جستجوی استاد راهنمای دانشگاهی

Panos Papadimitratosمشاهده پروفایل

استاد

• Cybersecurity
• Networking and Communications
• Artificial Intelligence
• +۶ مورد دیگر

Panos Papadimitratos is a Professor at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty's Division of Software and Computer Systems. He leads the Networked Systems Security Lab and is part of the Trust Working Group. His research focuses on cybersecurity in vehicular systems, global navigation satellite systems (GNSS), edge AI applications, and privacy-preserving technologies. Education details are not explicitly stated in the provided text, but his extensive work suggests expertise in computer science, networking, and security systems. His research interests emphasize resilient communication protocols, secure authentication mechanisms, and adversarial machine learning applications. Recent publications highlight advancements in vehicular network security, GNSS spoofing detection, and AI-driven cybersecurity solutions. His work addresses challenges like DoS resilience, decentralized privacy, and large language model safety filters. Awards and grants are not explicitly listed here, but his prolific publication record indicates sustained research funding. He collaborates with RISE Research Institutes and Stockholm University through Digital Futures, focusing on societal challenges in digital technology development. Papadimitratos is involved in developing secure vehicular communication frameworks (e.g., VPKIaaS), privacy-enhancing mix-zones for location anonymity, and hardware-based GNSS attack mitigation systems. His lab's innovations include tools like RTKiller for GNSS rover control and statistical methods for spoofing detection.

Mikael Skoglundمشاهده پروفایل

استاد

• Information theory
• Communications
• Signal processing
• +۵ مورد دیگر

Mikael Skoglund is a Professor and Head of the Division of Information Science and Engineering at KTH Royal Institute of Technology. His academic roles include membership in the Executive Committee of Digital Futures, and leadership in projects such as Humanizing the Sustainable Smart City (HiSSx) and PERCy. He holds a Ph.D. from Chalmers University of Technology (1997) and became a Professor at KTH in 2003. Education: Ph.D. in Signal Processing, Chalmers University of Technology, 1997 Docent in Signal Processing, KTH, 2001 Professor of Communication Theory, KTH, 2003 Research Interests: Focus on Information Theory, Communications, Signal Processing, Networks, Security and Privacy, Quantum Information, and Inference & Learning. His work bridges theoretical foundations with applications in wireless systems, federated learning, and privacy mechanisms. Projects & Collaborations: Leading initiatives in integrated sensing and communication (ISAC), federated learning, and smart city technologies. Active in strategic research areas like ICT-TNG and Digital Futures. Awards: IEEE Fellow (2023). Students & Grants: Supervised over 40 Ph.D. and postdoc researchers. Notable students include Borja Rodríguez Gálvez (Ph.D. 2024) and Amirreza Zamani (Ph.D. 2023). Active in securing grants for projects like PERCy and HiSSx. Labs/Teams: Part of the Digital Futures research center and the Division of Information Science and Engineering at KTH, fostering interdisciplinary innovation in digital technologies.

Liam Solusمشاهده پروفایل

استادیار

• Combinatorics
• Algebraic and Geometric Combinatorics
• Discrete Geometry
• +۵ مورد دیگر

Liam Solus is an Assistant Professor of Mathematics at the Department of Mathematics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the ALGEBRA COMBINATORICS & TOPOLOGY division and the Digital Futures Faculty, focusing on research in combinatorics, discrete mathematics, algebraic statistics, and their applications in artificial intelligence and causal inference. His work is supported by grants from WASP (Wallenberg AI, Autonomous Systems, and Software Program), the Swedish Research Council, and the Göran Gustafsson Foundation. Education: PhD in Mathematics (University of Kentucky, 2015), postdoctoral fellowships at MIT, IST Austria, and the Max Planck Institute. Current research projects include causal AI for mobility behavior analysis (cAIMBER), combinatorial and geometric approaches to statistical models, and algebraic methods in machine learning. Research Interests: Algebraic and geometric combinatorics, lattice polytopes, causal inference, graphical models, algebraic statistics. Key contributions include studies on Ehrhart theory, Bayesian networks, and combinatorial sequences like Eulerian polynomials and Fibonacci numbers in causal structures. Awards: Large Prize for Young Researchers (Göran Gustafsson Foundation, 2023). Supervision includes PhD students Danai Deligeorgaki and former student Petter Restadh, along with postdocs in algebraic statistics and combinatorics. Labs/Teams: Co-PI of cAIMBER, member of Digital Futures, organizer of international workshops (e.g., AlCoVE 2024 in VR). Teaching includes courses on statistical learning, algebraic statistics, and probabilistic graphical models.

Pernilla Ulfvengrenمشاهده پروفایل

دانشیار

• Aviation Noise Analysis
• Sustainable Aviation Systems
• Human-Machine Systems
• +۵ مورد دیگر

Pernilla Ulfvengren is an Associate Professor in the School of Industrial Engineering and Management at KTH Royal Institute of Technology. She holds a PhD in Human-Machine Systems and is a Docent in Industrial Engineering and Sociotechnical Systems. Her roles include Program Director for the Industrial Technology and Sustainability program (Civilingenjör Industriell Teknik och Hållbarhet, CITEH) and Director of the KTH Centre for Sustainable Aviation. She is also affiliated with the Digital Futures Faculty. Her research focuses on aviation noise analysis, sustainable aviation systems, socio-technical systems governance, safety management, and work system innovation. She has contributed to studies on urban air mobility (UAM), noise exposure from drone traffic, and wicked problems in aviation noise systems. Her work integrates interdisciplinary approaches to address complex challenges in transportation infrastructure and policy. Key projects include the FLYT 365 initiative for innovative collective transportation and the CITEH program focusing on industrial sustainability. She has led research on metrics for aviation operational data, emergency response systems for maritime autonomy, and risk governance frameworks like the ARK platform. Her publications span over two decades, addressing topics from auditory alert systems in cockpits to policy recommendations for airport collaborative decision-making (A-CDM). Dr. Ulfvengren has collaborated on projects such as the SAS HILAS safety management system and has contributed to education reforms, including flipped education methods for systems thinking. Her work emphasizes bridging technical and human-centric aspects of complex systems, with a focus on sustainability and innovation in transportation systems.

Ruoli Wangمشاهده پروفایل

دانشیار

• Biomechanics
• Musculoskeletal Modeling
• Medical Imaging Techniques
• +۵ مورد دیگر

Ruoli Wang is an Associate Professor at the Department of Engineering Mechanics, KTH Royal Institute of Technology. She holds a BEng (2004) from Southeast University and MSc/PhD (2007/2012) in Engineering Mechanics from KTH. Her research focuses on biomechanical methodologies for neuromusculoskeletal systems, combining medical imaging, musculoskeletal modeling, and optimization. She leads projects on digital twins for neurorehabilitation and PelvicMIM for childbirth-related injuries. She is a member of the Promobilia MoveAbility Lab and affiliated with the Division of Paediatric Neurology at Karolinska Institutet. Awards include the ISB Clinical Biomechanics Award (2023) and Docent title in Biomechanics (2022). Her research interests include in vivo muscle morphology quantification, neural-muscle interaction, and wearable sensor technologies. She teaches courses like Biomechanics of Human Movement and oversees degree projects in Engineering Physics and Mechanics. Major grants include funding from Stiftelsen Promobilia, Carlssons Stiftelse, and the Swedish Research Council. Her lab develops non-invasive methods to estimate muscle neuromechanics, with applications in clinical assessment and biofeedback-based rehabilitation. Current projects address personalized medicine in neurorehabilitation and real-time musculotendon parameter estimation.

Xi Wangمشاهده پروفایل

دانشیار

• Cloud-based Manufacturing
• Sustainable Manufacturing
• Robotics
• +۳ مورد دیگر

Xi Wang is an Associate Professor in the Department of Production Engineering at KTH Royal Institute of Technology, Sweden, and serves as the Division Head of Industrial Production Systems (IPS). He holds a PhD from the University of Auckland (2013) and a Bachelor's from Tianjin University (2008). His research focuses on Cloud-based Manufacturing, Sustainable Manufacturing, Robotics, Digital Twin Technology, and Computer-aided Design. He is actively involved in editorial roles for journals like the International Journal of Manufacturing Research and the Journal of Manufacturing Systems. Education: PhD in Mechanical Engineering, University of Auckland, 2013 Bachelor of Mechanical Engineering, Tianjin University, 2008 Research Interests: Dr. Wang’s work emphasizes integrating advanced technologies into manufacturing systems, including: Cloud Manufacturing: Cyber-Physical Systems and Service Composition Sustainable Practices: Energy Efficiency and Recycling Systems Robotics: Collaborative Systems and Calibration Methods Digital Twin: High-Fidelity Modeling for Industry 5.0 His recent publications highlight optimization algorithms, predictive modeling, and blockchain applications in manufacturing. Professional Contributions: Managing Editor, International Journal of Manufacturing Research (IJMR) Editorial Board Member of 3+ international journals Lead researcher on EU-funded projects like SYMBIO-TIC and CAPP-4-SMEs Lab/Team Involvement: His research groups focus on Human-Robot Collaboration and Smart Production Logistics , with projects addressing real-time adaptive systems and safety frameworks.

Magnus Wiktorssonمشاهده پروفایل

استاد

• Smart Production Logistics
• Sustainability and Circular Economy
• Machine Learning Applications
• +۷ مورد دیگر

Magnus Wiktorsson is a Professor at KTH Royal Institute of Technology's Department of Sustainable Production Development within the Digital Futures Faculty. His work focuses on smart production logistics, sustainability, and digital transformation in manufacturing systems. He explores applications of machine learning, IoT, blockchain, and digital twins to enhance supply chain visibility, optimize production processes, and achieve circular economy goals. His research integrates technical and socio-technical perspectives, addressing challenges in human-robot collaboration, data-driven decision-making, and Industry 4.0/5.0 transitions. Key themes include material efficiency, real-time data utilization, and participatory modeling in urban and industrial logistics. Recent work emphasizes frameworks for adaptive scheduling, explainable AI in logistics, and blockchain-based collaboration models. Wiktorsson collaborates with industry partners to validate frameworks through case studies, pilot projects, and simulation-based methodologies. His contributions span academic publications and contributions to strategic innovation programs like Produktion2030, focusing on sustainability and resilience in manufacturing systems.

Magnus Janssonمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Navigation and Positioning
• +۳ مورد دیگر

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

• Quantum Mechanics
• Statistical Physics
• Black Hole Physics
• +۳ مورد دیگر

Erik Aurell is a Professor at KTH Royal Institute of Technology in the department of CALCULATION KNOWLEDGE & CALCULATIONS SIGNS. He is also a board member of the European Physical Society's Division for Statistical and Nonlinear Physics. His research focuses on statistical physics, quantum mechanics, black hole physics, and biostatistics, particularly in open quantum systems and SARS-CoV-2 genomic analysis. He teaches courses such as Open Quantum Systems (DD2366) and Advanced Individual Course in Computational Biology. Research highlights include editing a memorial volume for physicist Göran Lindblad and studying Hawking radiation, quantum entanglement, and epistasis inference from genomic data. His work bridges quantum theory and biological systems, with recent contributions to understanding viral evolution and quantum gravity thought experiments. Publications span quantum thermodynamics, non-equilibrium entropy, and high-dimensional statistical methods, reflecting his interdisciplinary approach. He actively contributes to both theoretical and applied scientific communities through teaching, research, and professional service roles.

• Machine Learning
• Computational Biology
• BioImage Informatics
• +۳ مورد دیگر

Gisele Barboni Miranda is an Assistant Professor in Machine Learning for Computational Biology at KTH Royal Institute of Technology and a SciLifeLab Fellow. She holds a joint Ph.D. in Computer Science and Applied Mathematics from the University of São Paulo (Brazil) and Applied Biological Sciences from Ghent University (Belgium). Her research focuses on integrating multi-modal data (e.g., microscopy images, omics data) to model cellular behavior and improve disease diagnosis/treatment. She leads projects in the BioImage Informatics Facility and the Imaging Program of the Chan-Zuckerberg Initiative. Research interests include generative AI, deep learning, graph machine learning, and multi-modal profiling for biological systems. She teaches courses such as Statistical Methods in Applied Computer Science and supervises degree projects in Computer Science and Engineering. Scientific contributions span epidemiological modeling (e.g., influenza, SARS-CoV-2), environmental engineering applications of ML, and computational linguistics. Awards include the SciLifeLab Fellowship. Active in collaborative initiatives like the Chan-Zuckerberg Imaging Program.

• Machine Learning
• Computational Biology
• Cancer Genomics
• +۳ مورد دیگر

Golnaz Taheri is an Assistant Professor at KTH Royal Institute of Technology and a Data-Driven Life Science SciLifeLab and Wallenberg (DDLS) Fellow. She holds a Ph.D. in Computer Science and specializes in applying machine learning to biological data, particularly in cancer genomics and personalized medicine. Her work bridges computational methods with life sciences to uncover biological system insights. Previously, she served as an Assistant Professor at Stockholm University's Department of Computer and System Sciences (DSV), where she coordinated large-scale courses and supervised graduate students in computational biology. Her research focuses on developing novel machine learning frameworks for analyzing complex biological datasets, including cancer mutation analysis and drug interaction prediction. Awards: DDLS Fellowship (SciLifeLab/Wallenberg) Key Projects: GenePioneer Python package, drug repurposing for SARS-CoV-2 Teaching: Examinations and course coordination for statistical methods in computer science Her research spans machine learning applications in oncology, virology, and systems biology, with a focus on translational medicine and computational tools for healthcare innovation. Current PhD openings exist in machine learning/deep learning for cancer and drug interaction studies.

• Machine Learning
• Stochastic Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Alexandre Proutiere is a Professor of Communication Networks at KTH Royal Institute of Technology, specializing in stochastic optimization and machine learning. His research focuses on developing mathematical tools to optimize machine learning algorithms and manage dynamic systems, particularly in the context of autonomous systems like robots and self-driving vehicles. He leads a research team advancing techniques for data analysis, network performance verification, and reinforcement learning. His academic contributions span theoretical advancements in reinforcement learning, multi-agent systems, and statistical methods for change point detection. Proutiere's work bridges practical applications in telecommunications and robotics with foundational mathematical rigor. He collaborates on projects involving adaptive control algorithms, distributed optimization, and the design of efficient learning frameworks for complex environments. Key areas of investigation include optimal policy identification in Markov decision processes, low-rank matrix estimation in reinforcement learning, and conformal prediction under Markovian data. His research has implications for improving network efficiency, autonomous system decision-making, and real-time data-driven control mechanisms.

• Nanochemistry
• Energy Storage Materials
• Synthetic Chemistry
• +۵ مورد دیگر

Prof. Kasper Moth-Poulsen is a Full Professor in Applied Chemistry at Chalmers University of Technology. His research focuses on nano-chemistry, energy storage materials, and synthetic chemistry, with applications in renewable energy, sensors, and nanomedicine. He holds a Cand. Scient. (2003) and Ph.D. (2007) from the University of Copenhagen, followed by postdoctoral work there and at UC Berkeley. Since 2011, he has led a research group at Chalmers, developing innovative materials for solar thermal energy storage and photon upconversion systems. Education: Cand. Scient., University of Copenhagen, 2003 Ph.D., University of Copenhagen, 2007 Research Interests: The group designs self-assembling molecular and nanoparticle-based materials for energy storage (e.g., photoswitches for solar thermal systems), photon upconversion, and single-molecule electronics. Key themes include sustainable energy solutions, nanomedicine, and advanced sensors. Publications: Recent work emphasizes molecular solar thermal systems, photon upconversion materials, and energy storage applications. Articles highlight innovations in photoswitch design, heterogeneous catalysts, and hybrid solar devices. Awards/Grants: Not explicitly stated in the text, but extensive project involvement is noted (e.g., molecular solar thermal systems). Labs/Teams: Leads the Moth-Poulsen Research Group at Chalmers, collaborating on interdisciplinary projects in materials chemistry and renewable energy.

Elad Michael Schillerمشاهده پروفایل

دانشیار

• Distributed Computing
• Fault-Tolerant Systems
• Self-Stabilizing Algorithms
• +۴ مورد دیگر

Elad Michael Schiller is an Associate Professor at the Department of Computer Science and Engineering, Chalmers University of Technology, within the Division of Networks and Distributed Systems. His affiliation includes membership in the Distributed Computing and Systems research group. He has held significant roles in organizing conferences, including co-chair positions at ApPLIED 2019/2018 and general chair at PODC 2017. His research focuses on fault-tolerant distributed systems, self-stabilizing algorithms, vehicular networks, and secure communication protocols. Dr. Schiller has led projects such as KARYON (predictable coordination in vehicular systems) and Chameleon-MAC (adaptive media access control for MANETs). He has contributed to testbeds like Gulliver for vehicular systems and Renaissance for SDN control planes. His work emphasizes practical applications of distributed systems in safety-critical domains like autonomous vehicles and intelligent transportation systems (ITS). He has served on technical program committees for events including SSS, ICDCS, and DISC. His research outputs span self-stabilizing consensus algorithms, Byzantine fault tolerance, and vehicular coordination mechanisms. Key themes in his publications include fault recovery, secure distributed messaging, and resilient network architectures.

Romaric Duvignauمشاهده پروفایل

دانشیار

• Distributed Systems
• Network Security
• IoT/CPS Applications
• +۷ مورد دیگر

Romaric Duvignau is an Associate Professor at Chalmers University of Technology since 2024, affiliated with the Department of Computer Science and Engineering within the IT Faculty. His primary research focuses on distributed systems, network security, and cyber-physical systems (CPS), with an emphasis on optimizing communication and computational efficiency in distributed applications. Key projects include the READY project for continuous monitoring systems and contributions to P2P energy sharing optimization via the DEEP and HyperP2P initiatives. He has been at Chalmers since 2017 and also holds teaching responsibilities in computer networks and data-driven systems. Research Interests: Distributed Algorithms & Optimization Cybersecurity & Privacy Peer-to-Peer Energy Sharing Vehicular Networks Edge Computing & Streaming Analytics Notable Articles: His recent works include advancements in P2P energy matching algorithms (2025), Byzantine fault-tolerant consensus (2025), and vehicular data localization (2021). Earlier contributions include impactful studies on vehicular networks (DRIVEN framework, 2020) and network traffic analysis (2019). Teaching & Grants: Responsible for courses like Computer Communication (EDA343) and Data-driven Support for CPS. Active in educational innovation, exploring AI impacts on teaching and active learning tools like Kahoot/Mentimeter. Labs/Teams: Member of the Distributed Computing and Systems group at Chalmers Networks and Systems division, collaborating with researchers like Marina Papatriantafilou and Vincenzo Gulisano on energy systems and distributed algorithms.