Farshad Moradi is a Professor at the Department of Electrical and Computer Engineering at Aarhus University, specializing in neuromorphic engineering, spintronics, and biomedical device design. His work focuses on integrating advanced materials and circuits for applications in neural interfaces, energy-efficient computing, and wireless biomedical systems. Research Interests include: Spintronic-based neuromorphic computing architectures Ultra-low power analog/mixed-signal integrated circuits Ultrasonically powered implantable medical devices Neural signal processing and seizure detection systems Wireless energy transfer and structural health monitoring Key Projects (2016-2026): SPICE: Spintronic-Photonic Integrated Circuit Platform PHOTON-NeuroCom: Photonic-assisted Neuromorphic Computing Neuro-Sense: Flexible bioinspired neuroprostheses CorroSense: Self-powered corrosion monitoring HERMES: Hybrid Enhanced Regenerative Medicine Systems Recent innovations include: Ultrasonically powered optogenetic implants Low-power neural amplifiers for deep-brain interfaces Spin-torque nano-oscillator-based neuromorphic hardware Energy harvesting systems for structural monitoring
Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.
Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.
Silvia Tolu is an Associate Professor at the Technical University of Denmark's Department of Electrical and Photonics Engineering, specializing in Neurorobotics. She leads the NeuroRobotics Technology Lab (NRT-LAB), focusing on bio-mimetic control architectures for compliant robotic systems. Her research integrates neuroscience, computer science, and biology to develop solutions for assistive robotics and neurodegenerative disease diagnosis. Her research interests span: Neuro-robotics and neuromorphic engineering Bio-inspired control systems and adaptive motor control Machine learning for robotic applications Human-robot compliant interaction Cerebellar control models Publications primarily focus on neurorobotics, bio-inspired control, and human-robot interaction, with recent advances in learning-based control systems for soft robots and aerial manipulation. Awards include the AEG Elektrofonden Research Grant and funding for human-robot interaction safety research. Current projects include LOCOPD (Lundbeck Foundation), AEROTRAIN (EU Marie Curie ITN), and compliant human-robot interaction systems. She supervises multiple PhD students in neurorobotics and maintains international collaborations across Europe and Asia. Laboratory resources include advanced robotic platforms for musculoskeletal and soft robot control.
Lasse Bjørn Kristensen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning with a focus on quantum computing applications. Research Interests His work bridges quantum computing, machine learning, and computational biology, with contributions to: Quantum neural networks and spiking neurons Quantum error correction and circuit robustness Quantum chemistry simulations Information flow in parametrized quantum systems Notable Research Trends Kristensen's publications reveal a strong emphasis on quantum-classical hybrid models, entanglement-enhanced devices, and computational methods for chemistry and physics. His recent work explores error-driven learning paradigms and quantum eigensolvers. Contact Email: lakr@di.ku.dk Address: Universitetsparken 1, 2100 Copenhagen Ø
William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.
Jordan Andrew Snyder serves as a Visiting Lecturer in the Department of Science and Environment at Roskilde University, Denmark, where he is affiliated with the Centre for Mathematical Modeling - Human Health and Disease. His academic work integrates advanced mathematical techniques with biomedical research, particularly in hematology and complex network dynamics. Dr. Snyder's research spans two primary domains: Biomedical Modeling : Focused on myeloproliferative neoplasms (MPNs), including stem cell dynamics during ruxolitinib treatment, CALR mutation progression from clonal hematopoiesis to myelofibrosis, and prognostic markers like neutrophil-to-lymphocyte ratios Network Science : Investigating oscillator synchronization, coarse-grained modeling of complex systems, cascading failures in modular networks, and emergent structures in mutualistic ecosystems His publication analysis reveals a distinctive interdisciplinary trajectory where mathematical frameworks bridge hematologic malignancies and fundamental network principles. Recent work demonstrates how amplitude dynamics stabilize oscillator clusters (2025) and how CALR mutations manifest in population screening (2024), showcasing dual expertise in theoretical dynamics and clinical translation. Dr. Snyder operates within Roskilde University's Centre for Mathematical Modeling - Human Health and Disease, an interdisciplinary hub where mathematical approaches address complex biomedical challenges through close collaboration with clinical researchers and international institutions including Memorial Sloan Kettering Cancer Center.
Timothy Robert Merritt is an Associate Professor at Aalborg University's Technical Faculty of IT and Design , Department of Computer Science. His research bridges Artificial Intelligence , Human-Robot Interaction , and Human-Centered Computing with a focus on enhancing wellbeing and designing playful, sustainable technologies. His work spans autonomous systems, shape-changing interfaces, and applications of AI in creative practices. PhD in Integrative Sciences and Engineering (2012) from National University of Singapore Research interests include: Generative AI in design and critical reasoning Autonomous Systems (drones, robots) for search and rescue Human-AI Collaboration in multi-robot environments Interactive Systems for education and health Sustainable Technology through digital fabrication and art-science fusion Recent publications address Generative AI in design research, auditory interventions for driver safety, and trust in multi-drone interfaces. He leads and collaborates on projects like NAMUR (Natural-language Assisted Multi-robot Interaction) and HERD (Human-AI Collaboration with Drone Swarms). He organized the Aalborg Robotics Challenge Workshop (2024) and has been featured in media for public demonstrations with celebrities like Will Smith and Rick Astley. His work involves grants such as the ERASMUS+ funding for robotics and sustainability initiatives.
Xenofon Fafoutis is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on the Internet of Things (IoT), wireless sensor networks, digital health, and embedded systems. He leads several projects including the DIREC Digital Community Building initiative and the eAI: Embedded AI initiative. His work emphasizes energy-efficient communication protocols, security in IoT, and the application of machine learning in edge computing. Key research interests include: IoT and Industrial IoT Wireless embedded systems and protocols Machine learning for edge devices Cybersecurity for sensor networks Wearable health monitoring systems Recent publications highlight his contributions to reinforcement learning-based scheduling, energy-efficient IoT protocols, and TinyML applications. He supervises PhD students in areas like embedded AI toolchains and predictive maintenance systems. His work aligns with UN SDG goals related to sustainable infrastructure and innovation.
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.
Ole Richter is an Assistant Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on advanced device integration and neuromorphic processors, particularly leveraging beyond-CMOS technologies for embedded systems engineering. His recent work involves developing electronic circuits with silicon-based components and exploring neuromorphic architecture for adaptive memory materials. A 2025 publication in Nature Communications highlights his contributions to on-chip learning systems. Richter’s research has attracted attention from 3 news outlets and is accessible via his ORCID profile (0000-0001-5399-8992). Collaborations and publications reflect his expertise in neuromorphic electronics and device integration.
Emil Jørgensen Njor is a Postdoctoral Researcher at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark , specializing in Embedded Systems Engineering . His research focuses on Machine Learning and TinyML for resource-constrained environments. SDG Contributions: Poverty eradication, planetary health, and prosperity through IoT infrastructure optimization Key Research Areas: Neural Architecture Search, Predictive Maintenance, Wearable Systems He actively contributes to academic publications in Journals , Conference Proceedings , and Technical Reports , with recent works addressing energy-efficient AI implementations in wearable medical devices and industrial IoT systems.
Milad Zamani is a Tenure Track Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University. His research focuses on integrated circuits, biomedical engineering, and wireless power systems with applications in neural engineering and sensor systems. He leads projects such as DONUT (European Doctoral Network for Neural Prostheses and Brain Research) and CorroSense (Self-powered corrosion monitoring). His work spans topics like bio-impedance sensing for wearables, ultrasonic energy transfer for implants, and high-precision analog circuits. Recent projects include thermally-powered leakage detection systems and optogenetic implantable devices. Zamani collaborates on advanced sensor technologies, neuromorphic hardware, and biomedical device miniaturization. His lab develops low-power circuits for medical applications, combining signal processing with cutting-edge microelectronic design.
Yuan Li is a Postdoctoral Researcher at the Applied Power Electronic Systems group within the Faculty of Engineering and Science at Aalborg University, Denmark, working under the supervision of Professor Frede Blaabjerg. Her research focuses on advanced control methodologies for power electronic systems and microgrids. Her primary research interests include: Predictive Control Power Electronics Microgrid Battery Energy Storage Model Predictive Control DC/DC Converters Transients Dr. Li specializes in model predictive control (MPC) applications for DC microgrids and power converters, with recent work emphasizing transient response optimization, prediction horizon design, and integration of neuromorphic computing for grid resilience. Her publications reveal a strong trend toward machine learning-enhanced control systems and hardware-efficient implementations for renewable energy integration. She serves as Principal Investigator for the project "Dynamic Stabilization of DC Microgrids Based on Model Predictive Control of Point-of-Load Converters" (2021-2024), securing significant research funding to develop MPC algorithms for DC microgrid stabilization. Her collaborative network includes prominent researchers like Frede Blaabjerg, Sahoo, and Dragicevic across multiple high-impact publications.
Subham Sahoo is an Associate Professor at Aalborg University's Faculty of Engineering and Science, Department of Applied Power Electronic Systems. His research focuses on power electronic control, reliability, and system optimization with particular emphasis on reliability of power electronic converters. He holds a PhD in Electrical Engineering from Indian Institute of Technology Delhi with dissertation on Coordinated Control of DC Microgrids. Dr. Sahoo's research interests span power electronics, microgrid systems, cyber-physical systems, reliability engineering, and AI applications for power systems. His work integrates advanced control strategies with cybersecurity considerations for modern power systems. He has developed innovative approaches for stability assessment, cyber-resilient control, and condition monitoring of power electronic systems. His recent publications demonstrate a strong focus on addressing critical challenges in modern power systems including cyber threats to microgrids, stability of grid-forming inverters, and data-light methods for system assessment. The research shows a clear trajectory toward more reliable, flexible, and secure power electronic systems with increasing integration of AI techniques. Scientific Awards: Innovative Students Projects Award 2019 - Doctoral Level Dr. Sahoo serves as Principal Investigator and Co-Investigator on multiple significant research projects including PRISM (Probabilistic bio-plausible machine learning) funded by The Lundbeck Foundation and SAFEr Grid (Store-And-Forward Energy Grid) funded by HORIZON-ERC-SYG. He has supervised PhD students and collaborated with institutions including Massachusetts Institute of Technology where he was a guest researcher from October 2023 to March 2024. His external engagement includes serving as Chair of the IEEE Joint IES/IAS/PELS Chapter since January 2025. His work contributes to UN Sustainable Development Goals related to clean energy and sustainable infrastructure.
