About the project
Objective
UrbanEI aims to advance AI-powered Earth Observation (EO) for urban environmental intelligence by mapping and monitoring urban green infrastructure (UGI), assessing urban heat hazards, and detecting illegal waste dumping and burning. The project will integrate satellite EO, environmental, and crowd-sourced data within geospatial foundation models and generative AI frameworks. EO foundation models will be adapted to process large-scale multimodal datasets, enabling detailed UGI mapping, heat-hazard detection, and improved urban heat-risk assessment.
Generative AI will be explored to produce super-resolution imagery for high-resolution UGI mapping and daily land surface temperature estimation, and to generate city-wide air-temperature surfaces from sparse crowd-sourced measurements. In parallel, UrbanEI will pioneer the use of generative AI and open EO data, including Sentinel-2 imagery, for early detection and monitoring of illegal waste dumping and burning.
Expected outcomes include dynamic high-resolution maps of urban green infrastructure, improved city-scale air-temperature and urban heat-island assessments, and earlier detection of illegal waste activities. UrbanEI will advance the state of the art in AI and EO for urban environmental intelligence and provide timely, reliable, and actionable information for climate adaptation, environmental monitoring, and sustainable urban development.
Background
With 2024 marking the warmest year on record and 2026 ranking as the second-warmest year to date, the accelerating impacts of climate change are increasingly evident. Intensifying heatwaves and extreme weather pose major challenges, particularly in rapidly growing cities where heat, environmental degradation, and illegal waste activities threaten public health, infrastructure, and sustainable development. There is an urgent need for timely, accurate, and actionable environmental intelligence to support urban monitoring, mitigation, and adaptation.
Cross-disciplinary collaboration
To be announced
PIs of the project
Yifang Ban, KTH
Josephine Sullivan, KTH
Sebastian Hafner, RISE
About the project
Objective
This project aims to develop a theoretically grounded, data-driven congestion control framework that combines output regulation, online feedback optimization, and reinforcement learning to achieve autonomous and optimal congestion mitigation in power grids, with guaranteed robustness and adaptability to renewable generation uncertainties, without relying on explicit grid models.
Background
The electrical power grid is undergoing a profound transformation driven by digitalization, market deregulation, and the rapid integration of renewable and flexible energy resources. The EU Green Deal and Sweden Climate Policy Framework set ambitious targets for a sustainable and resilient energy system, while addressing energy poverty. Yet, unlike traditional centralized plants, renewables are dispersed and weather-dependent, pushing the grid close to its operational limits. When congestion occurs, overloaded lines threaten stability and clean energy is curtailed. Industry manual and semi-automated congestion management mechanisms cannot cope with this growing complexity. The recent large-scale blackout across the Iberian Peninsula has revealed the fragility of existing operational paradigms and the urgent need for the integration of more efficient autonomous, data-driven control. This raises a fundamental question: How can we design autonomous, data-driven congestion control algorithms that harness the increased digitalization of the power grid to ensure efficient and reliable operation despite the volatility of renewable generation? To address this question, this project aims to develop a robust online reinforcement learning framework, grounded in output regulation theory and online feedback optimization, that learns optimal congestion mitigation strategies directly from real-time power grid data without relying on an explicit grid model.
About the Digital Futures Postdoc Fellow
Liquan Lin received his PhD from The Chinese University of Hong Kong, where his research focused on developing data-driven control methods for uncertain linear systems, with particular emphasis on the output regulation problem. His current research interests lie at the intersection of output regulation theory and feedback optimization, with a focus on data-driven methods and their applications to power systems.
Main supervisor
Giuseppe Belgioiso
Co-supervisor
Qianwen Xu
About the project
Objective
This project will develop efficient representation and learning methods for system dynamics that allow a robot to compensate for the inference delay of large robot action models, e.g., vision-language-action (VLA) models. The robot acquires and refines a predictive modelthrough its interaction with the environment. Through rolling out the learned system dynamics and generating the corresponding observations, the policy can be conditioned on ananticipated observation rather than an outdated one.
Background
VLA models allow robots to accomplish a wide range of tasks and have attracted intense interest in both academia and industry. Their capability comes at the expense of long inference time. By the moment an action or action chunk is computed, the robot state and the world have already evolved, and the robot acts on an observation of the past. Closing this gap requires anticipating the state at execution time and the observation that corresponds to it. For the robot’s own state, the prediction is straightforward. In dynamic scenes where objects roll, slide or deform in respond to robot actions, it becomes considerably harder.
About the Digital Futures Postdoc Fellow
Yunfan Gao completed her Ph.D. in 2026 at the University of Freiburg under the supervision of Prof. Moritz Diehl, and simultaneously, until September 2025 she was an industrial Ph.D.student at Bosch Center for AI, under the industrial supervision of Dr. Niels van Duijkeren. Previously, she obtained her master’s degree in Robotics, Systems and Control from ETH Zurich in 2022 and her bachelor’s degree in Electronic Engineering from Fudan University in 2019.
Main supervisor
Danica Kragic Jensfelt, KTH Royal Institute of Technology.
Co-supervisor
Florian Pokorny, KTH Royal Institute of Technology.
About the project
Objective
This project argues that urban autonomous systems should be understood as publicly sustained rather than self-sufficient. It aims to conceptualise the invisible public work through which this dependency is maintained and to explore how design can make it more legible and socially sustainable.
Background
Urban digitalisation is bringing autonomous technologies into urban public space, from service robots on sidewalks to autonomous mobility services and intelligent urban infrastructure. These systems are often framed and deployed as self-sufficient forms of machine autonomy. Yet in practice, their everyday operation frequently depends on co-present people who interpret their behaviour, adapt their own actions, and at times intervene to resolve issues. This is exemplified by pedestrians altering their paths to grant robots passage and removing obstacles or providing instructions when robots become immobilised, and by drivers adjusting their behaviour to ensure safe interaction with autonomous vehicles. This suggests that what appears as machine autonomy is often sustained through invisible public work.
However, this dependency remains under-recognised in both design and deployment. As a result, the work required from the public is often left to emerge informally, without being made legible or supported through design. This can place unfair burdens on people and create friction in shared spaces, undermining public trust and acceptance of these technologies. To address this gap, the project aims to propose and develop the concept of publicly sustained autonomy through empirical and design research. This concept reframes urban autonomous systems as sociotechnical arrangements whose operation depends not only on technical autonomy, but also on distributed support from the public. Building on this framing, the project investigates how urban autonomous systems can be designed to make the public work that sustains them more legible and socially sustainable, and under what institutional conditions such approaches can be deployed responsibly in public space.
About the Digital Futures Postdoc Fellow
Xinyan Yu is a Design and Human–Computer Interaction researcher. She received her PhD from the Design Lab at the University of Sydney. Her research examines how emerging autonomous technologies become part of everyd
ay public life, with a particular focus on human interactions with service robots and autonomous vehicles in urban environments. Situated at the intersection of design research, human-computer interaction, and human robot interaction, her work investigates how these systems are encountered and experienced in everyday settings, and how design can support their integration in more socially meaningful ways.
Main supervisor
Andrii Matviienko, KTH Royal Institute of Technology.
Co-supervisor
Airi Lampinen, Stockholm University
About the project
Objective
- Develop an accurate and computationally efficient model of power-electronics-dominated microgrids with distributed renewable energy sources.
- Establish electrical-parameter-dependent Lyapunov methods for analysing microgrid stability and robustness under time-varying loads and network conditions.
- Design distributed event-triggered control strategies that enable reliable, communication-efficient, and scalable operation of renewable-energy microgrids.
Background
The transition towards renewable energy is increasing the complexity of modern power systems. Microgrids with high shares of distributed renewable energy sources rely on power-electronic converters, communication networks, and coordinated control, making their modelling, stability analysis, and operation challenging.
This project will develop event-triggered networked control methods for microgrids, combining control theory, power systems, network science, and data-driven methods. The goal is to improve the reliability, resilience, and efficiency of future renewable-energy systems while reducing unnecessary communication and supporting the sustainable digital transformation of energy infrastructure.
About the Digital Futures Postdoc Fellow
Dr. Libei Sun is a postdoctoral researcher at the Department of Electrical Engineering at KTH Royal Institute of Technology. Libei received a PhD in Control Theory and Control Engineering from Chongqing University, China, and was previously a postdoctoral researcher at the University of Hong Kong and a researcher at Nanyang Technological University. Libei’s research focuses on event-triggered control, smart grids, safe control, and robotics.
At KTH, Libei works with Prof. Qianwen Xu and Prof. Dimos Dimarogonas on reliable and communication-efficient control solutions for sustainable power systems.
Main supervisor
Prof. Qianwen Xu, KTH Royal Institute of Technology.
Co-supervisor
Dimos Dimarogonas, KTH Royal Institute of Technology.
About the project
Position in the Digital Futures research matrix
This project sits within the Trust research theme, which addresses the foundations of safe, reliable, and accountable digital systems, and connects to the Smart Society societal context, which looks at how digital technologies shape public life and institutions. By working at this intersection, the project bridges technical AI safety research with the societal and human dimensions of AI misuse.
Objective
The project sets out to understand how generative AI systems can be exploited for socially harmful purposes, through jailbreaks, adversarial prompting, and other unsafe forms of human-AI interaction. Rather than treating this as a narrow cybersecurity issue, the project looks at broader harms such as manipulation, coercive persuasion, misinformation, and intimate surveillance. Its goals are to map emerging misuse scenarios, identify research gaps across AI safety, HCI, and sociotechnical security, build interdisciplinary collaborations, explore ethical approaches to red-teaming, and produce an initial roadmap for future research in this space.
Background
The project responds to a growing body of evidence that safety-aligned AI systems can still be manipulated into producing harmful or restricted outputs, despite existing safeguards. Techniques for bypassing these safeguards continue to evolve and circulate within online communities, often outpacing efforts to detect and prevent them. While most existing AI safety research concentrates on technical robustness and content filtering, far less attention has gone to socially situated harms like coercive control, deceptive influence, and interpersonal abuse enabled by AI. This project aims to help close that gap between technical jailbreak research and human-centred research on digital harm.
Cross-disciplinary collaboration
The project brings together expertise from human-centred cybersecurity, HCI, control systems security, and software supply chain security, combined through a shared program of exploratory research and four interdisciplinary workshops covering AI-enabled interpersonal harm, cybersecurity misuse, misinformation and manipulative communication, and ethical red-teaming methodologies.
PIs
- Asreen Rostami (PI) – Senior Researcher, RISE Research Institutes of Sweden
- Nicolas Harrand (Co-PI) – Senior Lecturer, Stockholm University
- Henrik Sandberg (Co-PI) – Professor, KTH Royal Institute of Technology
About the project
Objective
The objective of this project is to develop an AI-enabled, fully self-powered, and biodegradable wound-healing patch that accelerates tissue regeneration and enables continuous monitoring of post-cardiac-surgery wounds. The system combines triboelectric nanogenerators (TENGs) for bioelectric stimulation with AI-based image analysis to provide personalized, sustainable, and real-time wound care without external power sources.
Background
Post-cardiac-surgery wounds face high risks of infection, delayed healing, and limited continuous monitoring. Existing wound-care technologies rely on external power sources, are costly, and lack portability. Triboelectric nanogenerators (TENGs) offer a promising alternative by harvesting biomechanical energy from natural body movements to deliver gentle bioelectric stimulation.
This project integrates biodegradable hydrogels with antibacterial and anti-inflammatory properties and AI-driven wound image analysis to assess healing stages such as inflammation, proliferation, and remodeling. The approach reduces electronic waste, enables continuous monitoring, and supports faster, safer recovery through sustainable digital healthcare solutions.
About the Digital Futures Postdoc Fellow
Swati Panda is a postdoctoral researcher at the Department of Biomedical Engineering and Health Systems at KTH, specializing in biocompatible and biodegradable energy-harvesting devices for self-powered healthcare applications. She holds a PhD in Robotics and Mechatronics Engineering from DGIST, South Korea. Her research focuses on triboelectric and piezoelectric nanogenerators, biodegradable/biocompatible polymers, and AI-based signal and image processing for healthcare sensing.
She has extensive experience in material synthesis, device fabrication, in-vivo experimentation, and self-powered health monitoring systems. Through her work, she aims to develop sustainable, wearable, and smart healthcare technologies that improve patient outcomes while reducing environmental impact.
Main supervisor
Seraina Dual, Assistant Professor, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology.
Co-supervisor
Erica Zeglio, Assistant Professor, Department of Chemistry, Stockholm University.