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Adam DarwichView profile

Associate Professor

• Health Systems Engineering
• Healthcare Modeling and Simulation
• Pharmacokinetics
• +3 more

Adam Darwich is an Associate Professor in the Division of Health Informatics and Logistics at KTH Royal Institute of Technology. He is a Co-Principal Investigator (Co-PI) in the Data-driven Improvement of Work-Flows project at Karolinska University Hospital under the Digital Futures research center. His research focuses on modeling and simulation methods for healthcare decision-making, evidence generation for medical technologies, and real-world evidence (RWE) applications. He holds a PhD in Pharmaceutical Sciences and is a Licensed Pharmacist with expertise in pharmacokinetics and systems pharmacology. Research Interests: Dr. Darwich’s work integrates healthcare logistics, physiologically-based pharmacokinetic (PBPK) modeling, real-world data analysis, and health technology assessment. He leads projects on patient flow optimization, hospital overcrowding solutions, and precision dosing strategies. His contributions include developing frameworks for RWE generation and advancing methodologies for drug metabolism studies. Publications: His recent work emphasizes hybrid modeling approaches, machine learning applications in healthcare, and the use of real-world data to address discrepancies in clinical trial outcomes. Key topics include small cell lung cancer prognosis, biosimilar dosing optimization, and the impact of chronic kidney disease on drug clearance. Grants & Advising: As a Co-PI, he contributes to interdisciplinary grants focused on digital health innovation. He teaches courses on healthcare logistics, medical device design, and simulation methods in medical engineering at KTH. Labs & Teams: He is affiliated with the Center for Data Driven Health (CDDH) and collaborates with Digital Futures, a tripartite initiative with Stockholm University and RISE Research Institutes.

Abbas DashtimaneshView profile

Assistant Professor

• Ship Hydromechanics
• Green Ship Design
• Digital Twins in Marine Engineering
• +3 more

Abbas Dashtimanesh is an Assistant Professor at KTH Royal Institute of Technology's Naval Architecture Center within the School of Engineering Sciences. He specializes in ship hydromechanics, green ship design, and digitalization strategies for decarbonization. His research focuses on applying digital twins, generative AI, and CFD to optimize hull forms and reduce emissions. He co-leads the SHIFT-DT project, developing sustainable ship design frameworks using digital twins, and contributes to the DeWaTra initiative addressing hydrodynamic hull design for energy efficiency. He teaches courses like Marine Hydromechanics and Advanced Topics in Naval Architecture, advising master's and PhD students. His work bridges academia and industry through collaborations with DNV, Wallenius Marine, and RISE, advancing Sweden's maritime innovation landscape. Affiliations: Naval Architecture Center, Digital Futures Faculty (KTH-Stockholm University-RISE partnership) Key Projects: SHIFT-DT, Decarbonising Waterborne Transportation (DeWaTra), AI/ML in Ship Hydrodynamics Research Interests: His work emphasizes reducing CO2 emissions via innovative hull designs and digital twin applications. He explores machine learning for hull optimization and integrates AI with traditional CFD methods. Current projects include wind-assisted propulsion systems and retrofitting solutions for existing vessels. Grants & Partnerships: Active collaborations with DNV, Alfa Laval, Wallenius Wilhelmsen, and Swedish Transport Agency ensure industry relevance. His projects aim to create open-source DT frameworks and validate designs using real-world models like Oceanbird. Labs/Teams: Leads the Naval Architecture Center’s digitalization initiatives and contributes to KTH-RISE partnerships. His team works on virtual testbeds for zero-emission ship designs.

Erik Flores-GarcíaView profile

Assistant Professor

• Data-Driven Production Logistics
• Machine Learning
• Digital Twin Technology
• +3 more

Erik Flores-García is an Assistant Professor at the Digital Futures Faculty of KTH Royal Institute of Technology , collaborating with Stockholm University and RISE Research Institutes of Sweden. His research focuses on data-driven production logistics, machine learning applications in industrial systems, and digital twin technologies to enhance manufacturing efficiency and sustainability. Located at Osquars Backe 5 in Stockholm, his work bridges academic research with industrial challenges. His research interests include optimizing logistics systems through AI, integrating human-centric vision systems, and developing frameworks for smart production environments. Key themes include energy efficiency, explainable AI, and socio-technical implications of automation in biopharmaceutical and automotive industries. Recent publications highlight advancements in deep reinforcement learning for warehouse optimization, digital twin-based maintenance planning, and frameworks for industrial IoT integration. His work emphasizes both technological innovation and societal impact, addressing challenges like real-time data transmission and ethical considerations in automation. No scientific awards are listed, but his contributions reflect a focus on industry-academia collaboration through co-production models and problem-based education initiatives. His research also explores simulation-based methodologies for facility layout design and decision-making in production system innovations.

Gyözö GidofalviView profile

Associate Professor

• Smart Cities
• Environmental Modeling
• Sustainable Electromobility
• +3 more

Gyözö Gidofalvi is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Digital Futures research center. He serves as Principal Investigator (PI) for the EFFECT project and Co-PI for GEOMETRIC and Learning in Routing Games for Sustainable Electromobility. His work focuses on digital innovations addressing societal challenges, such as sustainable construction, urban environmental modeling, and electromobility. Key Projects: ChEss Machines For ElectriFiEd Construction SiTes (EFFECT) : Aims to advance electrified construction technologies. SENZ-Lab : Develops environmental zone digital laboratories for Stockholm. GEOMETRIC : GEO-based multi-layer environmental modeling for urban traffic. Research Interests: Digital Futures integrates AI, data science, and engineering to solve global challenges. Gidofalvi’s projects emphasize environmental sustainability, smart infrastructure, and intelligent transportation systems. His work bridges academia and industry through collaborative initiatives like Digital Futures, established with Stockholm University and RISE Research Institutes. Affiliations: Digital Futures Faculty SENZ-Lab Demonstrator Lead Contact: +46 8 790 70 21 | gyozo@kth.se

Martin JacobssonView profile

Associate Professor

• Machine Learning
• Biomedical Engineering
• IoT and Wearable Technologies
• +5 more

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 KronqvistView profile

Assistant Professor

• Optimization
• Machine Learning
• Convex MINLP
• +7 more

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
• +3 more

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 MorenoView profile

Associate Professor

• Medical Imaging
• Biomechanics
• Artificial Intelligence in Healthcare
• +4 more

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 SemereView profile

Associate Professor

• Manufacturing Systems
• Machining Processes
• Quality Prediction in Production
• +3 more

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 StadlerView profile

Professor

• Network Management
• Cloud Analytics
• Resource Management
• +3 more

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 SullivanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

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 WuView profile

Associate Professor

• Distributed Optimization
• Machine Learning
• Federated Learning
• +3 more

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 ZhangView profile

Research Fellow

• Distributed Optimization
• Decentralized Machine Learning
• Algorithm Design
• +4 more

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
• +3 more

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 PielawskiView profile

Research Fellow

• AI
• Healthcare
• Statistics
• +2 more

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.