Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.
Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.
Yang Liu is a tenured Associate Professor at the Department of Management and Engineering , Linköping University, Sweden, and an Adjunct Professor at the University of Oulu, Finland. His expertise spans smart manufacturing, clean energy transition, and Industry 4.0 applications. He holds an M.Sc. and D.Sc. from the University of Vaasa, Finland. Research & Awards: Liu's work focuses on sustainable systems, decision support systems, and AI-driven energy efficiency. He has authored over 140 Web of Science publications, including top 0.1% ESI Hot Papers. He is ranked among the world's top 2% scientists (Stanford-Elsevier) and leads globally in 'big data analytics in manufacturing' and 'Industry 4.0-driven circular economy' research. Leadership & Projects: He leads projects like FlexSUS (EU Horizon 2020) and PERSEUS, developing tools for smart urban energy planning and 15-minute city models. He serves as Editor-in-Chief of Cleaner Engineering and Technology and Guest Editor for multiple journals. His research emphasizes bridging data science with sustainability challenges in manufacturing and energy systems. Key Achievements: Top-ranked in global citations, ESI Highly Cited Papers, and industry-driven sustainability frameworks. Grants: Leads EU-funded projects and collaborates with Siemens Energy on energy transition solutions. Labs & Teams: Part of the Environmental Technology and Management (MILJÖ) division and Unit for Product Service Innovation (MILJOPSI) at Linköping.
Dr. Harry T. Conway is the Alternate Permanent Representative of Liberia and Chair of the Marine Environment Protection Committee (MEPC) at the International Maritime Organization (IMO). He leads global maritime environmental policy initiatives, including the 2023 IMO GHG Strategy. His academic affiliations include guest lecturing at the International Maritime Law Institute's special course on marine environmental protection and opposing a WMU PhD candidate's thesis. Education: PhD in Politics and International Relations (Cardiff University, UK), MA (Distinction) in International Maritime Policy (University of Greenwich, UK), and MBA (Hanze University of Applied Sciences, Netherlands). His research focuses on environmental politics, with emphasis on climate agreements and maritime decarbonization. Professional Experience: Policy Officer and Officer-in-Charge of Liberia's IMO Permanent Mission (2012–present), Chief of Staff at Liberia Maritime Authority (pre-2012), and finance roles in Liberian banking institutions. He contributed to the 2050 Africa Integrated Maritime Strategy for the African Union and serves on the International Quality Assessment Review Body (IQARB). Publications include contributions to 'The African Union and the Law of the Sea' and peer-reviewed articles on maritime innovation and decarbonization. He frequently speaks at international shipping conferences and advises on maritime regulatory frameworks.
Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.
Hedvig Kjellström is a Professor at the Division of Robotics, Perception and Learning within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. She holds significant affiliations with the Swedish e-Science Research Centre and the Max Planck Institute for Intelligent Systems in Germany. Her work spans multiple interdisciplinary domains and she serves as Editor-in-Chief for CVIU and was Program Chair for CVPR 2025. Her research centers on Computer Vision as a sub-field of AI, with three interconnected themes: Computational Aesthetics (exploring aesthetic aspects of human communicative behavior), Communicative Behavior (developing models of how humans and animals perceive and produce non-verbal communication), and Embodied Artificial Intelligence (creating methodologies for robots and autonomous agents to perceive the world through sensors, primarily vision). Her work has significant applications in medical diagnostics, animal welfare, human-robot interaction, and creative arts. Analysis of her recent publications reveals a strong trend toward multimodal AI systems that integrate vision, language, and action understanding. Her research increasingly focuses on animal-centered applications, particularly equine pain detection and behavior analysis, while maintaining strong foundations in human communication modeling, gesture recognition, and 3D reconstruction techniques. The interdisciplinary nature of her work bridges computer science with veterinary medicine, neuroscience, and performing arts. Hedvig Kjellström actively supervises numerous PhD and Master's students across various projects and maintains extensive collaborations with institutions including Karolinska Institutet, Swedish University of Agricultural Sciences, and international partners. Her research is supported by major funding bodies including WASP, VR, and SeRC. She leads or participates in several notable projects including OrchestrAI (communication between conductor and orchestra), ANITA (Animal Translator), MARTHA (3D horse motion analysis), and STING (synthesis and analysis with transducers and invertible neural generators). Her work with ACAI (Animal Centered Artificial Intelligence), which she co-founded and directs, demonstrates her commitment to applying AI for animal welfare.
Thomas Winkler is an Associate Professor at the Division of Micro and Nanosystems, KTH Royal Institute of Technology, Sweden, and collaborates with TU Braunschweig, Germany. His research focuses on solving life science challenges using microsystems tools, particularly in neuropsychiatric disorders like schizophrenia. He develops organ-on-chip models, engineered microfluidic platforms, and biosensors for point-of-care diagnostics. Winkler leads an interdisciplinary ERC-funded team addressing metabolic coupling in neurovascular units and oxidative stress biomarkers. Key achievements include the ERC Starting Grant (2023) and work on electrochemical sensors for clozapine monitoring. He teaches courses such as Microsystem Technology (EK2350) and supervises PhD and postdoctoral researchers. Current projects include machine learning-guided robotic organoid maturation and electrochemical technology development for the CHIPzophrenia initiative. His lab actively seeks talent through open positions in Stockholm and Braunschweig. Scientific awards include the ERC Starting Grant and Marie Skłodowska-Curie Actions Fellowship. Research spans sensor development, microfabrication, and biomaterials, with a focus on translating lab technologies to clinical applications. Collaborations bridge engineering and life sciences, emphasizing personalized mental healthcare solutions.
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.
Michael Felsberg is a Professor and Head of Division at the Department of Electrical Engineering (ISY) at Linköping University, leading the Computer Vision Laboratory (CVL). His research focuses on artificial visual systems (AVS), including 3D computer vision, computational imaging, object tracking, and autonomous systems. He emphasizes HVS-inspired approaches to bridge the gap between human and machine vision capabilities. Notable achievements include over 20,000 citations (h-index 47), leadership roles in the Wallenberg AI, Autonomous Systems and Software Program (WASP), and recognition as Sweden’s top AI researcher by Vinnova. His work spans academic contributions, industry collaborations, and interdisciplinary projects like climate science applications of machine learning. Positions : WASP Executive Committee Member, WASP Area Cluster Leader for Machine Learning, and Vice-Head of Department (Electrical Engineering). Education : Extensive academic background in electrical engineering and computer vision (details not explicitly stated). Research trends in his articles reflect advancements in autonomous systems, multimodal AI, and robust vision models. His teams address challenges like object tracking, generative models for 3D simulation, and culturally diverse AI systems. Awards : Tracking Challenge Winner (OpenCV, 2015) Best Paper Awards (ICPR 2016, VISAPP 2021) Vinnova’s Highest-Ranked Swedish AI Researcher (2018) He advises numerous PhD students and oversees grants in WASP-funded initiatives. CVL collaborates on projects like disaster-response robotics and Berzelius supercomputer utilization for AI.
Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.
Mario Romero is an Associate Professor in Visualization at the Department of Computational Science and Technology (CST), KTH Royal Institute of Technology. He leads the InfraVis national research infrastructure for data visualization and is a Digital Futures Faculty member. His roles include national technical manager of InfraVis, member of the Executive Committee of Digital Futures, and Associate Director for Seminars & Workshops. Education: PhD in Computer Science (Georgia Tech, 2009), MSc in Computer Science (UIUC, 2001), and dual BSc degrees in Industrial Engineering and Construction Engineering (Universidad San Francisco de Quito, 1996). He is a Fulbright Scholar from Ecuador and holds postdoctoral experience at Uppsala University. Research focuses on Human-Computer Interaction, Visualization, and Ubiquitous Computing. Key projects include: TENT: Technology-Enhanced Neurosurgical Training VisBac: Visualizing airborne bacteria in ORs PSP: Platform for Smart People (autism support) SMART: Predictive maintenance in pharmaceuticals Homo Colossus: Energy footprint visualization Awards: Selected for IVA's 100 research2business projects (2021). Co-founded BrailleTouch (blind-friendly keyboard) and Anymaker (3D sketching app). Supervised students in C-Awards-winning projects (e.g., Yue Liu's thesis defense in 2024). Teaching: Responsible for courses like Information Visualization (DH2321) and Advanced Graphics & Interaction (DH2413). Active in organizing conferences (e.g., Eurographics 2020 Education Track Chair).
Klas Hjort is a Professor of Materials Science at Uppsala University's Ångström Laboratory , specializing in Microsystems Technology . He leads the microsystems technology program and has pioneered research in heterogeneous microsystems on stainless steel, flexible foils, and elastic substrates for biomedical applications and wireless sensor/actuator systems . Key projects: SSF robotic textiles , PERSIMMON smart patches Research themes: Microfluidic actuation , Liquid metal patterning , Stretchable electronics His recent publications focus on soft robotics , smart patches , and high-pressure microfluidic systems , with keywords spanning Microfluidics , Biomedical Engineering , and Stretchable Electronics . He collaborates extensively in robotic textiles , microvalve design , and liquid metal composites . Contact: klas.hjort@angstrom.uu.se
Lars Hanson is a Professor of Product Design Engineering at the University of Skövde's School of Engineering Science. His research focuses on ergonomics, digital human modeling, and optimizing manufacturing systems with a strong emphasis on human well-being and sustainable production. He leads projects like LITMUS (Industry 4.0 to 5.0 transition) and has contributed to developing tools such as IPS IMMA for ergonomic simulations. Active in virtual verification of human-robot collaboration and smart textile systems for workplace safety Published extensively in journals like International Journal of Human Factors Modelling and Simulation and IEEE Access Editor of conference proceedings and contributor to industry standards in automotive and healthcare sectors Research interests include multi-objective optimization of factory layouts, musculoskeletal risk assessment, and integrating ergonomic evaluations into product design processes. Current projects address Industry 5.0 sustainability challenges through digital twin technologies and smart manufacturing solutions.
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Christian Smith is an Associate Professor and Lecturer at the Department of Robotics, Perception and Learning at Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on robotics and applications in human-centered environments like home environments, small workshops, and healthcare facilities, including the development of new robotic systems for research. Teaching Roles: Course Coordinator/Teacher/Examiner for courses such as Introduction to Robotics (DD2410), Research Project in Robotics (DD2411), and Java Programming for Python Programmers (DD1380) Research Themes: Human-Robot Interaction, Behavior Trees, Exoskeletons, Intent Recognition, and Multimodal Perception Awards: No specific scientific awards mentioned in the provided text His KTH profile highlights work on adaptive robotics systems and formalized control strategies. The research portfolio spans from theoretical studies on behavior tree programming to applied work in assistive technologies and teleoperation systems.
