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.
About the project
Objective
This project aims to propose innovative distributed learning methods based on adaptive gradient coding techniques. Within this framework, workers’ participation is fluidly adjusted in real-time during training to enhance learning performance under practical constraints. We will offer rigorous theoretical proofs to ensure the convergence of the proposed methods, solidifying their reliability. We will also test the performance of the proposed methods on both simulated and actual datasets in real-world scenarios. This evaluation will benchmark the effectiveness of our techniques and underscore their superiority over current practices.
Background
In the framework of distributed learning, a central server aggregates computational results from various workers to update the trained model. However, in practical scenarios, “stragglers”—workers who are slow or unresponsive—can significantly impede overall training time. Addressing these slowdowns is crucial for real-time processing requirements in the healthcare and smart transportation sectors.
While current distributed learning methods employ gradient coding to mitigate the effects of stragglers, they rely on a fixed number of the fastest workers throughout the entire training process, which have limited flexibility in balancing training time and loss. Based on that, our research question is how to transcend the limitations inherent in existing distributed learning methods and to reduce the training time required to achieve a specified training loss.
About the Digital Futures Postdoc Fellow
Chengxi Li received a PhD in 2022 from the Department of Electronic Engineering at Tsinghua University and a bachelor’s degree in 2018 from the University of Electronic Science and Technology of China. Her research interests lie in distributed learning, federated learning, signal processing and information theory.
Main supervisor
Mikael Skoglund, Professor, Head of Department, Division of Information Science and Engineering, EECS, KTH.
Co-supervisor
Ming Xiao, Professor, Division of Information Science and Engineering, EECS, KTH.
About the project
Objective
The goal of the project is to design reactive, intelligent planning and control algorithms for underwater vehicles which quantify and reason about risk as well as incorporate machine learning. This will enable the use of AUVs for more autonomous environmental data collection with reduced human involvement and, therefore, reduced human risk.
Background
Autonomous underwater vehicles have great potential for environmental monitoring and exploration, but there are important technical challenges that prevent their widespread use. Some of the major challenges are that GPS location information is not available underwater, communication underwater is limited, and there may be significant vehicle drift due to local hydrodynamic disturbances. As a result, it is difficult to ensure high levels of reliability for these vehicles. To bridge the reliability gap, this project aims to design and test planning and control algorithms that explicitly reason about uncertainty and produce intelligent policies to minimize that uncertainty while gathering information about the vehicle’s environment.
About the Digital Futures Postdoc Fellow
Chelsea Sidrane began her studies with a Bachelor’s degree in mechanical engineering at Cornell University, where she developed an interest in dynamical systems and control theory. She went on to study machine learning and robot planning in her Master’s studies at Stanford University before beginning a PhD in the Stanford Intelligent Systems Laboratory focused on verifying neural networks. She defended her thesis, “Neural Network Verification for Nonlinear Systems”, in the summer of 2022. She is now a Digital Futures Postdoctoral Research Fellow at KTH based in the Planiacs group at the Division of Robotics, Perception and Learning (RPL).
Main supervisor
Jana Tumova, Associate Professor in the Division of Robotics, Perception and Learning, KTH
Co-supervisor
Ivan Stenius, Associate Professor in Vessel Engineering & Solid Mechanics, KTH
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This project aims to engineer the rules by which intelligent robots interact with each other in public spaces. In particular, this project focuses on tools from the field of mechanism design, which strives to set rules for interaction between rational agents. Designing rules via mechanism design allows for systems where robots collaborate toward global goals, even when their individual goals and specifications differ. Mechanism design, coupled with tools from formal methods and planning, can help achieve global goals like safely sharing resources, minimizing the risk of failures in uncertain systems, and engendering the trust of humans.
Background
Multi-robot planning is a complex optimization problem that must consider both the goals of each robot and the interaction between those goals. The first step toward solving this problem requires understanding the realities of modern-day robots. The second step requires meta-reasoning through game theory. This problem becomes more complicated with the introduction of humans, who interact with robots in unique and often unpredictable ways.
About the Digital Futures Postdoc Fellow
Anna Gautier is a postdoctoral researcher in artificial intelligence and robotics. She works in the Robotics, Perception and Learning division at KTH Royal Institute of Technology. Anna obtained her PhD from the University of Oxford in July 2023, with a thesis entitled “Resource Allocation for Constrained Multi-Agent Systems.” Her research interests include multi-agent systems, human-robot interaction, planning under uncertainty, formal methods and mechanism design.
Main supervisor
Jana Tumova, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Co-supervisor
Iolanda Leite, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Watch the recorded presentation at the Digitalize in Stockholm 2023 event