جست‌وجوی استاد و پژوهشگر

Martin Jacobssonمشاهده پروفایل

دانشیار

• Machine Learning
• Biomedical Engineering
• IoT and Wearable Technologies
• +۵ مورد دیگر

Martin Jacobsson is an Associate Professor at the School of Computer Science and Communication (CBH) at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He co-founded the Digital Futures research center, which focuses on cross-disciplinary digital technologies addressing societal challenges such as healthcare innovation, IoT systems, and wireless communication. His work emphasizes practical applications with high industrial relevance and strategic importance through collaborations with Stockholm University and RISE Research Institutes of Sweden. His research interests span machine learning in clinical decision support, biomedical data compression, wearable sensor networks, and low-power wireless communication protocols. Notable projects include drone-based motion capture systems for sports analytics, embedded emergency systems, and predictive models for surgical outcomes using ML techniques. Publications highlight contributions to healthcare informatics (e.g., trauma care improvement), IoT device optimization (e.g., battery-free wearables), and wireless network protocols (e.g., LoRaWAN interference analysis). His work bridges theoretical advancements with real-world implementations in healthcare, sports, and industrial IoT. He is actively involved in the Digital Futures community, engaging through LinkedIn, YouTube, and academic conferences. The center is located at Osquars Backe 5 in Stockholm, with contact via info@digitalfutures.kth.se.

Jan Kronqvistمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Convex MINLP
• +۷ مورد دیگر

Jan Kronqvist is an Assistant Professor at KTH Royal Institute of Technology and a faculty member of Digital Futures, a cross-disciplinary research center focused on digital technologies. His research emphasizes optimization methodologies, including convex MINLP, machine learning integration, and algorithm design for complex systems. He actively contributes to projects like SMART (Predictive Maintenance), SimOPT (Process Design), ADAPCOS (Undersea Swarm Optimization), and cybersecurity in power systems. Key research areas include global optimization for clustering problems, robustness in neural networks, GPU-accelerated machine learning, and ensemble model optimization. He has developed algorithms like the Extended Cutting Plane and SHOT solver for convex MINLP, with applications in chemical engineering, power systems, and control systems. His work bridges theoretical optimization and practical industrial challenges, addressing topics such as cyberattack resilience, multimodal mobility systems, and ensemble learning verification. Projects often involve collaboration with industry partners to translate mathematical models into real-world solutions. Jan has advised initiatives like the SEC scholar program for Calvin Tsay and contributed to research on radiation therapy scheduling and distillation process optimization. His research outputs span over 40 peer-reviewed articles, focusing on advancing optimization algorithms and their applications across engineering and data science domains.

• Cyber-Physical Systems Security
• Control Theory
• Adversarial Machine Learning
• +۳ مورد دیگر

Aris Kanellopoulos is a Research Fellow at the Royal Institute of Technology (KTH) in Stockholm, Sweden. His research focuses on cyber-physical systems security, control theory, adversarial machine learning, and game theory applications. He works within the Control Technology domain, addressing challenges such as adversarial attacks, proactive defense mechanisms, and resilient learning algorithms in dynamic environments. His work often intersects with unmanned aerial vehicles (UAVs), multi-agent systems, and cybersecurity protocols. Kanellopoulos has contributed to over 20 peer-reviewed articles since 2019, with recent publications emphasizing real-time defense strategies, moving target defense mechanisms, and the integration of game theory into control systems. He is affiliated with KTH's academic community and can be contacted at arisk@kth.se . His research interests span the development of secure control architectures against worst-case attackers, analysis of clock offsets in learning systems, and verification of learning components in safety-critical applications. Notable areas of exploration include poisoning actuation attacks, quickest detection of adversarial activities, and spatial unpredictability for wireless communication security. His work often involves experimental validation and real-time implementations, particularly in multi-agent scenarios and IoT environments. Kanellopoulos's recent articles highlight advancements in switching control for deception, entropy-based security frameworks, and modular verification approaches. His contributions address both theoretical and practical aspects of cyber-physical security, with applications in water supply networks, autonomous vehicles, and unmanned aerial systems. His research aligns with KTH's focus on sustainable technology and international collaborations, such as projects involving Taiwanese institutions.

Rodrigo Morenoمشاهده پروفایل

دانشیار

• Medical Imaging
• Biomechanics
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Rodrigo Moreno is an Associate Professor at the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH) at KTH Royal Institute of Technology, and a Co-Principal Investigator (Co-PI) in two major research projects: 'Characterization of the mechanical tissue properties of the brain in the developing brain with magnetic resonance elastography' and 'Advanced Magnetic Resonance Elastography for the Brain.' He is also affiliated with Digital Futures, a cross-disciplinary research center focused on developing digital technologies to address societal challenges. His primary research interests include biomedical imaging techniques, biomechanical properties of tissues, and the application of artificial intelligence (AI) in healthcare. Dr. Moreno’s work spans neurodegenerative diseases like Alzheimer’s and Parkinson’s, as well as advancements in medical imaging modalities such as MRI, CT, and ultrasound. His recent projects emphasize precision medicine using AI-powered longitudinal MRI analysis and the mechanical characterization of brain tissue in developmental and disease contexts. He leads collaborations in computational methods like tensor voting and deep learning for medical image processing, segmentation, and generative modeling of biological structures. His contributions include developing algorithms for tractography filtering, synthetic data generation, and robust segmentation of medical images. Dr. Moreno’s research has produced over 50 peer-reviewed articles since 2014, with a focus on advancing imaging technologies and their clinical applications. His work bridges computational engineering, biomedical research, and digital innovation, aiming to improve diagnostics and personalized treatment strategies for complex diseases.

Daniel Tesfamariam Semereمشاهده پروفایل

دانشیار

• Manufacturing Systems
• Machining Processes
• Quality Prediction in Production
• +۳ مورد دیگر

Daniel Tesfamariam Semere is an Associate Professor at KTH Royal Institute of Technology, affiliated with the School of Industrial Engineering and Management (ITM) and the Department of Production Engineering under the Digital Futures Faculty. His role includes teaching and coordinating courses in industrial production systems, such as Digital Factories and Modeling and Simulation of Industrial Processes. He is actively involved in academic administration as an examiner and course manager for several advanced-level programs. His research focuses on manufacturing systems, machining processes, and quality prediction, with notable contributions to tool wear analysis, residual stress prediction, and variation propagation modeling. His work integrates advanced methodologies like dual quaternions, machine learning, and optimization algorithms to address challenges in precision manufacturing and supply chain logistics. Dr. Semere’s recent publications emphasize data-driven approaches to improve manufacturing accuracy, reduce distortions, and optimize warehouse systems. His expertise bridges theoretical models with practical applications, contributing to sustainable and efficient industrial solutions.

Rolf Stadlerمشاهده پروفایل

استاد

• Network Management
• Cloud Analytics
• Resource Management
• +۳ مورد دیگر

Rolf Stadler is a Professor and Head of the Division of Network and Systems Engineering at KTH Royal Institute of Technology in Stockholm. He holds affiliations with Digital Futures Faculty and is Co-PI of the DEMOCRITUS project on decision-making in critical infrastructures. His research focuses on network and system management, including real-time monitoring, resource allocation, and data-driven engineering. Education: M.S. in Mathematics, University of Zurich Ph.D. in Computer Science, University of Zurich His research interests span advanced monitoring techniques, network analytics, and cybersecurity. Notable collaborations include Ericsson Research, Cisco Systems, and KDDI Labs. He leads the Laboratory for Communication Networks and co-founded the ACCESS Linneus Center. Dr. Stadler serves as Editor-in-Chief of IEEE Transactions on Network and Service Management. Research Trends: Recent work emphasizes adaptive security policies, online learning for intrusion detection, and scalable frameworks for dynamic network environments. His publications highlight innovations in game-theoretic security, distributed resource management, and real-time performance prediction. Awards: Applied Networking Research Prize (2014) Dan Stokesberry Award (2015) Teaching & Grants: Acts as examiner and course responsible for multiple graduate-level programs in computer science and electrical engineering. His grants include funding for projects like DEMOCRITUS and service mesh optimization initiatives. Labs & Teams: Active in the Laboratory for Communication Networks and the interdisciplinary ACCESS Linneus Center, focusing on complex network systems.

Josephine Sullivanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

Xuyang Wuمشاهده پروفایل

دانشیار

• Distributed Optimization
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Xuyang Wu is an Associate Professor at the School of Automation and Intelligent Manufacturing (AiM), Southern University of Science and Technology (SUSTech), China. Previously, he served as an Assistant Professor at SUSTech and held a postdoctoral position at KTH Royal Institute of Technology in Sweden under Professors Mikael Johansson and Sindri Magnússon. He completed his PhD in Communication and Information Systems at the University of Chinese Academy of Sciences (2015–2020) and a B.S. in Applied Mathematics from Northwestern Polytechnical University (2011–2015). His research focuses on distributed optimization, federated learning, and large-scale machine learning algorithms, emphasizing theoretical guarantees and practical implementations. Notable projects include safe resource allocation in power networks and asynchronous algorithms for distributed systems. He has published extensively in top-tier journals like IEEE Transactions on Automatic Control and conferences such as ICML and ICLR. Dr. Wu has received recognition including a best student paper finalist award at IEEE ICCA 2019. His work bridges theoretical advancements with real-world applications in smart networks and cyber-physical systems.

Jiaojiao Zhangمشاهده پروفایل

پژوهشگر ارشد

• Distributed Optimization
• Decentralized Machine Learning
• Algorithm Design
• +۴ مورد دیگر

Jiaojiao Zhang is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology, focusing on decentralized machine learning algorithms with convergence guarantees. Her research addresses second-order, online, and robust methods for distributed optimization, emphasizing applications in privacy-preserving technologies and smart systems. Education: B.E. in Automation (Harbin Engineering University, 2015), M.Sc. in Control Theory (University of Science and Technology of China, 2018), Ph.D. in Operations Research (CUHK, 2022). Award: Hong Kong PhD Fellowship Scheme (2018). Supervisors: Mikael Johansson (KTH EECS) and Joakim Jaldén (KTH). Her work bridges distributed optimization and algorithm design, targeting challenges in federated learning, wireless sensor networks, and multi-core processor training. Projects like 'Decentralized Learning' aim to enhance cooperation and trust in digital futures through robust, efficient algorithms. Key areas: privacy-preserving federated learning, Byzantine-robust systems, and dynamic privacy allocation. Publications emphasize convergence analysis, communication efficiency, and adversarial resilience in decentralized settings. She actively contributes to Digital Futures, a joint initiative of KTH, Stockholm University, and RISE.

• Medical Image Analysis
• Artificial Intelligence
• Planetary Science
• +۳ مورد دیگر

Christos Matsoukas is a Researcher and Industry doctoral student at the Division of Computational Science and Technology, KTH Royal Institute of Technology. His work bridges Artificial Intelligence and Planetary Science, focusing on medical image analysis via advanced machine learning techniques and exploring Titan's surface composition using remote sensing data from missions like Cassini. His research interests span Medical Image Analysis, leveraging transformer models and self-supervised learning for diagnostic applications, as well as Planetary Science analyzing Titan’s chemical composition through spectral and morphological analysis of craters and geological features. Recent studies investigate the efficacy of foundation models in low-data medical contexts and domain adaptation strategies for high-content imaging. Publications highlight innovations like Random Token Fusion for multi-view diagnosis, Metadata-guided consistency learning, and compositional mapping of Titan’s surface using Cassini/VIMS and RADAR data. His work contributes to both AI-driven healthcare solutions and understanding Titan’s habitability potential.

Nicolas Pielawskiمشاهده پروفایل

پژوهشگر ارشد

• AI
• Healthcare
• Statistics
• +۲ مورد دیگر

Nicolas Pielawski is a Research Fellow at Uppsala University's Department of Information Technology, Division of Systems and Control, where he develops AI solutions for emergency medicine. His work bridges Bayesian statistics, information theory, and healthcare applications to enhance decision-making systems. Research interests focus on AI implementation in clinical settings, uncertainty quantification, trustworthy AI systems, and mathematical modeling of complex problems. Key areas include Bayesian inference, neural network interpretability, and multimodal learning for biomedical data. Publications demonstrate consistent focus on deep learning innovations, particularly in medical image processing and natural language model optimization. Recent work emphasizes computational efficiency, interpretability, and applications in spatial omics and emergency healthcare contexts.

Philip Harrisonمشاهده پروفایل

پژوهشگر

• Machine Learning
• Pharmaceutical Science
• Biodiversity Analysis
• +۳ مورد دیگر

Philip Harrison is a Visiting Researcher at the Department of Pharmaceutical Biosciences, Uppsala University. He holds a PhD in marine biology (2006, focusing on grey seal population dynamics) and is pursuing a second PhD in pharmaceutical science, developing machine learning methods for microscopy image analysis within the HASTE project. His research spans biodiversity modeling, computational biology, and pharmaceutical applications of AI. Education : PhD in Marine Biology, 2006 (Topic: Population Dynamics of Grey Seals) Current PhD Candidate in Pharmaceutical Science (Focus: Machine Learning in Microscopy Analysis) Research Interests : Harrison’s work bridges ecology and computational bioscience. Early research modeled wildlife populations and biodiversity trends. Current projects leverage deep learning for drug discovery, cellular imaging analysis, and lipid nanoparticle drug delivery. He collaborates on the HASTE project, advancing AI-driven scientific pipelines. Publications : Recent work includes peer-reviewed articles on mechanism-of-action prediction via image data fusion, Boolean protein interaction methods, and deep learning in cytometry. His publications emphasize interdisciplinary approaches, combining AI with pharmaceutical and ecological challenges. Advising/Grants : No formal student advising or grant details provided in sources. His research is embedded in collaborative projects like HASTE, focusing on cloud-native data pipelines and AI applications. Labs/Teams : Part of the Pharmaceutical Bioinformatics group at Uppsala, contributing to cross-disciplinary teams in computational bioscience and drug discovery.

• Nonlinear Optimization
• Stochastic Optimization
• Operations Research
• +۵ مورد دیگر

Michael Patriksson is a Professor of Applied Mathematics at Chalmers University of Technology, specializing in nonlinear optimization, stochastic programming, and maintenance scheduling. His research integrates theoretical advancements with applications in industrial engineering, energy systems, and transportation. He has contributed to over 160 publications and led 10 major projects, focusing on topics such as wind turbine maintenance, supply chain optimization, and blockchain-based transportation systems. Key research interests include combinatorial optimization, variational inequalities, and algorithm design for real-world problems. Notable applied work spans predictive maintenance strategies, traffic equilibrium modeling, and resource allocation in aerospace manufacturing. His methods often address stochastic uncertainties and operational constraints, with practical implementations in industries like renewable energy and logistics. Publications highlight contributions to maintenance optimization frameworks, hybrid machine-learning algorithms, and strategic traffic management. Collaborations with institutions like CIGRE and IEEE underscore his interdisciplinary impact. Current projects explore opportunistic maintenance models, blockchain applications in mobility, and sustainable infrastructure planning.

Holger Wallbaumمشاهده پروفایل

استاد

• Sustainable Building
• Life Cycle Assessment
• Building Stock Modeling
• +۵ مورد دیگر

Holger Wallbaum is a Full Professor in Sustainable Building at Chalmers University of Technology’s Department of Building Technology, serving as Vice-Head of Department and Vice-Dean for Research in the School of Architecture and Civil Engineering. His work focuses on enhancing sustainability in construction materials, buildings, and cities through lifecycle assessments, tools, and strategies. Education: PhD in Architecture, University of Hanover, Germany (2002) Diploma in Safety Engineering (Environmental Engineering), Bergische University Wuppertal, Germany (1998) Maîtrise Gestion de Risques, Université de Mulhouse, France (1995) Research Interests: Wallbaum’s research spans ecological and economic lifecycle assessments of construction materials and infrastructure, sustainability assessment tools, climate-adapted design, building stock modeling, and the refurbishment of urban housing. His work bridges technical, social, and environmental dimensions of sustainability, with a focus on occupant well-being, circular economy principles, and policy implementation. Article Trends: His recent publications emphasize digital tools for urban sustainability (e.g., digital twins, machine learning), lifecycle management of infrastructure, and occupant-centric design. Key themes include circular economy frameworks, material flow analysis, and bridging assessment-action gaps in sustainability practices. Advising & Grants: Advised PhD/Master’s students including Sanjay Somanath, Xinyue, and Toivo. Involved in projects like CREATE (energy/climate strategies), SSO User Insight Toolbox, and PROCEEDR (road infrastructure sustainability). Labs/Teams: Active in the Sustainable Building research group and the Area of Advance: Building Futures at Chalmers, collaborating on initiatives like the Building Inventory and Refurbishment Scenario Database.

• CO2 Capture
• Chemical Engineering
• Energy Storage
• +۳ مورد دیگر

Henrik Leion is an Assistant Professor in combustion chemistry at the Department of Chemistry and Chemical Engineering, Chalmers University of Technology. He serves as the Head of Unit for Energy and Materials and directs the master's program in Innovative and Sustainable Chemical Engineering. His research focuses on developing oxygen-carrier materials for CO₂ capture via Chemical-Looping Combustion (CLC), combustion chemistry, energy storage, and biomass gasification. The combustion laboratory under his leadership features world-class fluidized bed equipment. Key topics include oxygen carrier interactions with alkali compounds, biomass ash, and sulfur, alongside studies on energy storage materials like manganese oxides. Recent work emphasizes sustainable waste-to-energy systems and the application of machine learning for process optimization. Research interests are anchored in experimental development of metal oxide-based oxygen carriers, with applications in CLC, energy storage, and gasification. The majority of experimental work involves fluidized bed systems, exploring material stability, reaction kinetics, and scalability. Collaborative projects include evaluating industrial residues (e.g., steel converter slag) as low-cost oxygen carriers and studying phosphorus release mechanisms during biomass thermal conversion. Publications highlight advancements in oxygen carrier design, material durability under extreme conditions, and system optimization for negative-emission energy plants. His lab’s cutting-edge equipment enables detailed analysis of particle behavior and reactor dynamics, contributing to both fundamental and applied research in sustainable energy technologies. Grants and advising details are not explicitly stated in the provided texts, but his program director role indicates involvement in educational initiatives. The combustion laboratory serves as a hub for collaborative research with industry and academia, focusing on real-world applications of chemical looping technologies.