جستجوی استاد راهنمای دانشگاهی

Danica Kragicمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

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.

Zhonghai Luمشاهده پروفایل

استاد

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence

Karl Henrik Johanssonمشاهده پروفایل

استاد

• Networked and distributed control
• Cyber-physical systems
• Cyber-secure control systems
• +۴ مورد دیگر

Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.

Michael Felsbergمشاهده پروفایل

استاد

• Artificial Visual Systems
• Computer Vision
• Robot Vision
• +۴ مورد دیگر

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.

• Mobile Robotics
• Underwater Robotics
• Autonomous Systems
• +۴ مورد دیگر

John Folkesson is an Associate Professor at the Department of Robotics, Perception and Learning at KTH Royal Institute of Technology. His research focuses on mobile robotics, underwater autonomous vehicles (AUVs), and Simultaneous Localization and Mapping (SLAM), particularly addressing challenges in dynamic underwater environments. He leads the AUV group within the Swedish Maritime Robotics Centre (SMaRC2.0) and supervises multiple PhD projects, including those funded by Ocean Infinity and Vinnova. Folkesson has pioneered work on sonar-based SLAM, bathymetric mapping, and autonomous underwater navigation without human intervention. He teaches courses such as Probabilistic Graphical Models (DD2420) and Applied Estimation (EL2320). Recent projects include developing neural rendering techniques for sidescan SLAM and automatic launch systems for AUVs in collaboration with Purdue University and SAAB. His research emphasizes long-term autonomy, environmental ambiguity, and sensor data interpretation in unstructured underwater scenarios. Education: PhD in Robotics (2005, KTH Royal Institute of Technology) Recent Funding: 2024 projects include ALARS (Vinnova), WASP WARA-PS, and industrial collaborations. Research Interests Folkesson's work spans underwater robotics, SLAM algorithms, and sensor fusion. Key areas include: Underwater SLAM and sonar modeling Bathymetric reconstruction using neural networks Autonomous decision-making in AUV missions Real-time terrain modeling and localization Articles Trends Recent publications emphasize neural networks for SLAM optimization, sonar data processing, and autonomous underwater systems. Themes include real-time bathymetric mapping, sensor fusion in dynamic environments, and neural rendering techniques for improving navigation accuracy. Folkesson's work bridges theory and practice, with applications in marine robotics and industrial surveys. Advising & Grants PhD supervision: AUV perception (2024), SLAM with Ocean Infinity, event-response AUV systems. Collaborations: Purdue University, SAAB, Ocean Infinity. Course responsibilities: Over 10 advanced robotics and engineering courses at KTH. Labs & Teams Lead of SMaRC2.0, KTH's official research center for maritime robotics. Active in developing AUV systems for long-duration missions, including ice-covered and deep-sea exploration.

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +۶ مورد دیگر

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.

• Software Metrics
• Model Driven Software Development
• Empirical Software Engineering
• +۵ مورد دیگر

Miroslaw Staron is a Professor of Interaction Design and Software Engineering at Chalmers University of Technology. He maintains a unique 50/50 work arrangement, spending half his time on field research at Ericsson while holding his academic position. His research bridges academic theory with industrial practice through collaborations with major companies including Volvo Car Corporation and Volvo Information Technology. His research spans several key areas in software engineering: Software metrics and measurement systems in industry Model driven software development and empirical studies Defect prediction in software projects Requirements engineering in model-based development Applications of AI and machine learning in software engineering Automotive software development and security Staron's recent work demonstrates a strategic shift toward integrating AI technologies into software engineering processes, with particular focus on automotive applications. His publications from 2024-2025 reveal expertise in generative AI applications for code review automation, testing methodologies, and requirements engineering, showing how these technologies can transform traditional software development practices while addressing domain-specific challenges in automotive systems. Current research projects include: Kvantdatorer för framtidens mobilitetslösningar (2025-2027) Automatiserad och designoptimerad programvarukonstruktion/kodgenerering (2025-2029) Förvandla fordonsarkitektur med hjälp från AI (2021-2023) Arkitektonisk design och verifiering/validering av system med maskininlärning komponenter (2020-2024) With 78 publications documented in Chalmers' research database, Staron has established himself as a significant contributor to evidence-based software engineering research with strong industrial relevance.

• Process Technology
• Device Design
• Silicon Carbide (SiC) Electronics
• +۳ مورد دیگر

Carl-Mikael Zetterling is a Professor and Head of Department at Kungliga Tekniska Högskolan (KTH) in Stockholm, Sweden, affiliated with the School of Electrical Engineering and Computer Science (ICT) and the Electronics and Embedded Systems department. His research focuses on process technology and device design for high-temperature, high-power silicon carbide (SiC) electronics, expanding into SiC-based analog and integrated circuits. He has authored over 300 publications, including books on SiC process technology and plagiarism prevention. Dr. Zetterling has held leadership roles such as Vice Dean of the School of ICT (2013–2017) and teacher representative on KTH's faculty board. He has collaborated internationally at Stanford University, Kyoto University, and Kyoto Institute of Technology. His work addresses applications in extreme environments, including Venus exploration and fusion reactor monitoring, with a focus on radiation tolerance and thermal resilience. The 15 most recent publications highlight trends in wide bandgap semiconductors, gamma irradiation effects on SiC devices, and high-temperature integrated circuits. His articles span structural health monitoring with machine learning, novel SiC diode designs, and radiation-hardened electronics. Key contributions include advancements in self-aligned contacts, trench MOSFETs, and compact modeling for extreme conditions. While no formal awards are listed, his roles in technical program committees (TMS Electronic Materials Conference, IEEE SISC Conference) and editorial work demonstrate significant academic service. He teaches courses ranging from digital design to high-temperature electronics, overseeing degree projects in embedded systems, communication, and nanotechnology.

Martin Norgrenمشاهده پروفایل

استاد

• Electromagnetic Inverse Problems
• Material Characterization
• Biomedical Imaging
• +۳ مورد دیگر

Martin Norgren is a Professor at KTH Royal Institute of Technology, leading the Department of Electromagnetic Fusion Physics. His research focuses on electromagnetic inverse problems, including material characterization, biomedical imaging (e.g., brain current sources), environmental monitoring (e.g., snow and avalanche prediction), and smart grid technologies. He specializes in reconstructing object properties using electromagnetic measurements and has contributed to applications in healthcare, energy systems, and environmental science. His work involves advanced analytical and numerical methods such as mode-matching techniques, perturbation theory, and convex optimization. Notable projects include noncontact current measurement in power grids and transformer diagnostics using microwave radiation. Norgren teaches courses in electromagnetic field theory and electrical engineering design, emphasizing practical applications and interdisciplinary collaboration. Recent research trends highlight advancements in glide/twist symmetry-based metamaterial design, waveguide analysis, and inverse scattering techniques. His studies bridge fundamental physics with applied engineering, addressing challenges in energy infrastructure and medical diagnostics. As a department head, he oversees educational and research programs at KTH, fostering innovation in electromagnetism and fusion physics. His contributions to curriculum development include project-based courses integrating theory and hands-on design.

• Nuclear Reactor Safety
• Thermal-Hydraulics
• Computational Fluid Dynamics (CFD)
• +۳ مورد دیگر

Satya Prakash Saraswat is a Postdoctoral Researcher at KTH Royal Institute of Technology's Nuclear Science and Engineering Unit in Stockholm, Sweden. He holds a Ph.D. from the Indian Institute of Technology Kanpur, with expertise in thermal-hydraulics, nuclear reactor safety, computational fluid dynamics (CFD), and system code development. His work spans fission and fusion reactor analysis, including contributions to the VALIDATIO project (University of Pisa) for fusion safety tools and the ATLAS project (Khalifa University) for advanced reactor safety enhancements. Research interests focus on computational modeling, AI integration in nuclear safety, and experimental validation of safety systems. He has developed skills in both experimental and numerical techniques, addressing challenges in multiphase flow, reactor core dynamics, and material compatibility. Key projects include validation of ASYST and SIMMER codes for condensation phenomena and lead-lithium interaction studies. Publications highlight advancements in burn-up wave characterization, code stability analysis (RELAP5/SIMMER), and thermal-hydraulic safety assessments for reactors like ESBWR and ITER systems. His work emphasizes enhancing safety tools through rigorous validation and innovative methodologies.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

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

Martin Servinمشاهده پروفایل

دانشیار

• Digital Physics
• Computational Modelling
• Robotics
• +۳ مورد دیگر

Martin Servin is an Associate Professor at the Department of Physics, Umeå University, and leads the Digital Physics research group within the UMIT Research Lab. His work focuses on computational modeling and simulation of granular materials, robots, and vehicles, with applications in AI-based control and perception. He holds a doctoral degree from Umeå University (2003) and has pioneered research in real-time physics simulation, particularly in the context of autonomous machinery and off-road robotics. Research Interests : Digital physics, granular materials simulation, autonomous systems, reinforcement learning, and simulation-to-reality transfer. His group develops advanced simulation tools for industries like forestry, mining, and construction. Key Projects : Mistra Digital Forest (2019–2026) AILUR (Digital Twin for AI-controlled Lunar Robotics) XSCAVE (Explainable, Safe Control for Heavy Machinery) Publications emphasize simulation methodologies, AI integration, and real-world validation across robotics, vehicle dynamics, and granular mechanics. Notable contributions include work on wheel loader dynamics, deep reinforcement learning for control systems, and terrain modeling. Awards include the Spin-off award for industry-grade physics in Unreal Engine (2018) , recognizing his role in Algoryx Simulations, a spin-off company commercializing his research. Labs/Teams : UMIT Research Lab, Digital Physics Group, and collaborations with Algoryx Simulations.

• Mechatronics
• Signal Processing
• Control Systems
• +۷ مورد دیگر

Jonas Sjöberg is a Full Professor of Mechatronics at Chalmers University of Technology, where he leads the Mechatronic research group in the College of Engineering. His research spans multiple aspects of mechatronic systems with a strong focus on automotive applications. Sjöberg holds leadership roles in numerous research projects related to autonomous vehicles, vehicle control systems, and transportation safety. His research interests encompass a broad spectrum of mechatronics applications, with particular emphasis on model-based methods, signal processing, control systems, system identification, and optimization for design and product development of mechatronic systems. Sjöberg's work bridges theoretical control engineering with practical automotive applications, especially in the domains of Automotive Active Safety and Hybrid Electric Vehicles. Analysis of Sjöberg's recent publications reveals a strong research trajectory focused on autonomous vehicle technologies, with particular attention to vehicle dynamics control, intersection safety, road surface condition estimation, and optimization of vehicle maneuvers. His work demonstrates a consistent approach of applying advanced control theory to solve real-world transportation challenges, with increasing emphasis on machine learning techniques integrated with traditional control systems. Sjöberg actively supervises research and education at both undergraduate and graduate levels while leading multiple research projects funded by VINNOVA, the European Commission, and other organizations. His research group collaborates extensively with both academic institutions and industry partners in the automotive sector. His laboratory work focuses on mechatronic systems development, particularly for automotive applications including autonomous bicycles, bus docking systems, and vehicle control algorithms. The research group maintains strong connections with the automotive industry, particularly in Sweden's robust vehicle technology ecosystem.

• Vehicular Control Systems
• Electric and Hybrid Vehicle Technology
• Vehicle Dynamics
• +۴ مورد دیگر

Jonas Fredriksson is a Professor in the Mechatronics research group at the Department of Systems and Control Engineering, Chalmers University of Technology. His work focuses on electric/hybrid vehicles, vehicle dynamics, active safety systems, and optimization-based coordination of automated vehicles. Academic Rank: Professor Affiliation: Chalmers University of Technology Department: Systems and Control Engineering Email: jonas.fredriksson@chalmers.se Research Themes: Powertrain control and energy management for electric/hybrid vehicles Advanced control strategies for heavy articulated vehicles Autonomous driving in confined environments Battery thermal management and charging optimization Vehicle stability and safety systems using Newtonian mechanics Article Trends: Recent publications emphasize 1) optimization algorithms for electric vehicle coordination, 2) aerodynamic modeling under crosswind conditions, 3) stochastic approaches to longitudinal vehicle dynamics, and 4) robust control systems for articulated heavy vehicles. The work combines classical mechanics with modern machine learning techniques. Teaching & Leadership: Supervises doctoral students and leads research projects in mechatronics. Manages the master's program in Systems, Control and Mechatronics. Teaches courses in mechatronics and vehicle control systems.

Wlodek Kuleszaمشاهده پروفایل

استاد

• Systems Engineering
• Measurement and Instrumentation
• Sensor Networks
• +۳ مورد دیگر

Wlodek Kulesza is a Senior Professor in the Department of Mathematics and Natural Sciences at the Blekinge Institute of Technology in Karlskrona, Sweden. He has held academic positions since 2001, including a Professorship in Multi-sensor Systems since 2007. Research: Multisensory systems for health and security applications Teaching: Research Methodology, Philosophy of Science, and Sensors Signals and Systems Expertise: Systems engineering, sensor fusion, IoT, and measurement data handling Research Focus : His work in systems engineering emphasizes multisensor integration for applications spanning healthcare, security, wind energy safety, and subsea cable monitoring. He has pioneered approaches to stereovision calibration, localization algorithms, and real-time safety systems. Awards : Andy Chi Best Paper Award (2009, IEEE Transactions in Measurement and Instrumentation) Collaborations : Visiting Professor at Chinese and Polish universities, with extensive cross-border educational workshops and remote lab federations (e.g., PILAR/VISIR projects).