Find Academic Supervisors

Tejs VeggeView profile

Professor

• Autonomous materials discovery
• Energy conversion and storage
• Battery materials
• +4 more

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.

Leon Eyrich JessenView profile

Associate Professor

• Bioinformatics
• Immunology
• Machine Learning
• +6 more

Leon Eyrich Jessen is an Associate Professor and Groupleader at the Department of Health Technology, Technical University of Denmark (DTU). He leads the Computational Autoimmunity research group, focusing on computational immunology with a particular emphasis on understanding disruptions in central tolerance and the mechanisms that allow the immune system to differentiate between self and non-self. His work aims to uncover immuno-cellular processes associated with autoimmune diseases through single-cell technology and machine learning-based bioinformatics tools. Dr. Jessen's research spans several key areas including Bioinformatics, Immunology, Machine Learning/AI, Data Science, and T-cell Biology. His work specifically addresses Computational Autoimmunity, Bayesian Modeling, and Molecular Evolution. He employs single-cell technology to investigate autoimmune disease mechanisms, with applications in psoriatic arthritis, diabetes, and other autoimmune conditions. His group develops computational methods for T cell receptor analysis, epitope prediction, and spatial transcriptomics. His recent publications demonstrate a strong focus on computational immunology, with key contributions in single-cell RNA sequencing, T cell receptor analysis, and spatial transcriptomics. His work bridges computational methods with immunological applications, particularly in autoimmune disease research. The research shows a consistent pattern of developing and applying machine learning algorithms to complex immunological data, with significant contributions to understanding T cell biology and autoimmune mechanisms. Dr. Jessen actively supervises PhD students including Drachmann, C., Montemurro, A., Povlsen, H. R., and Schaap-Johansen, A.-L. on projects related to single-cell analysis, immunoinformatics, and deep learning methods for immunotherapy. He is also the creator and instructor of "R for Bio Data Science," which is the third largest course at his department, demonstrating his commitment to education and training the next generation of bioinformaticians. He leads the Computational Autoimmunity group which focuses on developing, applying, and serving machine learning-based bioinformatics tools. The group works at the intersection of computational methods and immunological research, with particular emphasis on single-cell technologies and their application to autoimmune disease mechanisms.

Steen LysgaardView profile

Researcher

• Computational Materials Science
• Genetic Algorithms
• Battery Technology
• +5 more

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

Manfred JaegerView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +5 more

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.

Svante EriksenView profile

Associate Professor

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

Svante Eriksen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His research spans statistics, forensic genetics, and computational modeling, with a focus on Bayesian networks, graphical models, and statistical methods for forensic DNA analysis. He is actively involved in interdisciplinary research and software development for probabilistic genotyping and large-scale inference. Research Interests: Bayesian Networks and Graphical Models Statistical Methods in Forensic Genetics SNP and Y-STR Genotyping Data Mining and Knowledge Discovery Model Selection and Context-Specific Independence Software Development for Statistical Inference Recent Publication Trends (2024–2025): His recent work focuses on improving SNP genotyping accuracy using logistic regression models, developing efficient software (jti and sparta) for Bayesian network inference, and advancing forensic DNA analysis through dynamic SNP selection and probabilistic modeling of Y-STR databases. These contributions reflect a strong integration of statistical theory, computational efficiency, and real-world forensic applications. Scientific Contributions: Principal contributor to software packages for Bayesian network prediction. Developer of statistical models for forensic DNA data interpretation. Collaborator on projects involving digital learning analytics and student retention. Advising and Grants: While specific student names are not listed, the profile indicates involvement in PhD supervision (4 cases). He has participated in multiple externally funded research projects, including those supported by Novo Nordisk and Danish research councils, focusing on forensic DNA analysis, graphical models, and educational data mining. Research Groups and Collaborations: He is part of a strong research network in forensic genetics and statistical modeling at Aalborg University, collaborating with leading researchers such as N. Morling, M. M. Andersen, and T. Tvedebrink. His work is closely tied to the development and application of statistical software in both forensic and educational domains.

Jaron Skovsted GundersenView profile

Academic

• Privacy-Preserving Computation
• Secure Multi-Party Computation
• Federated Learning
• +15 more

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.

• Quantum Physics
• Semiconductor Devices
• Quantum Computing
• +4 more

Anasua Chatterjee is a researcher at the Center for Quantum Devices, part of the Niels Bohr Institute at the University of Copenhagen. Her work focuses on quantum dot arrays, spin qubits, and semiconductor-based quantum computing platforms. She collaborates with leading quantum research groups and contributes to advancements in quantum device calibration, optimization, and noise mitigation. Affiliation: Center for Quantum Devices, Niels Bohr Institute, University of Copenhagen Her research spans quantum device automation, charge sensing, and real-time control of qubit fluctuations. Recent publications highlight her expertise in radio-frequency reflectometry, gate voltage optimization, and topological superconductivity in hybrid devices. Key article trends include autonomous calibration of quantum dots using evolutionary algorithms, spin qubit control via FPGA-based feedback systems, and integration of superconducting elements with semiconductor platforms. These studies often involve collaborations with institutions in the U.S. and Europe. While no formal awards are listed in the provided texts, her work appears integral to scaling quantum processors and improving qubit coherence for fault-tolerant systems.

• Machine Learning
• Probabilistic Inference and Reasoning
• Stochastic Processes
• +1 more

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).

• Transportation Systems
• Behavioral Modeling
• Machine Learning
• +5 more

Rico Krueger is an Associate Professor at the Department of Technology, Management and Economics within the Transport Division at the Technical University of Denmark (DTU). His research focuses on developing models and technologies at the intersection of behavioral modeling, machine learning, and simulation to improve human-centric and sustainable transport systems. Key areas include travel demand forecasting, multimodal systems, and emerging technologies like virtual reality for understanding human decision-making. Education: Ph.D. in Civil and Environmental Engineering from UNSW Sydney (Australia), followed by postdoctoral research at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a prestigious European Research Council Starting Grant (2024–2029) for his project IMMERSION , exploring human decision-making through choice and process data integration. Research emphasizes interdisciplinary approaches, combining data science with behavioral theories to address challenges in urban mobility, sustainability, and public health. His work bridges theoretical advancements with practical applications, such as optimizing policy interventions during pandemics and enhancing ride-sourcing systems. Awards: ERC Starting Grant for IMMERSION project. Grants: Focus on behavioral modeling and pandemic-related transport policies. Labs/Teams: Active in DTU’s Intelligent Transport Systems Section, leading interdisciplinary projects on mobility and health.

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.

Ricardo Jose Gabrielli Barreto CampelloView profile

Professor

• Data Science
• Data Mining
• Machine Learning
• +13 more

Ricardo Jose Gabrielli Barreto Campello is a full Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), Faculty of Science and Engineering. He holds a prestigious International Recruit Fellowship from the Novo Nordisk Foundation (2023–2028) and leads advanced research in data mining and machine learning. He has held prior full professorships at the University of Newcastle and James Cook University, and adjunct or visiting roles at the University of Alberta, University of Melbourne, and University of São Paulo. Education: PhD in Electrical and Computer Engineering, University of Campinas (2002) Master in Electrical and Computer Engineering, University of Campinas (1997) Bachelor in Electronics Engineering, State University of São Paulo (Unesp) (1994) His research centers on data mining and machine/statistical learning , with a focus on developing general-purpose algorithms for descriptive and predictive analytics. He emphasizes unsupervised and semi-supervised learning , particularly in clustering, outlier detection, and intrinsic dimensionality estimation . His work bridges conceptual innovation with real-world applications, including gene-expression data analysis under his current NNF-funded project. The recent publications reflect a strong trend in unsupervised and probabilistic methods , particularly in outlier detection, clustering evaluation, and local intrinsic dimensionality. These works appear in high-impact journals and conferences such as Information Systems , Applied Energy , and SIAM SDM, often in collaboration with leading researchers like Arthur Zimek. The research demonstrates a deep integration of statistical rigor and algorithmic innovation. Scientific Awards: Best Research Paper Award, SIAM SDM (2024) 10-year Test of Time Award, PAKDD (2023) International Recruit Fellow, Novo Nordisk Foundation (2023) Runner-Up Best Paper, IEEE Transactions on Big Data (2022) Top 2% Scientist in the World (2020) Ricardo has consistently secured external research funding, including major grants from the Novo Nordisk Foundation and Danish Council for Independent Research. He has successfully advised numerous postgraduate students and led interdisciplinary research teams. His current projects include Advanced Computational Methods for Unsupervised Data Mining and collaborations on Clustering Evaluation Revisited and Reliable Outlier Detection , all based at SDU. He is actively involved in research networks and collaborations across Australia, Canada, and Europe, and maintains affiliations with institutions such as the University of Melbourne and James Cook University. His work continues to shape methodological advances in data science, particularly in foundational areas with broad application potential.

Nicki Skafte DetlefsenView profile

Associate Professor

• Deep Learning
• Representation Learning
• Machine Learning
• +5 more

Nicki Skafte Detlefsen serves as an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing within the Cognitive Systems research section. His institutional affiliation includes the Richard Petersens Plads campus in Kgs. Lyngby, Denmark, with primary research activities centered on advanced machine learning methodologies. Dr. Detlefsen's research program focuses on deep learning architectures for representation learning, particularly emphasizing invariant representations through inductive bias incorporation. His work spans protein sequence analysis using generative models, temporal alignment networks for time-series data, and stochastic representation frameworks via Laplacian autoencoders. Key contributions include developing methods for predictive uncertainty estimation and addressing suboptimal performance in biological sequence modeling, with strong connections to computational biology and neural network theory. Analysis of his 2019-2025 publications reveals an evolving research trajectory from foundational representation learning techniques toward broader AI ecosystem development. Early work centered on protein sequence modeling and temporal alignment algorithms, culminating in high-impact publications like the Nature Communications paper on protein representations. His most recent 2025 publication demonstrates strategic expansion into European HPC/AI infrastructure development, indicating a shift toward large-scale collaborative AI initiatives while maintaining core expertise in representation learning. No formal scientific awards or fellowships are documented in the source material. Dr. Detlefsen completed his PhD at DTU in 2020 under Søren Hauberg's supervision through the "Deep Metric Learning" project (2017-2021), which received significant academic attention with 328 Orbit downloads and coverage by 6 news outlets. His current research appears institutionally funded through DTU's Cognitive Systems section, with emerging involvement in pan-European AI infrastructure projects as evidenced by the 2025 HPC/AI ecosystem publication. He operates within DTU's Cognitive Systems research environment, collaborating extensively with Søren Hauberg, Ole Winther, and international researchers. His work shows strong interdisciplinary connections between computer science, bioinformatics, and neural engineering, with recent expansion into AI policy and infrastructure development through European collaborative networks.

• Auditory Working Memory
• Neural Mechanisms of Imagination
• Neuroscience of Music
• +5 more

David Ricardo Quiroga Martinez is a Postdoctoral Researcher in the Department of Psychology at the University of Copenhagen's Faculty of Social Sciences. His research focuses on the neural mechanisms underlying auditory working memory and musical imagination using intracranial EEG and MEG techniques. He investigates how the brain represents and manipulates sounds during perception and imagination, with applications for understanding psychiatric disorders and brain-computer interfaces. His research interests span Auditory Working Memory , Neural Mechanisms of Imagination , Neuroscience of Music , and Reward Coding . He employs invasive and non-invasive neuroimaging to study how musical sequences are processed in the brain, examining temporal hierarchies in predictive processing from pure tones to complex songs. His work bridges cognitive neuroscience, computational modeling, and clinical applications. Key publications reveal trends in neural representation of musical thoughts (2024), asymmetric reward prediction coding (2023), and temporal hierarchies in melody processing (2023). His research consistently explores how musical structure, predictability, and expertise shape neural responses, with implications for understanding disorders involving auditory imagery abnormalities. Reintegration Fellowship from the Carlsberg Foundation: The representation of musical thoughts in neurons of the human brain International Postdoc from DFF: The neural basis of musical imagination His collaborative network spans international institutions, with significant work on congenital amusia, atonal music processing, and neural gain modulation. Current projects involve decoding imagined musical sounds and investigating reward prediction errors in cortical networks, supported by major Danish research foundations.

Christophe BiscioView profile

Associate Professor

• Point Processes
• Statistics
• Mathematics
• +3 more

Christophe Biscio is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research focuses on spatial statistics, point processes, and topological data analysis, with applications in materials science and engineering. He leads interdisciplinary projects like 'Deciphering Nanoporosity of Amorphous Materials using Topological Data Analysis,' exploring medium-range order structures in glasses and metal-organic frameworks. Key research interests include statistical learning for point processes, functional central limit theorems, and adversarial machine learning. His work bridges theoretical mathematics with practical challenges in wireless communication, radar sensing, and urban system modeling. Collaborations span materials scientists, computer scientists, and data analysts. Biscio has advised one PhD student and contributed to datasets on topological summaries for spatial processes. He actively organizes conferences on data science and participates in public outreach events like Earth Day climate change discussions.

• 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.