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.
About the project
Objective
This research aims to extract Parkinson’s disease (PD) related temporal features from brain activity and develop brain activity-based biomarkers for diagnosis and prognosis by leveraging advances in machine learning that can form the basis for classifying PD patients.
Background
PD is the second most common neurodegenerative disorder with debilitating consequences. PD diagnosis is based on behavioural symptoms and chemical/genetic markers. Such a diagnosis can be erroneous and often does not capture the disease severity. Despite being a brain disease, the brain’s electrical activity (e.g. fMRI, EEG/MEG) is completely ignored in PD diagnosis. This oversight is intriguing, considering experimental evidence indicating that PD-related changes profoundly impact the temporal dynamics of brain activity.
While biomarkers derived from brain activity usually focus on frequency domain parameters like oscillations and coherence, temporal features have been surprisingly neglected. In fact, except for certain forms of epilepsy, usually, brain activity is not considered in the diagnosis of brain diseases. However, currently, brain activity-based biomarkers are not specific enough. This may be because these are defined in the frequency domain (e.g. oscillations and coherence). Unlike in epilepsy diagnosis, temporal features of brain activity are ignored. Therefore, in my project I would like to change this. I hypothesize that brain region-specific temporal features will provide highly specific information about the disease severity and can provide a more precise diagnosis and prognosis of PD.
About the Digital Futures Postdoc Fellow
Satarupa Chakrabarti, after completing her postgraduation in Computer Science engineering, undertook a PhD degree in Computer Science from KIIT Deemed to be University, Bhubaneswar, India, where she defended her thesis related to designing a generalized epileptic seizure detection method with different feature-extracting techniques from biosensor data that showed the presence of temporal changes. She simultaneously worked as a Junior Research Fellow (JRF) with DST-SERB, India, in an interdisciplinary project (2018–2020).
She has a strong and diverse background in research, with primary research interests in biomedical engineering, signal processing, image processing, space physics, machine learning and deep learning. Satarupa is a Digital Futures Postdoctoral Research Fellow at KTH, based in the Computational Brain Science group at the Division of Computational Science and Technology. Her research experiences involved collaboration with multidisciplinary teams and conducting research on various electrical engineering and space physics projects that led to innovative, beneficial, and cutting-edge endeavours.
Main supervisor
Arvind Kumar, Associate Professor, Division of Computational Science and Technology at KTH.
Co-supervisor
Saikat Chatterjee, Associate Professor, Division of Information Science and Engineering at KTH.
About the project
Objective
Wildfire monitoring involves two main problems, i.e., active fire detection and burnt area mapping. Active fire detection aims to find the ongoing wildfire hotspots, while burnt area mapping is expected to detect the areas affected by wildfire.
This project mainly focuses on large-scale wildfire burnt area mapping and near real-time wildfire monitoring. In view of the limited transfering performance of the existing wildfire monitoring algorithms on a larger scale and various climate zones, this project aims to develop large-scale or even globally applicable models by exploiting global coverage, multi-source satellite remote sensing data and advanced machine learning/deep learning techniques.
Background
Wildfire has coexisted with human societies for more than 350 million years, always playing an important role in shaping the Earth’s surface and climate. Across the globe, wildfires are becoming larger, more frequent, longer-duration, and tend to be more destructive in terms of lives lost and economic costs because of climate change and human activities.
To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even in real-time, to support fire-fighting and keep everything under control. Satellite remote sensing enables cost-effective, accurate, and timely monitoring of the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8, and Sentinel-1/2 satellite data opens a new era for global land surface monitoring, providing an opportunity to analyse wildfire impacts around the globe.
About the Digital Futures Postdoc Fellow
Puzhao Zhang is a research fellow at the Division of Geoinformatics, Royal Institute of Technology. He received his PhD in Pattern Recognition and Intelligent Systems from Xidian University, China, at the end of 2019. His PhD research was focused on remote sensing and deep learning for change detection.
Since fall of 2017, he has worked on wildfire monitoring with radar and optical remote sensing and deep learning as a joint PhD student at KTH. His research interests include satellite imagery analysis, change detection, machine learning, deep learning, and spatio-temporal modelling for monitoring environmental change and biomass carbon dynamics.
Main supervisor
Yifang Ban, Professor, Division of Geoinformatics, KTH.
Co-supervisor
Josephine Sullivan, Associate professor, Division of Robotics, Perception and Learning, KTH.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
This research aims to develop network monitoring technologies that provide deep, real-time insights into network activity. It focuses on designing in-hardware algorithms and tailored analysis tools to detect and analyze critical factors, including security intrusions, network anomalies, and root causes of failures.
Background
Computer networks are vital to the operation of modern industries and society. Failures and security breaches can range from costly inconveniences to catastrophes. For this reason, networks are continuously monitored to ensure reliability.
Unfortunately, the high speeds of modern networks make comprehensive monitoring difficult, leaving blind spots that hinder effective analysis. Achieving fine-grained, real-time traffic analysis could transform how intrusions are detected and failures are mitigated, enabling faster responses and enhancing network resilience.
About the Digital Futures Postdoc Fellow
Jonatan Langlet is a researcher with expertise in on-hardware monitoring algorithms and data structures. He earned his PhD in computer science from Queen Mary University of London, during which he focused on high-speed data collection, probabilistic streaming data analysis, and in-network artificial intelligence. His research interests span network programmability, systems, and algorithms, particularly on real-world deployability.
Main supervisor
Marco Chiesa, Associate Professor, Division of Software and Computer Systems, KTH.
Co-supervisor
Dejan Kostić, Professor, Division of Software and Computer Systems, KTH.