جستجوی استاد راهنمای دانشگاهی

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

Cyrille Arthoمشاهده پروفایل

دانشیار

• Software Engineering
• Formal Methods
• Cybersecurity
• +۵ مورد دیگر

Cyrille Artho is an Associate Professor in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, actively contributing to research in formal methods, software testing, and cybersecurity. His work spans model checking, smart contract security, and concurrent systems verification, with significant contributions to tools like Java Pathfinder and Modbat. PhD from ETH Zurich (2005) with dissertation on multi-threading fault detection Teaches Software Safety and Security, Software Engineering Fundamentals, and supervises degree projects His research focuses on developing formal techniques for safety-critical systems, particularly in blockchain security and distributed applications. Recent work emphasizes smart contract verification, anomaly detection in microservices, and trusted execution environments for secure cloud analytics. The 15 most recent publications reveal a strong trend toward blockchain security (6 articles), formal verification of distributed systems (5), and novel testing methodologies (4), with increasing integration of machine learning for vulnerability detection. As chair of the FTSCS workshop series and contributor to major conferences like ASE and ICST, Artho has significantly shaped the formal methods community. His leadership in organizing workshops demonstrates commitment to advancing safety-critical systems research. Principal investigator for C3.ai DTI Cyber Safety Cage for Networks project Develops Modbat framework for model-based API testing Active in Digital Futures research initiative at KTH

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.

• 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.

Martin Berggrenمشاهده پروفایل

استاد

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +۸ مورد دیگر

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .

• Bioinformatics
• Systems Biology
• Disease Module Identification
• +۶ مورد دیگر

Zelmina Lubovac is a Senior Lecturer in BioInformatics at the School of Bioscience, University of Skövde. She serves as both a Course Coordinator for multiple undergraduate and graduate courses in bioinformatics and a Programme Coordinator for Master's level programs. Her academic work focuses on the intersection of computational methods and biological applications, particularly in disease analysis and biomarker discovery. Dr. Lubovac's research spans several key areas in bioinformatics and systems biology: Disease module identification in complex biological networks Multi-omics integration (genomics, proteomics, metabolomics) for biomarker discovery Machine learning applications in RNA-seq and other high-throughput biological data Development of bioinformatics software tools for network analysis miRNA analysis in cancer and neurological disorders Her recent publications (2022-2024) demonstrate a strong focus on applying computational approaches to understand disease mechanisms, particularly in pancreatic cancer and multiple sclerosis. She has developed several widely-used bioinformatics tools including MODalyseR, MODifieR, and TFTenricher that facilitate disease module analysis and gene network interpretation. Her work often involves collaborative research with clinical teams to translate computational findings into potential diagnostic applications. Dr. Lubovac has been involved in significant research projects including: BIO-AID (Biomedical AI-driven data analytics): Oct 2020 - Sep 2024 Systems Biology DMDPipe: Mar 2018 - Feb 2021 She actively contributes to both undergraduate and graduate education at the University of Skövde, coordinating multiple courses and programs in bioinformatics and bioscience, with a clear emphasis on preparing students for careers at the intersection of biology and computational science.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

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.

Elina Rönnbergمشاهده پروفایل

استاد

• Discrete Optimisation
• Mathematical Optimisation
• Scheduling
• +۱۸ مورد دیگر

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

• computer security
• formal methods
• program logics
• +۳ مورد دیگر

Mads Dam is a Professor in Teleinformatics at the School of Computer Science and Communication at Kungliga Tekniska Högskolan (KTH), where he heads the Department of Theoretical Computer Science. His research focuses on computer security, formal methods, and program logics, with particular emphasis on the formal modeling and verification of low-level hardware and software execution platforms for security and application isolation. His educational background includes: PhD in Computer Science from the University of Edinburgh (1990) MSc in Computer Engineering from Aalborg University, Denmark BSc in Information Technology from Aalborg University, Denmark Mads Dam's research interests center on computer security, formal methods, and program logics. His current work focuses on the formal modeling and verification of low-level hardware and software execution platforms such as hypervisors and OS kernels and their underlying hardware. He has made significant contributions to information flow security, verification of microarchitectural systems, and network programming language security. His research bridges theoretical foundations with practical security applications. His recent publications show a strong trend toward verifying low-level systems, with a focus on information flow security for processors, network programming languages (particularly P4), and microarchitectural vulnerabilities. His work combines formal methods with practical security concerns, developing verification techniques that address real-world security challenges in hardware and software systems. The research spans theoretical foundations in temporal and epistemic logics to practical applications in network security and processor verification. His scientific awards and recognition include: Two framework grants from the Swedish Foundation for Strategic Research A junior individual grant from the Swedish Foundation for Strategic Research Project grants and a five-year research fellowship from the Swedish Research Council (VR) Project grants from Ericsson, Microsoft Research, US Air Force, and Vinnova (the Swedish Innovation Agency) Mads Dam has been a principal investigator on numerous research projects and has supervised many graduate students. He has been a partner in several European projects including HATS, S3MS, VerifiCard, LOMAPS, and UaESMC. His research has been supported by substantial grants from major funding bodies, reflecting the significance and impact of his work in computer security and formal methods. He is a founding member of several research centers at KTH, including Access, the CASTOR software research center, and the CDIS center for cyber defense and information security. These centers bring together researchers from multiple disciplines to address complex challenges in cybersecurity and software engineering.

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۷ مورد دیگر

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.

Morteza Haghir Chehreghaniمشاهده پروفایل

استاد

• Machine Learning
• Decision Making
• Reinforcement Learning
• +۱۷ مورد دیگر

Morteza Haghir Chehreghani is a Professor of Artificial Intelligence and Machine Learning at the Data Science and AI Division of Chalmers University of Technology , Sweden. He leads the Machine Learning and Decision Making Lab and is affiliated with WASP , CHAIR , and ELLIS . Education : PhD in Computer Science (2014) from ETH Zurich under Prof. Dr. Joachim M. Buhmann Prior Roles : Staff Research Scientist at Naver Labs Europe (2014-2018) Research spans Interactive Machine Learning , Sequential Decision Making , Federated Learning , Efficient Deep Learning , and Graph-Based Learning . Key application areas include Transport , Autonomous Systems , Energy , Drug Discovery , and Computational Biology . Selected Publications (2020-2025) demonstrate expertise in Reinforcement Learning for drug design, Minimax Distance Measures for clustering, and Graph Neural Networks for trajectory analysis. Current work focuses on Combinatorial Bandits and Human-in-the-loop AI . Teaching includes graduate courses like Advanced Topics in Machine Learning (DAT441/DIT41), Algorithms for Machine Learning (TDA233/DIT382), and PhD-level Advanced Reinforcement Learning . He has also taught Statistical Methods for Data Science and Theoretical Foundations of ML . Patents include systems for Autonomous Vehicle Motion Control , K-NN Search via Minimax Distances , and Trip Prediction Algorithms . Collaborative projects involve Nature Communications (2022) and multiple ICML / CVPR publications.

• 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.

• Turbulence Modeling
• Computational Fluid Dynamics (CFD)
• Large Eddy Simulation (LES)
• +۷ مورد دیگر

Lars Davidson is a Professor in the Department of Fluid Dynamics at Chalmers University of Technology. His research focuses on numerical simulations of fluid flow and heat transfer, with an emphasis on turbulence modeling for Large Eddy Simulation (LES) and hybrid LES/RANS methods. He has developed computational codes CALC-BFC and CALC-LES based on finite-volume techniques, and recently integrated machine learning to enhance wall functions and turbulence models. Key projects include Hybrid LES/RANS for wall-bounded flows Machine learning applications in fluid dynamics Aeroacoustic noise reduction in automotive and aerospace systems Wind turbine load analysis in forested regions . His publications span 302 articles in journals and conferences, with recent work on Neural networks for turbulence closure Plasma actuators for drag reduction Lattice Boltzmann wall-modeled LES . Collaborations include teams at Volvo, Siemens, and international research groups.

Martina Maggioمشاهده پروفایل

استاد

• Control Theory
• Cyber-Physical Systems
• Real-Time Systems
• +۵ مورد دیگر

Martina Maggio is a Professor at the Department of Computer Science, Saarland University (full-time since 2020) and holds a 20% position at Lund University's Department of Automatic Control (since 2023). She serves as Coordinator of LTH's AI and Digitalization Profile Area and is a member of Lund University's Natural and Artificial Cognition initiative. Her research integrates control theory, real-time systems, and cybersecurity in cyber-physical systems. She has supervised over ten PhD students and postdoctoral researchers, contributing to advancements in resource allocation, fault-tolerant control systems, and self-aware computing. Education: PhD in Control Theory from Politecnico di Milano (with MIT visiting research), postdoctoral work at Lund University. Key affiliations: ELLIIT, Bosch Corporate Research (sabbatical 2019). Research focuses on robust control strategies under computational uncertainties, cyberattacks, and sensor misalignment. Notable work includes influential papers at the intersection of software engineering and control theory (e.g., 2015 SEAMS most influential paper). Grants and advising: Supervised 10+ students, including alumni Dr. Nils Vreman (2023) and Dr. Gautham Nayak Seetanadi (2021). Active in collaborative projects like Bosch's control system verification initiatives. Labs/Teams: Leads research groups at Saarland and Lund, focusing on real-time systems, embedded systems security, and AI-driven control architectures.

Zebo Pengمشاهده پروفایل

استاد

• Embedded Systems
• Electronic Design Automation
• SoC Testing
• +۶ مورد دیگر

Zebo Peng is a Professor and Deputy Head of Department at Linköping University's Department of Computer and Information Science (IDA), leading the Software and Systems (SAS) division. His research focuses on embedded systems design, electronic design automation, SoC testing, and real-time systems with emphasis on fault tolerance and hardware/software co-design. He has contributed to projects like the ASTECC initiative, funded by the Swedish Foundation for Strategic Research, addressing adaptive software in edge-cloud continuum systems. Key research interests include cyber-physical systems security, time-sensitive networking (TSN), and optimization techniques using genetic algorithms. Recent work explores thermal-aware design for reliability, security-aware scheduling, and stability guarantees in control systems. His publications span journals like IEEE TPDS and ACM TECS, alongside conference contributions on topics like resource management and fault detection in distributed systems. Prof. Peng collaborates extensively within the SAS division, which bridges academic and industrial research in software engineering and computer systems. His team's projects address challenges in real-time systems, embedded security, and parallel computing architectures.