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Jesper Rindom JensenView profile

Associate Professor

• Audio Signal Processing
• Artificial Intelligence
• Speech Enhancement
• +12 more

Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.

Renaud LambiotteView profile

Professor

• Network Science
• Complex Systems
• Temporal Networks
• +7 more

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

Per LynggaardView profile

Professor

• Integrated Circuit Design
• Wireless Sensor Networks (WSN)
• Machine Learning
• +8 more

Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.

Morten NielsenView profile

Professor

• Harmonic Analysis
• Approximation Theory
• Sparse Signal Processing
• +7 more

Morten Nielsen is a Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research is centered on harmonic analysis, approximation theory, and sparse signal processing, with strong emphasis on modulation spaces, wavelet systems, and matrix-weighted function spaces. His research interests include: Harmonic and Functional Analysis Approximation Theory and Sparse Representations Wavelet and Time-Frequency Analysis Nonlinear Approximation in Banach Spaces Matrix-Weighted Function Spaces Applications in Signal and Image Processing The recent publications show a consistent focus on theoretical developments in function spaces, particularly α-modulation and Triebel-Lizorkin spaces, with applications to sparse representations and nonlinear approximation. His work often involves deep connections between operator theory, frame theory, and signal decomposition techniques. Although no specific scientific awards are listed, his extensive publication record in top-tier journals and book chapters in prestigious series such as Applied and Numerical Harmonic Analysis indicates significant recognition in the mathematical community. Morten Nielsen has been involved in multiple research projects, including 'Generalized Wavelet Systems', 'Sparse Representation of Data', and 'Muckenhoupt Matrix Weights', often in collaboration with researchers like Hrvoje Šikić. He has supervised at least one PhD student and continues to be actively involved in research, as evidenced by publications in 2025. His work is supported by ongoing research grants and collaborations across Europe. He leads and participates in research groups focused on harmonic analysis and approximation theory, contributing to both theoretical advancements and practical applications in data science and engineering.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

• Information Retrieval
• Data Science
• Text Analytics
• +5 more

Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.

• Deep Learning
• Federated Learning
• Data Analysis on Graphs
• +9 more

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.

• 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.

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

Jevgenijs IvanovsView profile

Associate Professor

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

Jevgenijs Ivanovs is an Associate Professor in the Department of Mathematics at Aarhus University . His research focuses on stochastic processes , particularly Lévy processes , extreme value theory , and probability theory , with applications in queueing systems , statistical inference , and multivariate extremes .

Lorenzo Dall'AmicoView profile

Academic

• Network science
• Temporal networks
• Machine learning
• +13 more

Lorenzo Dall'Amico is a Postdoctoral Researcher at ISI Foundation in Turin, Italy, and an assistant professor at the University of Torino, where he teaches a course on complex networks. He is a key member of the research team led by Professor Ciro Cattuto, focusing on the analysis of complex, temporal, and proximity networks with applications in epidemiology, social sciences, and human behavior modeling. PhD in Signal, Image, Speech, and Telecommunications, 2021 – Université Grenoble Alpes Master in Physics of Complex Systems, 2018 – Politecnico di Torino M2 in Physics of Complex Systems, 2018 – Paris Sud (XI) BSc in Physical Engineering, 2016 – Politecnico di Torino His research lies at the intersection of statistical physics, mathematics, and computer science. He develops interpretable representations of high-dimensional network data, with a particular focus on temporal and proximity networks. His work has significant applications in epidemic modeling, public health, and social dynamics. He is deeply involved in interdisciplinary projects such as COVID-19 Real Time Epidemiology and Periscope , both aimed at improving pandemic response through data-driven modeling. His recent publications span top journals including Nature Communications , Science Advances , and Physical Review E , with themes centered around temporal graph embeddings, community detection in dynamic graphs, and the integration of socioeconomic factors into epidemic models. He has developed the open-source Julia package CoDeBetHe.jl for efficient spectral community detection in static and dynamic graphs, which is widely used in network science research. His scientific contributions include: Development of embedding-based distances for temporal graphs Creation of generalized contact matrices for improved epidemic modeling Efficient algorithms for distributed representations using SoftMax normalization Analysis of household and school-based contact patterns in disease transmission Lorenzo actively mentors through open science practices, releasing code and datasets alongside his publications. He has advised or collaborated with researchers across disciplines, contributing to projects funded by the Botnar Foundation. His work emphasizes data for good , applying advanced network science to real-world public health challenges. He leads and contributes to research teams focused on digital epidemiology and network-based modeling, leveraging high-resolution proximity data from projects like SocioPatterns. His future work aims to further bridge theoretical network science with practical applications in public health policy and intervention design.

Thomas PockView profile

Professor

• Computer Vision
• Image Processing
• Mathematical Optimization
• +7 more

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

Christian Fischer PedersenView profile

Associate Professor

• Signal processing
• Machine learning
• Distributed systems

Christian Fischer Pedersen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University School of Engineering. His research focuses on signal processing, machine learning, and distributed systems, with applications in biomedical engineering and industrial diagnostics. Signal processing for healthcare and mechanical systems Machine learning applied to survival analysis and text mining Distributed systems for medical and industrial applications Recent publications highlight his work in explainable AI for pediatric arthritis detection, probabilistic neural networks for survival analysis, and MEMS sensor applications in mechanical diagnostics. His projects include European collaborations like the PRECISE fall-risk prevention initiative. Contact: cfp@ece.au.dk

Henrik Anders LindénView profile

Researcher

• Computational Neuroscience
• Neural Dynamics
• Spinal Motor Circuits
• +2 more

Henrik Anders Lindén serves as a Guest researcher in the Department of Neuroscience at the University of Copenhagen's Faculty of Health and Medical Sciences, where he is part of the Neuronal Signalling research group. Holding a Ph.D. in Physics (Computational Neuroscience) from the Norwegian University of Life Sciences, Lindén applies computational methods to study neural dynamics in motor and sensory systems, with particular focus on spinal cord networks and auditory processing mechanisms. His educational background includes: Ph.D. in Physics (Computational Neuroscience), Norwegian University of Life Sciences Lindén's research centers on computational neuroscience with emphasis on neural circuit dynamics. He investigates rotational dynamics governing movement in spinal motor networks, firing rate distributions in central pattern generators, and critical periods in auditory processing. His work bridges theoretical modeling and experimental neuroscience to uncover fundamental brain mechanisms across multiple scales, integrating mathematical approaches with biological data to explain emergent properties of neural systems. Analysis of his 2016-2022 publications reveals strong concentration on motor control and sensory neuroscience. Key contributions include demonstrating rotational dynamics in spinal motor networks, elucidating excitation-inhibition balance in motor circuits, and identifying late critical periods in auditory processing. Lindén consistently employs computational modeling to interpret neural data, with increasing focus on how neural dynamics translate to behavioral outputs through interdisciplinary approaches combining physics, neuroscience, and data science. No scientific awards or major grants are documented in available sources. Similarly, student advising activities are not specified in current institutional records. Lindén operates within the Neuronal Signalling group, which investigates cellular and network mechanisms of neuronal communication using electrophysiology, imaging, and computational techniques to understand information processing in neural circuits related to movement and sensation.

Bjarne Kjær ErsbøllView profile

Professor

• Applied Statistics
• Data Analysis
• Image Analysis
• +4 more

Bjarne Kjær Ersbøll is a Professor in Statistics and Data Analysis at the Department of Applied Mathematics and Computer Science, DTU Compute, Technical University of Denmark (DTU). He has held academic positions since 1983, progressing from Research Assistant (1983-1986) to Full Professor (2010-present). His research focuses on applied statistics, data analysis, and their applications in industrial and medical projects, emphasizing collaborative solutions with industry and institutions. He holds M.Sc. (1983) and Ph.D. (1990) degrees from DTU. Key research interests include image analysis, multivariate statistics, and big data methodologies. He has supervised numerous master’s and Ph.D. theses and organized conferences on image analysis and statistics. Notable projects include studies on environmental contaminants in food, cardiovascular mortality linked to drinking water magnesium, and interventions in childhood dietary habits. His work contributes to UN Sustainable Development Goals related to health, sustainable cities, and responsible consumption. Recent publications highlight trends in public health (e.g., dietary interventions), environmental science (e.g., PFAS in eggs), and machine learning applications (e.g., deep learning for quality assessment). He actively participates in academic activities, including lecturing on applied statistics and supervising interdisciplinary projects.