Find Academic Supervisors

Andres MasegosaView profile

Associate Professor

• Machine Learning
• Bayesian Networks
• Data Streaming
• +4 more

Andres Masegosa is an Associate Professor at the Department of Computer Science, Aalborg University (AAU), within The Technical Faculty of IT and Design. He is actively involved in the DarkScience project (2022–present), focusing on metagenomic binning and microbial dark matter analysis. His research interests span Bayesian networks, machine learning, probabilistic graphical models, and educational methodologies in computer science instruction. Key research contributions include advancements in PAC-Bayes theory, genome representation learning, and cold posterior effects in Bayesian models. He has published extensively in top venues like Advances in Neural Information Processing Systems and Transactions on Machine Learning Research. His work often bridges theoretical contributions with practical applications in genomics and education. Masegosa leads the development of tools like InferPy for probabilistic modeling and has contributed to open-source projects such as the AMIDST toolbox. His educational research explores learning styles and active learning strategies, emphasizing live coding and programming exercises. Collaborations include interdisciplinary projects with microbiologists and data scientists, reflecting his expertise in computational methods for complex biological systems. His research portfolio demonstrates a strong focus on scalable probabilistic methods and their real-world applications.

Yoshiharu MaenoView profile

Professor

• Data Science and Engineering
• Complex Systems Theory
• Stochastic Process Theory
• +7 more

Dr. Yoshiharu Maeno is a Professor at the School of Interdisciplinary Mathematical Sciences, Meiji University, where he conducts interdisciplinary research at the intersection of mathematics, data science, and social systems. His work applies advanced mathematical frameworks to real-world complex phenomena in economics, finance, epidemiology, and social networks. Position: Professor Institution: Meiji University School: School of Interdisciplinary Mathematical Sciences Research Interests Dr. Maeno's research focuses on data science and engineering, complex systems theory, and stochastic process theory applied to economics and social sciences. He specializes in modeling reaction-diffusion processes to understand the dynamics of infectious disease spread, financial crises, and misinformation dissemination on social media. His work bridges theoretical mathematics with practical applications in societal challenges. Publication Trends His recent scholarly output demonstrates a consistent focus on applying reaction-diffusion and stochastic models to complex social and economic systems. The publications span interdisciplinary domains including econophysics, computational social science, and mathematical epidemiology, showing a strong trend toward data-driven modeling of real-world dynamic processes. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants While specific students or grant details are not listed, Dr. Maeno's active research program suggests involvement in mentoring graduate students and securing research funding. His participation in events like the Econophysics Colloquium and CSH Workshop indicates collaboration within the global complexity science community. Labs and Teams Dr. Maeno is associated with research initiatives focused on complex systems and data science, likely involving collaborative teams working on interdisciplinary projects. His involvement in workshops and colloquia suggests membership in international research networks studying complexity in economic and social systems.

• Probabilistic Graphical Models
• Machine Learning
• Deep Generative Models
• +7 more

Thomas Dyhre Nielsen is a Professor (MSO) in the Department of Computer Science at Aalborg University . He is a member of the Distributed, Embedded and Intelligent Systems (DEIS) research group and contributes to the Artificial Intelligence and Machine Learning team at the university. His primary research interests lie in the theoretical and applied aspects of probabilistic graphical models , machine learning , and deep generative models . His work spans from foundational methods for learning and inference to the development of frameworks for solving complex decision problems. He is the co-author of the authoritative textbook Bayesian Networks and Decision Graphs . His recent research output reveals a strong trend towards interdisciplinary applications. His 15 most recent publications demonstrate significant work in bioinformatics , using graph neural networks and variational autoencoders for metagenomic binning, and in urban infrastructure , applying reinforcement learning and probabilistic models to optimize stormwater and wastewater management systems. He also has impactful research in healthcare , developing Bayesian network models for clinical risk prediction. Senior area editor for the International Journal of Approximate Reasoning . Principal or Co-Investigator on research projects such as the AMIDST project (developing a Java toolbox for scalable probabilistic machine learning) and PGM 2020 (organizing the International Conference on Probabilistic Graphical Models). Actively supervising research, as evidenced by a current opening for a PostDoc position in probabilistic machine learning. Professor Nielsen leads a research lab focused on probabilistic machine learning, which is part of the larger DEIS and AI/ML teams at Aalborg University. His group develops and applies advanced models to real-world problems in environmental science, healthcare, and intelligent systems.

Søren HaubergView profile

Professor

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +11 more

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection

Søren Wengel MogensenView profile

Associate Professor

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +3 more

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

Jasper Marijn EverinkView profile

Researcher

• Bayesian Inference
• Regularization
• Gaussian Distribution
• +7 more

Jasper Marijn Everink is a Postdoc researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark, specializing in computational uncertainty quantification and inverse problems. His work bridges statistical learning, regularization techniques, and imaging applications. Research Interests: Everink focuses on Bayesian inference, sparse modeling, and spatial regularization methods. His research develops hierarchical frameworks for uncertainty quantification, leverages level-set techniques in electrical impedance tomography, and explores implicit prior modeling for inverse problems. Publication Trends: Recent work emphasizes conformal prediction for imaging (2025), level-set regularization in tomography (2024), and sparsity-promoting priors (2024). Earlier contributions include projected density Bayesian methods (2023) and regularized Gaussian distributions for sparse inference (2023). Collaborations: Collaborates across mathematics, computer science, and engineering disciplines, with applications in imaging and computational physics.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +4 more

Andreas Bjerregaard Jeppesen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning. His work bridges theoretical and applied research across diverse domains including quantum computing, biomedical informatics, environmental monitoring, and AI ethics. The Machine Learning section at DIKU explores foundational algorithms and their applications in Medical data analysis Remote sensing Biological modeling Sustainable AI Information retrieval . Andreas contributes to interdisciplinary projects like the SCIENCE AI Centre, leveraging the department's compute cluster for intensive simulations. His recent publications highlight trends in Quantum-inspired neural architectures Generative models for protein sequences Energy-aware AI development Neurological applications of ML Climate-conscious computing . Collaborations span computational biology, quantum chemistry, and federated learning for precision medicine.

• Programming Languages
• Formal Verification
• Privacy Risk Analysis
• +2 more

Willard Rafnsson is an Associate Professor in Programming Logic and Semantics at IT University of Copenhagen. His research develops formal methods for security and privacy, particularly probabilistic programming approaches for quantifying privacy risks. Research investigates privacy risk assessment methodologies, attacker knowledge modeling, and formal verification techniques. Recent work examines privacy vulnerabilities in genetic data and develops tools for leakage quantification. Rafnsson leads projects on practical static analysis and reliable AI for autonomous systems, focusing on security foundations.

Mark PodolskijView profile

Professor

• Mathematical Statistics
• Probability Theory
• Stochastic Processes
• +3 more

Mark Podolskij is an Honorary Professor at the Department of Mathematics, Aarhus University. Since 2006, he has contributed over 70 research outputs, focusing on mathematical statistics, probability theory, and stochastic processes. His work emphasizes asymptotic theory, statistical inference, and applications in financial markets. He holds editorial roles in journals like Bernoulli and Scandinavian Journal of Statistics . Research Interests: His primary areas include stochastic processes, limit theorems, and high-dimensional statistical methods. Notable contributions involve quadratic variation analysis, heavy-tailed distributions, and Lévy processes. Scientific Awards: He secured prestigious grants, including the ERC Consolidator Grant (2018) and Villum Fonden funding (2015). These grants supported projects on statistical methods for diffusions and probabilistic inference in ambit fields. Grants/Editorial Roles: Podolskij has led or co-investigated research projects totaling over €10M. His editorial work spans multiple journals, reflecting his influence in academic publishing.

Jilin HuView profile

Lecturer

• Representation Learning
• Time Series Forecasting
• Contrastive Learning
• +3 more

Jilin Hu serves as a Lecturer in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Dr. Hu is actively affiliated with the Data Engineering, Science and Systems research group, the AI for the People initiative, and the Daisy – Center for Data-intensive Systemer. Research focuses on representation learning and time series forecasting, with significant contributions in contrastive learning frameworks for trajectory data and crystal property estimation. Recent work demonstrates expertise in graph neural networks for spatiotemporal modeling, few-shot object detection, and foundation models for time series analysis. Key methodologies include zero-shot learning, self-supervised techniques, and diffusion probabilistic models applied to materials science and computer vision challenges. Dr. Hu leads the Villum Foundation project 'Inverse Design of Materials Using Diffusion Probabilistic Models' (2024-2026) as principal investigator and supervises one PhD student. Current research directions include developing diffusion models for glass-ceramic material design and benchmarking foundation models for time series forecasting applications.

Lys Sanz MoretaView profile

Research Fellow

• Molecular Biology
• Machine Learning
• Probabilistic Programming
• +3 more

Lys Sanz Moreta is a Research Fellow at the University of Copenhagen, holding dual appointments as a Postdoc in the Department of Cellular and Molecular Medicine (Faculty of Health and Medical Sciences) and a Guest Researcher in the Department of Computer Science's Programming Languages and Theory of Computation group. Her work bridges molecular biology and advanced machine learning to solve complex biomedical problems. Education PhD in Computer Science, University of Copenhagen, 2022 Dr. Moreta specializes in interdisciplinary research, applying Bayesian modeling and deep learning to molecular biology challenges. Her expertise spans protein structure prediction, viral epitope analysis, and probabilistic programming, enabling cross-disciplinary collaborations between computational and biomedical fields. She emphasizes optimizing programming and mathematical skills to connect diverse knowledge domains. Her 2019-2025 publications reveal a consistent focus on integrating machine learning with structural biology and immunology, particularly through variational autoencoders and probabilistic frameworks for protein superposition, ancestral sequence resurrection, and viral T-cell epitope prediction. This research has significant implications for drug design and molecular evolution studies. Scientific Awards No major scientific awards are documented in the provided profile. While no student advising or grant details are listed, her active roles in the Glycomics Program and Programming Languages research group suggest ongoing collaborative projects. Her interdisciplinary approach facilitates knowledge transfer between computer science and biomedical research teams. She maintains affiliations with the Glycomics Program (Department of Cellular and Molecular Medicine) and the Programming Languages and Theory of Computation group (Department of Computer Science), driving innovation at the computational-biology interface.

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

• Demography
• Population Studies
• Mortality Forecasting
• +4 more

Paola Vázquez-Castillo is a Research Fellow at the Faculty of Social Sciences, University of Southern Denmark, affiliated with CPop. Her work focuses on demographic research with emphasis on mortality forecasting, life expectancy analysis, and statistical methods development for population studies. Her research spans demography, population studies, and statistical methodology. Key interests include mortality forecasting techniques, modal age at death estimation, aging dynamics, and the impact of pandemics/violence on population health. She employs advanced statistical approaches like discretized derivative tests and probabilistic frameworks to model demographic trends and life expectancy changes, particularly in Mexican populations. Recent publications (2024-2025) demonstrate consistent innovation in mortality modeling, with strong focus on methodological rigor and real-world applications to crises like the COVID-19 pandemic and violence in Mexico. Her work bridges demographic theory with practical statistical tools for accurate life expectancy forecasting. She received the Premio Latinoamericano en Población Carmen Miró 2020 (Latin American Award in Population Carmen Miró) in 2021. Dr. Vázquez-Castillo has co-instructed the “Introduction to R” course annually (2022-2024), demonstrating commitment to statistical education. Her research is supported by major grants including the ERC Consolidator Grant “Born Once – Die Once” (2022-2027) and the AXA Chair in Longevity Research (2015-2025), where she contributed to transdisciplinary demographic projects with international collaborators. She actively participates in CPop at the University of Southern Denmark and maintains international research networks, as evidenced by collaborations with institutions in Italy, Japan, and Mexico across multiple projects.

• Inverse Problems
• Geostatistics
• Seismology
• +4 more

Klaus Mosegaard is a Professor at the University of Copenhagen's Niels Bohr Institute within the Physics of Ice, Climate and Earth department. His academic affiliation spans decades of research in computational geophysics and inverse problems methodology. His research focuses on computational aspects of inverse problems and geostatistics, with applications across seismology, planetary geophysics, geothermal investigations, and geomagnetism. Key contributions include development of algorithms for nonlinear inversion, particularly through Markov Chain Monte Carlo methods. His work bridges theoretical advances with practical applications in planetary seismology (including Mars interior studies), deep seismic lithosphere analysis, resource exploration, and thermal studies in Greenland ice shelves. Analysis of his 110+ publications reveals consistent innovation in probabilistic inversion techniques, with recent work integrating machine learning (particularly generative adversarial networks) with traditional geostatistical methods for reservoir characterization and facies modeling. His 2023-2025 publications demonstrate ongoing leadership in addressing dimensionality challenges in large-scale inverse problems. Best Paper Award Computers and Geosciences 2008 The Ole Rømer Grant for outstanding research in geophysics (1994) Teaching Award, Faculty of Science, University of Copenhagen (2000) Honorable Mention for Best Papers in Geophysics journal (2011) Mosegaard has delivered 25+ invited lectures on Bayesian inversion paradigms, Monte Carlo methods, and seismic inversion since 2020, including presentations at major international conferences. His research profile shows sustained external collaborations across planetary science and petroleum geophysics fields, with significant contributions to Mars interior modeling through InSight mission data analysis.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.