Lars Nordström is a Professor at the Division of Electric Power and Energy Systems within KTH Royal Institute of Technology, Stockholm, Sweden. His work bridges control systems , communication networks , and power systems , with a focus on future architectures, functionality, and quality aspects of ICT for power grid operations. He has led initiatives such as the Swedish Centre of Electric Power Engineering and served as Thematic Leader for Smartgrids in KIC InnoEnergy. In 2014, he was a Visiting Professor at Washington State University. Education : Ph.D., MSc.EE Nordström's research explores the intersection of smart grids , machine learning , and cybersecurity for power systems. Key areas include: Wide-Area Monitoring and Control (WAMC) systems Decentralized control strategies for DC microgrids Impedance modeling using neural networks Data-driven methods for islanding detection ICT reliability and protocol design for grid operations His recent publications emphasize machine learning applications in power systems, including LSTM networks for EV charging management, graph attention networks for stability monitoring, and digital twin approaches for cyber-attack mitigation. These works span disciplines such as Smart Grids, Power Electronics, and Data Science. Scientific Recognitions : Senior Member, IEEE Senior Member, CIRED Senior Member, Cigre Past Chairman, Swedish IEC TC57 Mirror Committee Nordström actively teaches and examines graduate courses like Communication and Control in Electric Power Systems and Computer Applications and Machine Learning in Electric Power Systems . His work influences industry practices through collaborations on digital substations, energy market analysis, and resilience strategies.
Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Erik G. Larsson is a Professor and Head of the Division for Communication Systems within the Department of Electrical Engineering (ISY) at Linköping University (LiU), Sweden. He joined LiU in September 2007 and has previously held academic and research positions at the Royal Institute of Technology (KTH), University of Florida, George Washington University, and Ericsson Research. Research Interests: Enabling technologies for 6G wireless communication Statistical inference and signal processing Network science and complex networks Decentralized and federated machine learning over networks Physical layer security and privacy Energy-efficient digital signal processing His research group, active in areas like RadioWeaves and massive MIMO, focuses on robust, efficient, and secure wireless connectivity. Recent publications highlight trends in decentralized learning, resource allocation in wireless networks, and the integration of AI into mobile networks, particularly through projects like 'Turning the Air into an AI Computer' funded by the Knut and Alice Wallenberg Foundation. Scientific Awards and Honors: IEEE Signal Processing Magazine Best Column Award (2012, 2014) IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) IEEE ComSoc Best Tutorial Paper Award (2018) IEEE ComSoc Fred W. Ellersick Prize (2019) IEEE SPS Donald G. Fink Overview Paper Award (2023) IEEE Fellow Member, Royal Swedish Academy of Sciences (KVA) Gyllene Moroten Best Teacher Award (2021) Advising and Grants: He has supervised numerous Ph.D. and Licentiate students, many of whom now hold positions at leading industry and academic institutions. His research is currently funded by major organizations including the Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research (SSF), ELLIIT, Security-Link, Swedish Research Council (VR), and EU Horizon 2020 (H2020-SNS-6GTandem). Previous sponsors include VR, KVA, NSF, ORAU, and multiple EU FP7 and H2020 projects (e.g., MAMMOET, REINDEER, 5G-Wireless). Leadership and Service: He has served as Associate Editor for IEEE Transactions on Communications and IEEE Transactions on Signal Processing, chaired technical committees and steering committees in IEEE Signal Processing Society, and held leadership roles in major conferences such as the Asilomar Conference on Signals, Systems and Computers. He was a Visiting Fellow at Princeton University in 2015.
Per Enqvist is an Associate Professor in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology, Stockholm, Sweden. He has held this position since 2009 after progressing from Assistant Professor (2006-2009) and post-doctoral roles at INRIA France and CNR Italy. His academic background includes: Ph.D. in Optimization and Systems Theory from KTH (2001), supervised by Professor Anders Lindquist M.Sc. in Engineering Physics (Civilingenjör) from KTH (1994) with Applied Mathematics focus Post-doctoral studies at INRIA Sophia-Antipolis (2003-2004) and CNR Padova (2001-2003) Enqvist's research centers on mathematical modeling of stochastic processes, scheduling, and queueing theory with applications across operations research, systems engineering, and signal processing. His principal interests span Optimization, Operations Research, Systems Engineering, Signal Processing, Mathematical Systems Theory, and Modeling and Simulation. He has made significant contributions to spectral estimation, covariance interpolation, and resource allocation frameworks. Publication analysis reveals an evolution from foundational systems theory work (2000s) on spectral estimation and minimal realization toward applied optimization in healthcare operations (2010s-2020s). Recent articles address radiation therapy scheduling and contact center modeling using queueing theory with risk-sensitive measures like CVaR, while earlier work established theoretical frameworks for covariance interpolation and passive system synthesis. No scientific awards are documented in the provided information. He has received funding from Vetenskapsrådet (Swedish Research Council) and led the ACCESS seed project on "Robust Spectral Estimation". Enqvist is course responsible for multiple master's program tracks including Aerospace systems and Industrial Engineering, and oversees the Optimization and Systems Theory seminar series. No student advisement details are provided. He maintains affiliations with the ACCESS Linnaeus centre, Center for Industrial and Applied Mathematics (CIAM), and serves on the Swedish Operations Research Society (SOAF) board.
Mikael Gidlund is a Full Professor of Computer Engineering at Mid Sweden University in Sundsvall and holds an adjunct professorship at Beijing Jiaotong University, China. He serves as head of the Computer Engineering subject and program manager for the international MSc program in Computer Engineering. His academic journey includes a Ph.D. in Electrical Engineering from Mid Sweden University (2005), followed by roles at ABB Corporate Research (2008-2014) where he led wireless technologies research. Dr. Gidlund's research spans Wireless Communication, Industrial IoT, 5G/6G Networks, and Network Security . His group focuses on AI/ML for beyond-5G wireless communication, time-critical industrial applications, and IoT security. Current research themes include Future Wireless Networks (5G/6G) using AI/ML, Time-and mission-critical wireless communication, Industrial IoT, and IoT Security. His work demonstrates strong interdisciplinary connections between wireless systems, industrial automation, and security. His publication portfolio includes over 200 scientific articles and 20+ patents. Recent publications show a clear trend toward AI/ML integration in wireless systems, NOMA techniques, RIS technologies, and security solutions for industrial applications. The research output demonstrates strong international collaboration across six continents. Best Paper Award at IEEE International Conference on Industrial IT (2014) Co-author of IEEE Sweden VT-COM-IT Joint Chapter Best Student Journal Paper Award (2022) Dr. Gidlund actively mentors 6 current PhD students and has supervised 16 former PhD students who now hold positions at institutions including Ericsson, Lund University, Aalborg University, and Mid Sweden University. His research is supported by multiple active projects including IRS TransTech, NIIT, ENSURE 6G, and TRUST. He collaborates with institutions worldwide including City University of Hong Kong, Iowa State University, Kyung Hee University, and KTH Royal Institute of Technology. His research group maintains strong industry connections through projects with ABB, Ericsson, and other industrial partners, focusing on practical implementations of wireless technologies for industrial automation and critical infrastructure.
Saleh Javadi is a Senior Lecturer at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. He is actively engaged in research and teaching within the field of systems engineering. His educational background includes: B.Sc. in Electrical-Control Engineering from Amirkabir University of Technology (2009) M.Sc. in Electrical, Electronic and Systems Engineering from The National University of Malaysia (2013) Ph.D. in Systems Engineering from Blekinge Institute of Technology (BTH) (2021) Saleh Javadi's research focuses on signal processing, machine learning, and computer vision , with applications spanning remote sensing, intelligent transportation systems, and AI-driven industrial optimization. His work bridges theoretical advancements with practical implementations, particularly in SAR imagery analysis, drone-based agricultural monitoring, and traffic surveillance systems. His recent publications demonstrate a strong focus on remote sensing technologies, particularly Synthetic Aperture Radar (SAR) image processing and analysis. There's a clear trend toward applying machine learning techniques to solve complex problems in aerial and satellite imagery, traffic monitoring, and agricultural applications. His research shows interdisciplinary connections between computer vision, signal processing, and practical engineering applications. Saleh Javadi has received significant recognition for his innovative work: Innovator of the Year award (SKAPA – Innovation Prize in Memory of Alfred Nobel) in Blekinge for innovative efforts in optimizing and reducing energy consumption in industries by using artificial intelligence ÅForsk Entrepreneur's prize at the Swedish Innovation Council Day – Swedish Incubators & Science Park's annual conference in May 2019 Dr. Javadi is involved in practical applications of his research through projects such as "Artificiell intelligens AI kan reducera ogräsfrön i utsäde" (ongoing) and "Bekämpa Renkavle med hjälp av drönare och Artificiell Intelligens (AI)" (completed). His work demonstrates a strong commitment to translating academic research into real-world solutions that address industrial and environmental challenges. His research appears to be conducted within a collaborative framework, working with colleagues on drone technology, SAR image analysis, and AI applications across multiple domains including agriculture, maritime monitoring, and transportation systems.
Tobias Oechtering is a Professor at the Division of Information Science and Engineering within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. His research focuses on information theory, privacy-preserving technologies, statistical signal processing, machine learning, and smart grid systems. He has held academic positions at KTH since 2008, advancing from Post-Doctoral Researcher to Assistant Professor (2010–2013), Associate Professor (2013–2018), and Professor (2018-present). He has supervised over 20 PhD students and contributed to numerous postdoctoral programs. Research Interests: - Network information theory and physical-layer security - Privacy mechanisms with provable guarantees - Distributed statistical inference and sensor calibration - Reinforcement learning and privacy-aware machine learning - Smart grid privacy and energy management - Wireless communication algorithms and signal processing - Networked control systems and stability analysis He currently supervises 7 PhD students and hosts 3 postdocs. His work has led to over 150 peer-reviewed publications, with recent contributions in privacy-preserving smart grid strategies, adversarial inference control, and information-theoretic security. He has served as editor for IEEE Transactions on Information Forensics and Security and held leadership roles in KTH's Digitalisation Research Platform.
Andrey Anikin is an Associate Professor and Researcher in Cognitive Science at the Department of Philosophy, Lund University. His work focuses on vocal communication and emotions, exploring how the voice conveys information beyond linguistic codes through nonverbal vocalizations and acoustic phenomena. Research Interests Dr. Anikin investigates how vocal qualities like roughness, laughter, and screams convey emotional and social information. His research takes a cognitive and biological approach, examining sensory biases and auditory attention in vocal communication. His work aims to illuminate the evolutionary origins and universal features of vocal communication across human cultures and animal species. Key areas include emotion perception, nonverbal vocalizations, acoustic analysis, and vocal communication systems. Research Output Trends Dr. Anikin's recent publications (2025) demonstrate a strong focus on acoustic properties of vocalizations across species. His work bridges biology, psychology, and acoustics with methodological innovations in analyzing nonlinear phenomena, voice roughness, and formant structures. His research shows increasing interdisciplinary collaboration, particularly with zoologists and signal processing experts, while maintaining a core focus on the cognitive mechanisms underlying vocal communication. Research Projects What makes baby cries impossible to ignore? (2024-2026): Funded by the Swedish Research Council, this active project investigates the sensory mechanisms behind infant cry perception. Sensory biases in nonverbal communication (2021-2023): A completed project funded by the Swedish Research Council that examined how sensory systems shape nonverbal communication. Research Environment Dr. Anikin is affiliated with the Cognitive Zoology Group and the Lund University Cognitive Science (LUCS) program. He contributes to the LU Profile Area: Natural and Artificial Cognition. His work connects with the UN Sustainable Development Goals through interdisciplinary research on communication and cognition, with implications for understanding human well-being and social interaction.
Gustav Henter is an Assistant Professor in Intelligent Systems at KTH Royal Institute of Technology, specializing in Machine Learning. He is affiliated with the Division of Speech, Music and Hearing (TMH) within the School of Electrical Engineering and Computer Science. His research focuses on deep generative models for applications like speech synthesis, 3D character animation, and human-computer interaction. He holds a Docent degree from KTH and has held post-doctoral positions at the University of Edinburgh and the National Institute of Informatics in Tokyo. Education: PhD in Electrical Engineering (KTH, 2013), MSc in Engineering Physics (KTH, 2007). He supervises doctoral students in areas like gesture synthesis and multimodal interaction. His work is supported by grants from the Wallenberg AI, Autonomous Systems, and Software Program (WASP) and South Korea's MOTIE. He co-founded Motorica AB to commercialize motion synthesis research. Awards include Best Paper Awards at ICMI 2020 and IVA 2020, and recognition for student theses. His research spans generative AI, perceptual evaluation, and robust statistical models. He organizes the GENEA Challenge and Workshop series for gesture generation benchmarking.
Bobby Lee Townsend Sturm JR is an Associate Professor at KTH Royal Institute of Technology, leading the MUSAiC project (ERC-2019-COG). He holds a PhD in Electrical and Computer Engineering from UC Santa Barbara (2009), followed by postdoctoral research at LAM, Paris 6, and academic roles at Aalborg University and Queen Mary University of London. His research focuses on AI ethics in music, generative AI for music, and folk music preservation. Current roles at KTH include teaching and supervising in Machine Learning, Music Informatics, and AI Ethics. He has pioneered AI music generation challenges (e.g., 2020 Double Jigs Challenge) and investigates societal impacts of AI on traditional music cultures. His work bridges technical innovation with cultural and ethical considerations, addressing issues like data colonialism, algorithmic bias, and human-AI collaboration in creative contexts. Education: PhD (UCSB, 2009), Postdoc (Paris 6), Academic appointments at Aalborg University (2010–2014) and Queen Mary University (2014–2018) Key Projects: MUSAiC (ERC), Virtual Session System for Irish Music, Traditional Music Dataset Analysis Teaching: Courses in Machine Learning, Music Acoustics, and ICT Innovation Publications span peer-reviewed journals and conferences, emphasizing ethical AI, music generation, and interdisciplinary research in MIR (Music Information Retrieval). He actively collaborates with musicians, anthropologists, and technologists to ensure culturally informed AI development.
Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.
Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se
Håkan Fischer is a Professor of Human Biological Psychology at Stockholm University, where he has served as Head of the Department of Psychobiology and Epidemiology since 2011. He also holds an associate professor position at Karolinska Institutet, is affiliated with the Aging Research Center and Stockholm University Brain Imaging Centre, and is a faculty member at Digital Futures at the Royal Institute of Technology. Since September 2025, he has additionally served as a visiting professor at Linköping University. Fischer has established himself as a leading researcher in emotional and cognitive processing, with particular expertise in socio-emotional aspects across the lifespan. Fischer earned his PhD in psychology from Uppsala University in 1998, followed by postdoctoral research at Harvard Medical School (1999-2001). He then worked at the Aging Research Center at Karolinska Institutet before joining Stockholm University in 2011. His academic journey includes a sabbatical year (2021-2022) at the University of Florida's Department of Psychology. Fischer is actively involved in university governance as a member of the Swedish Research Council's Subject Council for Humanities and Social Sciences (2023-present) and represents Stockholm University in multiple international collaborations. Håkan Fischer's research primarily focuses on investigating intra- and interindividual differences in affective, cognitive, social and perceptual processing, with special emphasis on age-related differences in adults. His laboratory employs advanced neuroimaging techniques including fMRI, PET, and fNIRS to examine brain function, while also utilizing structural imaging methods like T1-weighted imaging, DTI, and perfusion imaging to study brain structure. Fischer advocates for single-subject small-N designs to better understand emotional and cognitive mechanisms. His current research lines include socio-emotional perception and recognition, oxytocin effects on socio-emotional processing across the lifespan, and AI development for interpersonal communication analysis. Analysis of Fischer's recent publications reveals a strong focus on emotion recognition across populations, neurobiological mechanisms of socio-emotional processing, and methodological innovations. His work consistently integrates behavioral testing, neuroimaging, and genetic analysis to provide comprehensive insights. The increasing incorporation of AI approaches demonstrates his adaptation to emerging technological advances in psychological research. Fischer has published 136 peer-reviewed articles with over 12,200 citations and a Google Scholar h-index of 53. Fischer has received consistent funding since 2002 from prestigious sources including the Swedish Research Council, Wallenberg Foundation, STINT, Riksbankens Jubileumsfond, and Konung Gustav V och Drottning Victorias stiftelse. He currently leads nine funded research projects (two as principal investigator totaling 6.9 million SEK, seven as co-applicant totaling 24.8 million SEK) spanning multiple international collaborations in Sweden, Germany, and the USA. As an educator, Fischer leads the basic course in Cognitive Neuroscience and the master's course in Emotion Psychology and Affective Neuroscience. He regularly teaches at both undergraduate and advanced levels, primarily in biological psychology, cognitive neuroscience, and emotion psychology. Fischer currently supervises six doctoral students (one as main supervisor, four as assistant supervisor) and has mentored graduate students since 2002. Håkan Fischer leads a dynamic research laboratory that investigates emotional, social, perceptual, and cognitive processing. The lab examines how intraindividual variations across stimuli and time, as well as interindividual differences in age, gender, genetics, personality, and sleep deprivation affect these processes. His lab maintains active national and international collaborations with researchers at Stockholm University, Uppsala University, Karolinska Institutet, University of Florida, and University of Gothenburg, creating a robust interdisciplinary research environment focused on translating basic neuroscience into practical applications.
Ali W. Elshaari is an Associate Professor at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He holds a B.S. in Electrical Engineering from the University of Benghazi (2007) and a Ph.D. in photonics from the Rochester Institute of Technology (2011). His postdoctoral research at TU Delft’s Kavli Institute of Nanoscience focused on quantum transport. Currently, he leads the Quantum Nano Photonics Group, pioneering work in topological and quantum integrated photonics to develop high-performance circuits for communication, sensing, and metrology. His research spans hybrid quantum photonics, strain-tunable systems, and superconducting detectors, with applications in quantum communication and quantum materials characterization. Elshaari's research interests include integrating single-photon emitters into CMOS-compatible platforms, exploring quantum phenomena like entanglement and coherence, and developing advanced photonic materials (e.g., hexagonal boron nitride and Cu₂O). He has contributed to on-chip single-photon generation/filtering, strain-tunable photonic circuits, and slow-wave superconducting detectors. His work bridges experimental and theoretical approaches, leveraging imaging techniques and phase retrieval algorithms. Elshaari is an editorial board member for Nature Portfolio - Scientific Reports , Wiley Advanced Quantum Technologies , and EPJ Quantum Technology . He teaches courses in quantum technology, electromagnetism, and optical physics. His funding includes grants from the Wallenberg Foundation, Swedish Research Council, Vinnova, and the European Research Council. His lab actively recruits students for bachelor’s and master’s projects in quantum photonics and nanophotonics.
Kjell Brunnström serves as an Adjunct Professor in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, based in Sundsvall. He maintains active affiliation with the STC Research Centre, contributing to cutting-edge research in multimedia quality assessment and immersive technologies through extensive international collaborations. His research centers on Quality of Experience (QoE) with specialized focus on 3D video systems , virtual reality environments , and augmented teleoperation interfaces . He pioneers methodologies for subjective and objective video quality evaluation, particularly examining human factors in sports broadcasting (Video Assistant Referee systems), mining applications, and automotive displays. His work bridges theoretical psychophysical models with industrial implementations through standards development like ITU-T Rec. P.919. Analysis of his 15 most recent publications reveals consistent investigation into latency effects, distortion impacts, and viewing condition variables across emerging media formats. Key application domains include sports video refereeing, 360-degree video systems, VR simulators for remote machinery control, and adaptive streaming protocols, demonstrating strong industry-academia translation. Scientific Awards: No awards documented in source material. Advising and Grants: Student supervision and grant funding details were not disclosed in available documentation. Labs and Teams: Brunnström operates within Mid Sweden University's STC Research Centre framework, collaborating with global institutions including Royal Institute of Technology (Sweden), University of Surrey (UK), and multiple European research consortia. His work directly informs ITU standardization efforts and industrial implementations in broadcast and automotive sectors.
