg University, with notable collaborations in stochastic modeling and econometrics.

Jaron Skovsted Gundersenمشاهده پروفایل

عضو هیئت علمی

• Privacy-Preserving Computation
• Secure Multi-Party Computation
• Federated Learning
• +۱۵ مورد دیگر

Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.

Niels Erik Olesenمشاهده پروفایل

پژوهشگر ارشد

• Electrochemical Sensors
• Wearable Sensors
• Drug Delivery
• +۳ مورد دیگر

Niels Erik Olesen is a Postdoc researcher at the Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), affiliated with the Nano Bio Integrated Systems group. He serves as Contact Person for the DTU project "Active Wearable Sensors for Monitoring of Levodopa in Parkinson’s Disease" (2023-2025) and is a Member of the International Electrotechnical Commission (2022-2026). His ORCID profile (0009-0001-3908-5043) and institutional email nieol@dtu.dk confirm his active DTU affiliation. Research interests center on electrochemical sensors and drug delivery systems. Early work (2012-2018) focused on biopharmaceutics, including thermodynamic modeling of cyclodextrin formulations, bile salt interactions, and drug-polymer solubility prediction using DSC/ITC techniques. Current research pivots to wearable sensor technology, specifically microneedle-based porous gold electrochemical sensors for real-time levodopa monitoring in Parkinson's disease patients, as evidenced by his active project and 2024 conference presentation. His 15 most recent publications (2015-2018) reveal strong methodological innovation in pharmaceutical formulation science. Key themes include DSC-based solubility prediction (7 articles), cyclodextrin-bile salt displacement mechanisms (4 articles), and polymer molecular weight effects on drug miscibility (3 articles). The shift toward electrochemical sensors is documented in his 2023 project but not yet reflected in publications, indicating emerging research direction. No scientific awards or fellowships were mentioned in the provided sources. Olesen leads the DTU research project on Parkinson's disease wearable sensors (100% focus on levodopa monitoring) and presented preliminary work as Guest Lecturer at a conference in November 2024. No student advisement is documented, though his project involves interdisciplinary collaboration. The International Electrotechnical Commission membership (2022-2026) suggests industry-standardization contributions. He operates within DTU's Nano Bio Integrated Systems group, focusing on nanoscale biointegration. His current project team develops microneedle-based electrochemical sensors for continuous levodopa monitoring, aiming to translate lab research into clinical Parkinson's management tools through wearable technology.

• Machine Learning
• Probabilistic Inference and Reasoning
• Stochastic Processes
• +۱ مورد دیگر

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).

Boel Nelsonمشاهده پروفایل

استادیار

• Privacy
• Differential Privacy
• Metadata Privacy
• +۳ مورد دیگر

Boel Nelson is a Tenure Track Assistant Professor at the Programming Languages and Theory of Computation section of the Faculty of Science , University of Copenhagen. Her research focuses on privacy, particularly differential privacy and metadata privacy, with applications in distributed computing, instant messaging, and automotive data systems.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +۳ مورد دیگر

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.