About the project
Objective
The goal of this project is to better understand how the brain changes in diseases like Alzheimer and Parkinson. We study the brain’s wiring using a special type of MRI scan and look for patterns in how different regions are connected. By using advanced mathematical tools, we can detect subtle changes in these brain networks that are often missed by standard methods. These patterns may help us distinguish between healthy and diseased brains, and even identify early signs of disease before symptoms become clear. Ultimately, this research could lead to more accurate and earlier diagnosis, as well as a better understanding of how these diseases develop over time.
Background
Alzheimer’s and Parkinson’s diseases are the two most common neurodegenerative diseases, affecting more than 50 million people worldwide. They have a major impact not only on the patients, but also their families and healthcare systems. By improving early diagnosis through advancing methods to learn from complex brain connectivity patterns, this proposal has the potential to generate, in the future, benefits that extend beyond the scientific community, contributing both to societal well-being and to the sustainability of healthcare systems.
Tractography from diffusion magnetic resonance imaging (dMRI) reveals the wiring of the human brain but remains challenging to analyze due to its complexity and variability. Classic approaches, such as streamline count, fractional anisotropy, mean diffusivity, and tract length often capture pairwise relationships, but neglect the higher-order structural organization of the white matter network. As a result, complex and spatially distributed alterations that emerge early in neurodegenerative diseases can remain undetected.
Topological data analysis (TDA) and machine learning on graphs offer promising complementary tools to address this limitation. TDA provides tools to quantify the global and local topology of structural connectivity, such as loops, cycles, and motifs, that go beyond metrics accounting for pairwise interactions. Graph-based machine learning methods, such as graph neuronal network (GNN), can then leverage this richer representation to learn complex patterns of connectivity and distinguish between healthy and diseased brains.
About the Digital Futures Postdoc Fellow
Ilaria Carannante is a Computational Neuroscientist with a background in mathematics. She holds a PhD in Computer Science, with specialization in Computational Biology from KTH. Ilaria has always been passionate about science and mathematics, and her biggest career goal is to contribute expertise in neuroscience to advance our understanding of the human brain and to develop approaches for remedying disorders that impede its proper functioning.
During her PhD in Professor Jeanette Hellgren Kotaleski’s lab, she developed data-driven multiscale models of a brain region called Striatum. As a postdoctoral researcher in Professor Alain Destexhe’s group at CNRS in Paris, she expanded her work to larger-scale networks with a special focus on the Basal Ganglia (to which the striatum belongs). The next natural step in her research journey is to extend this multiscale approach to the human brain. For this reason in her current research she aims to leverage diffusion MRI, combined with topological data analysis and graph neural networks, to uncover higher-order patterns in brain connectivity.
In the long term, she aims to build data-driven whole-brain models to better understand brain function in health and Parkinson’s disease, with the ultimate goal of improving diagnosis and guiding targeted interventions.
Main supervisor
Martina Scolamiero, Associate Professor at the division of Algebra, Combinatorics, Geometry and Topology, KTH.
Co-supervisor
Rodrigo Moreno, Professor at the division of Biomedical Imaging, KTH.
About the project
Objective
This project aims to implement intrinsic magnetic resonance elastography (I-MRE). Instead of the pump, it uses vascular pulsatility as the source of tissue movement. More specifically, A) we will adapt and test I-MRE techniques to acquire subtle brain tissue motion in MRI scanners; B) develop and validate machine learning and computational models to estimate mechanics from I-MRE data; C) inform traumatic brain injury (TBI) simulations with I-MRE.
Background
Magnetic resonance elastography (MRE) in the brain is a new technique in which the mechanical properties of the brain tissue can be estimated non-invasively. Unfortunately, the standard MRE setting requires an expensive pneumatic pump and specialized software, hindering its use in most hospitals.
Cross-disciplinary collaboration
This project is creating a new multidisciplinary collaboration between three PIs with complementary expertise in understanding the biomechanical properties of the brain: Rodrigo Moreno’s group is an expert in magnetic resonance elastography. Zhou Zhou’s group’s expertise is in neuroimaging-informed finite-element simulation of brain biomechanics. Lisa Prahl Wittberg’s research is focused on computational fluid mechanics (CFD)blood flow dynamics using both experimental and numerical methods (computational fluid mechanics, CFD).
About the project
Objective
This project aims to establish an AI-based online platform for automated, and robust personalization and positioning of HBMs, focusing on infant HBMs. By developing a family of infant HBMs equipped with efficient personalization and a novel AI-based positioning pipeline, the project facilities rapid and subject-specific model generation that can foster industrial and clinical innovations relating to infant safety.
Background
Finite element human body models (HBMs) are digitalized representations of the human body and have become essential tools in both industrial innovation and clinical applications. These models often are a baseline and in a specified position, and before the use of the HBMs, personalization and positioning of HBMs are needed. Despite continuous active development, HBM positioning remains challenging and tedious; further comparing with existing adult HBMs, infant and child HBMs are underdeveloped.
This project builds on, and further develops, the results from the Research Pair project: “AI-based Positioning and Personalization Platform for Human Body Models (HBMs)“.
Crossdisciplinary collaboration
This project combines expertise within mechanical and biomechanical modeling (from KTH School of Engineering Sciences in Chemistry, Biotechnology and Health) with expertise in artificial intelligence (from the Department of Industrial Systems at RISE).
About the project
Objective
The project will develop a novel class of data-driven reduced-order models (ROM) that can represent wind farm flow dynamics with high-level accuracy, while being fast enough to support operational run-time analyses. The central aim is to bridge the gap between detailed computational fluid dynamics (CFD) simulations and the simpler models typically used in operational contexts, by employing CFD data to develop and train new machine-learning based models. The research will follow a modular and progressive strategy, starting from single turbine wake representation and then extending to farm-level interactions modeling.
Background
Wind farms operate in atmospheric conditions that vary across a wide range of spatial and temporal scales. At the farm scale, wakes develop and interact in ways that are difficult to capture with standard superimposition-based engineering models, especially if the site includes strong dependencies on terrain complexities, stability-driven variability, or farm-scale phenomena such as global blockage, which can contribute to systematic misprediction in terms of power forecasting and operating strategies.
High-fidelity CFD, for example large-eddy simulation, can capture these interactions at wind-farm scale, but the computational cost makes it impractical for real-time monitoring and frequent predictive analyses. By contrast, existing ROMs are often built on semi-empirical or engineering approximations that represent wakes through superposition of velocity deficits, deflections, and added turbulence. Although computationally efficient, these models often fail to capture complex wake-wake interactions, terrain-induced flow effects, stability dependent variability, and farm-scale phenomena such as blockage. Moreover, they are rarely designed to ingest real operational data, such as supervisory control and data acquisition (SCADA) logs, which encode the actual operating states of a wind farm. This motivates the need to bridge the gap between accurate but expensive simulations and fast but less reliable wake models, enabling near-real-time representations that can support forecasting, optimization, and future control strategies.
About the Digital Futures Postdoc Fellow
Filippo De Girolamo is a mechanical engineer and researcher working at the intersection of computational fluid dynamics and machine learning for wind energy applications. He holds a PhD in Energy and Environment from Sapienza University of Rome in Italy, with research focused on wind turbine flows and data-driven modeling of wake dynamics in offshore environments. He has developed experience across multiple wind energy problems, including wake and turbulence modeling with Large-Eddy Simulation, data-driven wake decomposition via unsupervised learning, and SCADA-based diagnostics for wind farm monitoring. He also carried out a research visit at the University of Texas at Dallas, working on high-fidelity simulations of real wind farms in complex terrain.
Main supervisor
Prof. Dan Henningson, KTH
Co-supervisor
Prof. Hedvig Kjellström, KTH
About the project
Objective
This project aims to develop a reliable and robust AI tool for early fault detection in power transformers. By combining physical laws with limited operational data, the project aims to predict the health and remaining lifetime of power transformers efficiently. The developed AI tool would enable proactive maintenance, reducing costs and enhancing power grid reliability.
Background
Power transformers play a silent but crucial role in our daily lives. They enable the transmission of electricity over long distances, powering homes, hospitals, industries, and public infrastructure. Although they usually operate unnoticed, transformers are subject to high stress and age over time. When a transformer fails, the consequences can be severe, including power outages, costly repairs, and safety risks. Today, many transformer failures happen because problems are detected too late, mainly due to limited and incomplete monitoring data.
To reduce these risks, various indicators are used, such as gas measurements, thermal images, vibration signals, and magnetic behavior, to assess a transformer’s condition. However, collecting large amounts of high-quality data from all these sources is difficult and expensive. Most existing AI methods depend heavily on large datasets, which limits their usefulness in real-world power systems. The project tackles this challenge by developing an AI model that combines data with physical knowledge about how transformers work. By embedding physics directly into the learning process, the model can learn meaningful patterns even from limited data.
The proposed approach uses a physics-enhanced multimodal neural operator framework that can combine information from multiple data sources and predict how long a transformer can continue to operate safely. The model is designed to be fast, reliable, and suitable for real-time monitoring. By enabling early fault detection and maintenance decisions, this research supports the digital transformation of power infrastructure and contributes to a more stable, efficient, and sustainable energy grid.
About the Digital Futures Postdoc Fellow
Abhishek Chandra is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. He holds a PhD in Electrical Engineering from Eindhoven University of Technology, The Netherlands, where his research focused on developing AI tools for characterizing piezoelectric and magnetic materials. His academic background includes applied mathematics, scientific computing, and AI. Abhishek has received several prestigious fellowships and scholarships, including the Digital Futures Postdoctoral Fellowship at KTH and the Information and Knowledge Society Scholarship at Université de Lille. With expertise in scientific machine learning and energy systems, his work aims to bridge the gap between theoretical AI models and practical engineering applications in critical infrastructure.
Main supervisor
Prof. Dr. Lina Bertling Tjernberg, Full Professor, Department of Electric Power and Energy Systems, EECS, KTH.
Co-supervisor
Prof. Dr. Cristian Rojas, Full Professor, Department of Decision and Control Systems, EECS, KTH.
About the project
Objective
The aim of the project is to develop control theoretic tools that can handle coarse models. In particular, coarse models as typically found in synthetic biology. Mathematically, we capture ‘coarseness’ through topological dynamical systems theory and aim to provide control theoretic counterparts to well-established index theories. Developing the theory is a first step, a second step is to integrate these tools directly into data-driven pipelines.
Background
Several pressing biological questions of today have a strong control-theoretic component, e.g., we do not only want to describe a cancerous cell, we want to prescribe its dynamics. Compared to classical fields of engineering, biology usually lacks the type of models that contemporary control theory can handle well. Instead, biological models are typically coarse and largely qualitative. In this project we accept this coarseness, take a topological viewpoint and develop control theoretic tools at precisely this level of granularity. We focus in particular on genetic regulatory networks that can or should generate oscillations. This, because of the large practical and theoretical appeal.
About the Digital Futures Postdoc Fellow
Wouter Jongeneel is a control theorist fascinated by topology and the life sciences. He received his PhD in Electrical Engineering from EPFL in Switzerland. Prior to that, he received a MSc in Systems & Control from TU Delft in the Netherlands. His research is centered around understanding the interplay between structural features of a system and qualitative behaviour it can display.
Main supervisor
Karl Henrik Johansson, KTH
Co-supervisor
Martina Scolamiero, KTH
About the project
Objective
The project aims to establish the technical, organizational, and legal foundations for an AI-based feedback system that supports operators at Stockholm City’s Elderly Safety Call Center. The system will function as a co-pilot, offering insights, alerts, and structured performance feedback to strengthen decision-making and professional development. Central objectives include analyzing operators’ decision-making processes and operational challenges in high-stakes, time-critical situations.
Another key objective is to identify the legal, ethical, and technical boundaries for data exchange across municipal and regional healthcare infrastructures. These insights will guide the design and prototyping of the LLM-based feedback system that enables post-call analysis and continuous learning. Further on, the project outcomes will inform the future development of a real-time feedback system.
Background
Elderly safety call centers play a critical role in Sweden’s elderly care system. Operators handle acute welfare and emergency-related calls, coordinate between medical and elderly home care actors, and make rapid decisions regarding additional services or short-term interventions. As the service operates during evenings, nights, and weekends, when regular elderly care services have limited staffing, operators manage cases under greater uncertainty and time pressure. The work is carried out under significant pressure, guided by a multitude of rules, guidelines, and policies, yet operators receive little structured feedback on the quality or outcomes of their decisions.
At the same time, demographic developments increase the need for efficient decision-making. A rising need of elderly care is projected to grow substantially and will lead to increased staffing requirements in elderly care. Managing this development without compromising quality will require new forms of digital support, particularly for staff working in high-stakes and time-critical environments such as night and weekend operations.
Current CRM systems in elderly care call centers provide access to information but offer limited decision support, structured learning, or follow-up of outcomes. Facing a stressful decision-making processes with little support affects both staff well-being and the quality of services provided to older adults. Strengthening decision support has the potential to reduce errors, improve consistency, and enhance both staff and patient well-being, while also generating significant savings in public resources.
Against this backdrop, AI-powered feedback systems offer a promising avenue. Recent advances in large language models (LLMs) and data-driven communication analysis open new possibilities for post-call learning, performance feedback, and more systematic follow-up of decision outcomes. This project aims to address these challenges by developing an AI-based feedback system that supports operators in elderly safety call centers. By enabling structured post-call analysis, reflective learning, and improved decision-making, the project seeks to enhance operator support, strengthen the resilience of elderly care services, and contribute to a sustainable response to the demographic challenges ahead.
Cross-disciplinary collaboration
The project team combines expertise in health care logistics, sociological analysis, service- and systemic design, complex system modeling, AI and knowledge graph methods. This mix of competencies supports observational studies, joint exploration and co-creation in the call center and the development and training of LLM models. The work is carried out in close cooperation between KTH, the City of Stockholm, Region Stockholm, and Stockholm University.
PI: Sebastiaan Meijer, KTH, Department of Biomedical Engineering and Health Systems
Co-PI: Magnus Eneberg, KTH, Department of Engineering Design