Dimitrios Papageorgiou is an Associate Professor in the Department of Electrical and Photonics Engineering at DTU, specializing in nonlinear and fault-tolerant control systems. His research develops advanced control methodologies for applications including marine vessel autonomy, wind turbine reliability, and industrial automation. Research highlights: Nonlinear sliding-mode control algorithms for robust system performance Fault diagnosis and prognosis for critical energy infrastructure Maneuvering control and situation awareness for autonomous vessels Biomimetic control approaches for robotic systems He leads PhD projects in aerial robotics inspection, wind turbine fault tolerance, and compliant human-robot interaction systems. His group collaborates with maritime and renewable energy industries to implement control solutions in operational environments.
Yasser Rezaeiyan is a Senior Researcher at the Department of Electrical and Computer Engineering (Electronics and Photonics group) at Aarhus University. His research focuses on neuromorphic computing, spintronics-based systems, and biomedical circuit design. He specializes in developing energy-efficient and high-performance analog/mixed-signal integrated circuits for applications in healthcare, environmental monitoring, and smart infrastructure. Key technical contributions include neuromorphic hardware leveraging spintronics and vortex oscillators, low-power sensor systems for leakage detection in district heating networks, and ultrasonically powered biomedical implants. His work bridges device physics, circuit design, and system-level integration to address challenges in next-generation computing and IoT. Publications highlight interdisciplinary innovations such as reservoir computing for seizure detection, STT-RAM-based in-memory computing architectures, and high-bandwidth operational amplifiers. His research has been published in top-tier journals like IEEE Transactions on Magnetics and IEEE Transactions on Biomedical Circuits and Systems. Rezaeiyan holds a strong record in analog circuit design, with expertise in chopper stabilizers, bio-impedance measurement systems, and energy harvesting solutions. His lab focuses on translating fundamental physics into practical systems with real-world impact.
Hoda Fares is an Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University, specializing in neuroprosthetics and human-machine interfacing. Her research focuses on developing brain-inspired neural interfaces (BI-BCIs) that integrate neuromorphic hardware and flexible materials to restore sensorimotor function in individuals with disabilities such as stroke or amputation. She works on closed-loop systems combining bioinspired spiking neural networks (SNN) with low-power neuromorphic hardware. Her expertise includes intelligent neuroprosthetics, tactile sensing technologies, and electrotactile stimulation for bionic limbs. Current projects involve wearable neuromorphic interfaces for bidirectional control of prostheses and enhancing rehabilitation systems through artificial sensory-motor restoration. Fares' work bridges artificial intelligence with neuroengineering to create adaptive, miniaturized neural implants. Selected publications highlight advancements in functional electrical stimulation for stroke recovery, electronic skin validation, and multi-channel tactile feedback systems. Her research emphasizes clinical translation of technologies for real-world prosthetic applications, leveraging flexible/stretchable materials and distributed sensing networks. Despite no listed academic awards, her contributions to tactile sensing and prosthetic control systems demonstrate significant innovation in biomedical engineering. Ongoing work includes collaborations on neuromorphic hardware design and validation of virtual prosthetic systems with tactile feedback interfaces.
Sonal Shreya is a Tenure Track Assistant Professor at the Department of Electrical and Computer Engineering, Aarhus University, within the Faculty of Engineering. Her research focuses on spintronics, neuromorphic engineering, and energy-efficient computing architectures. She explores applications in magnetic memory technologies, spintronic devices, and reservoir computing systems. Her work emphasizes developing novel circuits for in-memory computing, leveraging spintronic components like magnetic tunnel junctions (MTJs), STT-RAM, and spin-torque nano-oscillators. Key areas include energy-efficient data encryption, low-power pattern recognition, and multi-state memristor-based neuromorphic systems. She also investigates design thinking applications in engineering innovation, such as Tesla technology advancements. Recent studies highlight contributions to skyrmion dynamics for racetrack memory, vortex-based security solutions (PUF/TRNG), and thermal-induced memristor behaviors. Her publications span from 2014 to 2025, demonstrating a progression toward advanced spintronic applications in computing and sensing. Dr. Shreya collaborates on compact device modeling for circuit simulations and explores granular materials for reservoir computing. Her research bridges fundamental spintronics physics with practical applications in low-power electronics and hardware security.
Matthias Bo Stuart is Associate Professor at DTU's Department of Applied Mathematics and Computer Science, specializing in advanced ultrasound systems. His research develops novel imaging techniques including 3D super-resolution ultrasound, vector flow estimation, and row-column addressed arrays. Current projects focus on improving resolution and speed in medical ultrasonography. Research integrates computational fluid dynamics with experimental validation, enhancing diagnostic capabilities for cardiovascular conditions and microvascular imaging.
Charalampos Orfanidis is an Assistant Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Embedded Systems Engineering. His research focuses on Internet of Things (IoT), low-power wireless networks, wearable systems, and network performance optimization, with strong alignment to sustainable technological development. His research interests center on enabling efficient, intelligent, and secure embedded systems for real-world applications. Key areas include Low Power Wide Area Networks , Wearable Systems , 5G network performance , and reinforcement learning for industrial IoT . His work emphasizes energy efficiency, user experience, and robustness in mobile and distributed systems. The recent publications highlight a strong trend in applying machine learning and AI techniques to solve practical communication challenges in IoT and 5G environments. Topics span from detecting mobile jammers in LoRa networks to optimizing scheduling in industrial IoT using hierarchical reinforcement learning, indicating a focus on intelligent, adaptive network solutions. Scientific Contributions: Principal Investigator of the research project: Low-Cost Acoustic Networks for Underwater Communication Active contributor to international collaborative studies on 5G performance and IoT security He collaborates extensively with researchers across Europe and contributes to high-impact peer-reviewed venues. While no formal advising list is provided, his role as a PI and co-author on multiple student-involved publications suggests mentorship activity. His work bridges theoretical innovation and practical deployment in next-generation communication systems.
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.
Fatemeh Hadaeghi is a NeuroAI Researcher at the Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf (UKE), Germany, and an active member of bAIome (Center for Biomedical AI) and the Beatlab (Centre for Mental Health and Brain Sciences) at Swinburne University of Technology. She specializes in bio-inspired learning, computational modeling of neural networks, and machine learning for biomedical applications in psychiatry and neurology. Research Focus: Biological and artificial recurrent neural networks, wearable data processing, and neuromorphic implementations. Projects: Developing AI for depression relapse prediction, exploring reservoir computing, and dissecting neural network components via lesioning methods.
Yubo Song is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science, specializing in power electronics, microgrid systems, and reliability engineering. His work focuses on stability validation, control strategies, and the application of neuromorphic computing to power grids. His educational background includes: Ph.D. in Energy Technology from Aalborg University (awarded 2023) M.Sc. in Electrical Engineering from Shanghai Jiao Tong University (awarded 2019) B.Eng. in Electrical Engineering and Automation from Shanghai Jiao Tong University (awarded 2016) Dr. Song's research integrates microgrid engineering, power electronics reliability, and neuromorphic computing to address critical challenges in grid stability. His work pioneers Spike Talk technology for energy-efficient grid communication and develops hybrid modeling approaches for real-time power system simulation. Recent publications demonstrate expertise in damping control of grid-forming inverters, sensitivity analysis for reliability evaluation, and Bayesian risk assessment frameworks. His publication trends reveal a strategic shift toward neuromorphic solutions for power grids, with 2024-2025 works focusing on spike-based communication protocols and event-driven semantic systems. Earlier research centered on stability validation and reliability modeling for microgrids, establishing foundational frameworks still cited in current literature. Dr. Song has secured significant research funding including: Power Sentinel (Villum Foundation, 2024-2025): Developing cyber-physical safeguards for future power grids Reliability and Lifetime Analysis (2022-2025): Creating predictive models for power electronic components Stability and Reliability Validation (PhD project, 2020-2023): Establishing comprehensive microgrid assessment methodologies As Principal Investigator on multiple projects, he mentors early-career researchers while collaborating with industry partners on grid security solutions. His work with the Applied Power Electronic Systems group at AAU Energy focuses on translating theoretical advances into practical grid applications, particularly in converter reliability and neuromorphic grid communication.
Francesco Da Ros is an Associate Professor at the Technical University of Denmark , Department of Electrical and Photonics Engineering. His work focuses on machine learning applications in photonic systems, particularly in optical communication and photonic computing. Active in silicon photonics and nonlinear optics Key contributor to optical machine learning and digital signal processing Research Interests: Da Ros explores the intersection of machine learning and photonics , with specific projects involving: Photonic reservoir computing End-to-end optimization of optical communication systems Neuromorphic photonic circuits Channel equalization techniques Quantum communication systems Thermal crosstalk compensation in integrated photonics Scientific Contributions: His research has produced over 245 publications and 23 projects , including significant work on: Raman amplifier optimization Machine learning for optical matrix multipliers Frequency comb phase noise characterization Security systems using distributed acoustic sensing Awards: Recipient of prestigious awards such as: Best Young Italian Researcher in Denmark (2019) DOPS Prize (2017) Horizon Prize for Breaking Optical Transmission Barriers (2016) Academic Leadership: Actively supervises PhD students in projects related to: End-to-end learning for multi-core fiber systems Nonlinear fiber-optic channel optimization Quantum communication lasers Integrated quantum photonic reservoir computing
Ivano Eligio Castelli is a Professor and Head of the Autonomous Materials Discovery group at the Department of Energy Conversion and Storage, Technical University of Denmark (DTU). His research focuses on advanced materials for energy storage, catalysis, and next-generation computing. He leads projects in photovoltaic materials, solid oxide cells, and self-healing materials. Supervising multiple PhD students, his work aligns with UN Sustainable Development Goals related to affordable and clean energy. Recent research includes studies on adsorption isotherms for catalytic materials, oxygen evolution in perovskites, and memristor applications in neuromorphic computing. His contributions span peer-reviewed journals like Advanced Energy Materials and Nature Communications . Projects: Active supervision of 11 PhD projects, including AL-assisted nanoparticle synthesis, Kagome materials for catalysis, and autonomous workflows for energy applications. Labs/Teams: Leads the Autonomous Materials Discovery group, emphasizing high-throughput synthesis and computational materials science.
Hooman Farkhani is an Associate Professor at the Department of Electrical and Computer Engineering at Aarhus University. His primary research focuses on spintronics, neuromorphic computing, and energy-efficient circuit design. He leads projects including the PHOTON-NeuroCom: Photonic-assisted Neuromorphic Computing system (2017-2019) and Hybrid Enhanced Regenerative Medicine Systems (2019-2023). His work spans magnetic tunnel junctions, spin-torque nano-oscillators, and resistive switching memories. Notable contributions include neuromorphic computing architectures, low-power analog/digital converters, and laser-assisted spintronic systems. Recent publications (2023-2024) highlight advancements in neuromorphic engineering, thermal effects on memristors, and energy-efficient spintronic circuits. His research bridges hardware design and neuroscience, emphasizing practical implementations in nanoscale electronics. Key projects involve hybrid spin-CMOS systems and photonic integration. He collaborates on multi-state memristor development and RF signal classification front-ends.
Petr Taborsky is a postdoctoral researcher affiliated with the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His work focuses on artificial intelligence, machine learning, and computational methods within the Cognitive Systems group. Research interests include Bayesian inference , deep learning generalization , and graph-based learning , with applications in high-performance computing and federated learning . Key contributions involve developing the Bayesian Cut method for clustering and analyzing gradient noise in AI models. Recent publications highlight trends in European HPC/AI collaboration (2025) Bayesian graph cut techniques (2022) Statistical modeling in machine learning (2021)
Farshad Moradi is a Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark, specializing in Microelectronics. His research spans spintronics, neuromorphic computing, and analog-to-digital conversion technologies. Current Role: Professor at SDU Microelectronics Research Focus: Spin-Orbit Torque, Magnetic Tunnel Junctions, CMOS Integration Collaborations: Active in international research networks with recent partnerships in Europe His work emphasizes the intersection of electronics, materials science, and computational innovation, particularly in spin-based hardware for AI applications. Recent publications highlight trends in hybrid spin-CMOS circuits and vortex dynamics for wearable sensors, demonstrating interdisciplinary impact.
Jan-Matthias Braun is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. His research bridges artificial intelligence, robotics, and medical device engineering. Current projects focus on explainable AI integration in colon capsule endoscopy Development of real-time FPGA-based systems for colorectal diagnostics Biomechanical modeling for adaptive orthotic devices His work emphasizes cross-disciplinary applications of machine learning in healthcare, particularly for gastrointestinal disease detection and assistive robotics. Publications demonstrate expertise in deep neural networks, hardware acceleration, and smart environment control systems. Teaching responsibilities include: Advanced cybersecurity courses Deep learning applications in epilepsy detection Mentorship in capsule endoscopy image analysis
