About the project
About the Digital Futures Postdoc Fellow
Mareike Glöss main research interest is understanding digital transformations and their diffusion into everyday life. She looks at how novel computing technologies are appropriated and how this impacts everyday life. An important part of her work is translating results to inform the design and development of new technologies. Mareike is especially interested in public spaces and how people move through them. She has studied auto-mobility aspects – on the road with Swedish commuters or as a passenger in Californian cabs and Uber rides.
But there is a big chance that auto-mobility will only be a small part of future mobility (at most). Thus, more recently, she started to think about new approaches to mobility away from single modes of transportation. According to Mareike Glöss, we must start thinking much more about personal journeys and intermodal forms of transport. Closely related to this is her interest in Smart Cities. Those are still treated very much like a future vision, but cities are already very smart. Just in a much more chaotic form than we had imagined. And this chaos is something she would like to untangle.
Main supervisor
Rob Comber, Associate Professor, Division of Media Technology and Interaction Designs, KTH.
Co-supervisor
Jonathan Metzger, Professor at the School of Architecture and the Built Environment, Urban Planning and Environment, KTH.
About the project
Objective
The research goal of this proposal is to develop a novel hierarchical control framework that enables multi-vehicle systems (MVS) to distribute, manage, and execute complex tasks in possibly dynamic and unstructured environments with safety guarantees and computational efficiency. For specified low-level navigation tasks, a unified theory will be developed devoted to distributed estimation and formation tracking control problems endowed with reactive collision avoidance abilities based on onboard local sensors (e.g., cameras or range finders). Decision-making mechanisms will be integrated to coordinate high-level navigation tasks to provide MVS with the capability to cooperatively specify the overall task and competitively allocate sub-tasks for each vehicle while ensuring efficient time and energy consumption.
The proposed framework will also enable a set (possibly the whole group) of agents to instantaneously choose and execute between different tasks reacting to real-time environmental changes in a prescribed mission. The innovative framework and algorithms will be developed with rigorous mathematical analysis and realistic simulations, potentially including engineered implementations that address practical urban challenges like search and rescue and intelligent transportation using aerial and sidewalk robots.
Background
Recent decades have witnessed the rapid expansion of autonomous robotic vehicles, such as self-driving cars, drones, autonomous marine vehicles, etc. They are envisioned as essential tools to venture into unsafe areas, enhance human sensory and manipulation abilities, or explore unstructured and possibly dangerous environments. Since a single autonomous agent typically makes it impossible to perform tasks in vast areas or complicated tasks that have to be decomposed into sub-tasks performed by multiple agents, both industry and academia have shown a great interest in the scientific area of networked autonomous vehicle systems, which are systems that can interact and coordinate with each other.
Although academic work on controlled multi-vehicle systems (MVS) has become ever-expanding recently, a large gap exists between current capabilities and required ones in real-world scenarios. For MVS to perform a wide range of tasks, autonomous navigation and control is a fundamental ability under which each vehicle should interact safely with neighbour vehicles and the surrounding environment. However, most existing control algorithms rely on full-state measurements, limiting their applicability to specific applications with suitably equipped experimental areas.
GPS signal is typically unreliable for practical scenarios involving tasks in urban canyons or congested environments. Hence, robust and computationally efficient distributed controllers and estimation algorithms must be designed based on onboard exteroceptive sensors (such as laser range finders, vision, and acoustic sensors). However, the documented results for robotic vehicles using onboard sensors have been limited to ad hoc scenarios. Moreover, the current practice of MVS is often conducting simple missions with restricted autonomy based on offline and centralized supervision and planning, assuming the environment is static and known, such as lighting shows via drones and delivering in warehouses via mobile robots.
About the Digital Futures Postdoc Fellow
Zhiqi Tang is a Digital Futures Postdoctoral Fellow at the Division of Decision and Control Systems of KTH Royal Institute of Technology, Sweden. From 2021 to 2022, she was a postdoctoral researcher with the Institute for Systems and Robotics. She was an Invited Assistant Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico (IST) in Portugal.
She earned a double PhD in Automatic Control and Robotics from IST, University of Lisbon, Portugal, and I3S-CNRS, Université Côte d’Azur, France, in 2021. She obtained her B.S. in Electrical and Computer Engineering from the University of Macau, Macau SAR, China, in 2015. Her research interests focus on the estimation, control, and decision-making in multi-agent systems, with applications in Robotics and Transportation Systems.
Main supervisor
Jonas Mårtensson, Associate Professor, Division of Decision and Control Systems at KTH.
Co-supervisor
Karl H. Johansson, Professor, Division of Decision and Control Systems at KTH.
Michele Simoni, Assistant Professor, Transport Systems Analysis at KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This research (3Dfire) aims to enhance forest fire detection and characterization through advanced remote sensing and AI techniques, facilitating improved management and mitigation strategies within forest ecoregions. Our AI algorithms will streamline the processing of extensive freely available remote sensing data, like Sentinel-1 and -2, to drastically reduce the time and costs associated with fire monitoring and prediction. Consequently, it will provide a comprehensive understanding of fire dynamics within the region. Additionally, “3Dfire” will identify and analyze the risk factors and drivers of fires in the Miombo woodlands, including the impact of extreme climate events and anthropogenic factors on fire severity, frequency, and duration.
The project’s outcomes will offer valuable insights to researchers and policymakers responsible for forest fire prevention, minimizing societal and ecological impacts, and promoting sustainable forest management practices. Ultimately, “3DFire” is pivotal in establishing a comprehensive global-scale perspective on AI for forest applications and transitioning towards climate-smart forest management.
Background
Miombo woodlands cover 270 million ha across southern Africa and are increasingly threatened by natural and anthropogenic forces. Despite their importance for biodiversity and 100 million forest-dependent people, this ecoregion receives little attention from the scientific community. The annual forest loss in just a part of the Miombo ecoregion was recently estimated to be more than half a million hectares. Fires are a primary cause of vegetation loss in Miombo, occurring approximately 50% more frequently than in other global ecoregions. They also trigger massive CO2 emissions in sub-Saharan Africa and threaten many ongoing sustainable forest management projects, such as Reducing Emissions from Deforestation and Degradation (REDD). The lack of accurate fire records challenges the estimation of CO2 emissions and evaluation of fire management activities in forests.
Although some dimensions of Miombo’s wildfires, such as frequency and severity, have been explored through coarse- and medium-resolution satellite time-series data from MODIS and Landsat, knowledge of the exact spatial extent, duration, and timing of the fires and their relations with effective driving forces is still lacking. However, fire regimes in miombo are dominated by small burn patches, particularly in the drier regions. Their limited spatial resolution makes it difficult to detect and characterize these small fires using the aforementioned satellites. Extreme climate events significantly elevate the probability of forest fires, while anthropogenic drivers affect all dimensions of fires. Meanwhile, extreme climate events and human pressures on forests are expected to increase as global temperatures rise and the population of southern Africa doubles by 2050. To address these limitations, this research will focus on characterizing and mapping various dimensions of fires, including the severity, frequency, timing, and duration of forest fires in miombo. We will leverage the time series data of fires derived from the Sentinel data and employ deep learning-based algorithms, particularly our developed residual attention UNet5 (RAUNet5).
About the Digital Futures Postdoc Fellow
Zeinab Shirvani is a postdoctoral researcher affiliated with the Division of Geoinformatics at KTH. She obtained a PhD in Remote Sensing/Cartography focusing on forest disturbances from TUD, Germany, in 2020. Before her current position, Zeinab was a postdoctoral researcher at the Swedish University of Agricultural Sciences (SLU) for two years. Zeinab has made significant contributions to remote sensing and machine learning throughout her research career, particularly in mapping woodland fires in tropical dry forests using RAUNet. Her expertise lies in applying geospatial artificial intelligence and remote sensing techniques to study forest disturbances.
Main supervisor
Yifang Ban, Professor and Director of the Division of Geoinformatics at the Department of Urban Planning and Environment at KTH.
Co-supervisor
Ulla Mörtberg, Professor and associate professor (docent), Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This project aims to solve theoretical and practical challenges in distributed optimization and learning in smart networked systems. We wish to design fast and practical algorithms that have theoretical convergence guarantees. We are focusing on two concrete topics: 1) safe resource allocation in power networks to avoid systems breakdown and 2) efficient asynchronous parallel and distributed optimization (better step sizes and delay-tolerant algorithm design).
Background
Networked systems such as power networks and IoT systems are important in our life. To make these systems “smart” (e.g., saving cost or improving utility), we need to learn models from data, equivalent to solving optimization problems. Consequently, designing efficient algorithms to solve optimization problems in these systems is of strong practical significance. Moreover, theoretical convergence analysis is also dispensable to these algorithms to guarantee their reliability.
About the Digital Futures Postdoc Fellow
Xuyang Wu is a Postdoctoral researcher at KTH Digital Futures, co-supervised by Prof. Mikael Johansson at KTH (DCS, EECS) and Prof. Sindri Magnússon at SU (DSV). He received a B.S. degree in Applied Mathematics from Northwestern Polytechnical University, China, in 2015 and a PhD in Communication and Information Systems at the University of Chinese Academy of Sciences, China, in 2020. He was a finalist for the best student paper award at IEEE ICCA 2019. His current research interests include distributed optimization and federated learning. In particular, he focuses on algorithmic foundations, convergence analysis, and resource efficiency in emerging systems such as IoT and cyber-physical systems. More information can be found on his homepage: http://xuyangwu.github.io/
Main supervisor
Mikael Johansson, Professor, Division of Decision and Control Systems, School of EECS, KTH.
Co-supervisor
Sindri Magnússon, Associate Professor, Department of Computer and Systems Science, Stockholm University.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
The project aims to fill urban population data gaps in developing countries by harnessing the power of Earth Observation (EO) data and AI. An innovative framework will fuse high-resolution satellite information with ancillary sources, such as Volunteer Geographic Information data and machine learning. The long-term goal of POPAI is to understand better the synergy and potential of AI and EO towards scalable population mapping, help address the United Nations Sustainable Development Goals, support evidence-based policymaking and foster a better future for the cities of tomorrow.
Background
Accurate urban population distribution information is necessary prerequisites for a wide range of applications related to urban sustainability. The quality and quantity of population data in numerous countries are often inadequate due to the absence of detailed censuses or large temporal gaps between them. The disparaging effects of this lack of information are most evident in Sub-Saharan Africa (SSA) and the Global South. As estimated by the UN, more than 60% of the African population will reside in cities by 2050, which further emphasizes the need for accurate population information.
About the Digital Futures Postdoc Fellow
Stefanos Georganos is a research fellow at the Division of Geoinformatics, Royal Institute of Technology. He does research in quantitative human geography, remote sensing, spatial epidemiology and machine learning. He is particularly interested in the use of geo-information to help address the UN Sustainable Development Goals, with a geographical interest in sub-Saharan African cities. His latest research unravels the potential of Artificial Intelligence and Earth Observation to detect, measure and characterize socio-economic inequalities in deprived urban areas in support of the most vulnerable populations.
Main supervisor
Yifang Ban, Professor and Head of Division Geoinformatics at KTH.
Co-supervisor
Anders Wästfeldt, Professor at Stockholm University.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
The project’s main goal is to analyze the sociotechnical challenges that arise from the design and use of data-intensive methods for advocacy purposes. The project will advance a core understanding of how data-driven techniques support or hinder community engagement and political organizing in disabled communities. It contributes to long-standing concerns of promoting data literacy and making technologies accessible to historically marginalized populations. In addition to an empirical understanding of the local needs of disabled communities, this work also advances technical knowledge of designing for and with people with disabilities as applied to data science tools.
Background
The current data ecosystem consists of techniques for collecting and analyzing data influencing wide-ranging aspects of our social lives, from healthcare decisions to housing allocations. In these data systems, people with disability are often further marginalized in the increasingly datafied society. In recent years, marginalized communities have been leveraging the same data-intensive methods to combat the adverse effects of big data systems, advocating for under-recognized issues and driving social change. This project examines what happens when traditional data-intensive methods are employed for advocacy efforts, such as when activists mobilize statistics to shed light on issues or create data visualizations to engage big audiences. The goal is to interrogate the limits and opportunities of data science as we know it when applied to disabled communities. I aim to understand data needs specific to these communities and to design and deploy a suite of tools to support their efforts.
About the Digital Futures Postdoc Fellow
Stacy Hsueh is a postdoc in the Interaction Design team at KTH Royal Institute of Technology. She’s interested in exploring how crip wisdom offers critical frameworks for interrogating inequity in data-driven technologies and imagining more just alternatives.
Main supervisor
Marianela Ciolfi Felice, Assistant Professor in Interaction Design at KTH.
Co-supervisor
Karin Hansson, Researcher at Stockholm University.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
This project aims to develop efficient Deep Neural Network (DNN)-based approaches for solving inverse problems in Structural Health Monitoring (SHM) by integrating physical principles into data-driven models. By embedding physics into the otherwise black-box neural network framework, the approach seeks to enhance both the accuracy and credibility of predictions while improving interpretability.
The study will focus on two key strategies: utilizing high-fidelity Finite Element (FE) simulations to establish correlations between realistic damage parameters and observable, damage-sensitive structural responses, and incorporating system physics to ensure practical and physically consistent outputs. By leveraging these techniques, the project aims to formulate a robust framework for near real-time monitoring and management of large-scale bridge networks, enabling proactive maintenance and improved infrastructure resilience.
Background
Aging and heavily loaded civil infrastructure, particularly bridges, demand proactive maintenance strategies to ensure structural safety and longevity. Catastrophic failures, such as the Genoa bridge collapse in 2018, emphasize the urgency of effective monitoring systems. These challenges are further pronounced by corrosive pollutants and climate change-induced extreme weather conditions, accelerating structural deterioration.
The American Society of Civil Engineers (ASCE) Report Card reveals that the average age of bridges in the U.S. is 57 years, with 7.5% classified as structurally deficient. Similarly, the Trans-EU Transport Highway Network (TEN-T) faces mounting concerns, as numerous highway bridges in countries like France, the UK, and Germany exhibit significant structural vulnerabilities. The financial burden of maintenance, repair, and potential reconstruction, coupled with the risk of cascading failures, makes systematic bridge monitoring an urgent necessity .
Despite decades of research and substantial investments—such as the EU’s funding of FP7 and H2020 projects—industrial adoption of SHM remains limited. To effectively mitigate risks and optimize infrastructure management, the transition from periodic inspections to continuous, data-driven monitoring is essential.
About the Digital Futures Postdoc Fellow
Dr. Sharma is a dedicated researcher specializing in AI and ML/DL-based solutions for SHM bridges, buildings, and offshore structures. Her work focuses on addressing key challenges like data scarcity, rapid damage detection, and real-time frameworks for bridge infrastructure. Dr. Sharma has worked as a Postdoctoral Fellow at the Basque Centre for Applied Mathematics in Bilbao, Spain, where she contributed to assessing the condition of mooring lines in offshore wind turbines using deep learning techniques.
She was involved in the IA4TES (Artificial Intelligence for Sustainable Energy Transition) project, which was part of a government-funded renewable energy initiative. Her expertise includes condition assessment and modeling using tools like CSi-Bridge, Ansys, SAP, and OpenFAST, along with developing AI/ML/DL algorithms in Matlab and Python. Dr. Sharma’s research achievements include many international publications and several well-received conference presentations.
Main supervisor
Raid Karoumi, Professor in Structural Engineering and Bridges, KTH.
Co-supervisor
John Leander, Professor in Structural Engineering and Bridges, KTH.