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
- Careful assessment of what new information due to personal data of an individual has been memorised;
- Guidelines outlining what residuals of personal information in a ML model should be legally acceptable;
- Explore how the RTBF can be effectively implemented, ultimately in the context of ML systems.
Background
In the context of machine learning (ML), there is an urgent need to clarify what “deletion” under the Right to be Forgotten (RTBF) truly entails. As ML models generalize and internalize patterns from data, achieving complete removal of an individual data point remains a major technical challenge. We argue, however, that such full deletion often exceeds what the law actually requires. REFLECT-ML aims to explore how the RTBF can be effectively implemented, ultimately in the context of ML systems. We will address the disconnect between the abstract legal language of regulators and operationally founded technical theoretical measures that can be used to quantify the new information associated with individual data as well as the practical complexities in estimating those. Further, we will develop different technical approaches to control its memorization as well as exploring machine unlearning attempts.
Cross-disciplinary collaboration
The team consists of PI Oechtering (researcher in information theory, relevant for information quantification), PI Colonna (researcher in law, relevant knowledge related to the GDPR and AI Act), and PI Johansson (researcher in optimization, relevant for the design of efficient learning algorithms). The outlined work is a cross-disciplinary effort since information measures need to be legal compliant, legal requests need to be algorithmically feasible, and algorithms need to aim for the right objective.
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 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.
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
This project aims to provide mitigation solutions for decision-makers to reduce human and environmental exposure to particle emissions caused by the transport sector. Through the use of cross-disciplinary approaches, the project develops methodologies that are tested and validated by adopting Stockholm as a digital sandbox.
Background
In our daily city life, we are constantly exposed to means of transport such as passenger vehicles, heavy-duty vehicles, and rail transportation. These vehicles release toxic particle emissions originating from exhaust and non-exhaust sources. Recent projections indicate an increase of non-exhaust emissions in urban areas from 0.5% in 2021 to 67% in 2050. Non-exhaust emissions are the primary source of inhalable Particulate Matter (PM). With a diameter smaller than 10 µm, PM10 can be inhalable by humans causing inflammations and other health diseases. On the other hand, PM with a size larger than PM10 can deposit over the nearby infrastructures contributing to environmental pollution.
Stockholm is among the forefront European cities capable of monitoring in real-time level of PM and policy makers strive to mitigate these emissions. However, despite these efforts, the concerns regarding the increase of non-exhaust PM in the urban areas remain critical.
About the Digital Futures Postdoc Fellow
Henri Giudici completed his Ph.D. in Civil and Environmental Engineering at the Norwegian University of Science and Technology – NTNU (Norway). He specialized in vehicle tire-pavement interactions in winter conditions. His research supported the Norwegian road authority in reducing road salt application rates during winter. Between 2019 and 2022, he served as an industrial Principal Scientist, developing technologies and data-driven approaches for assessing the quality of transport infrastructures. In 2022, he continued his academic career as a researcher in systems engineering at the University of South-Eastern Norway – USN (Norway). In his current Digital Futures postdoc, Henri fosters agile approaches to integrate scientific evidence into policy-making by bridging transport tribology, systems engineering and data science.
Main supervisor
Ellen Bergseth, Associate Professor at Department of Engineering Design, KTH.
Co-supervisor
Ulf Olofsson, Professor at Department of Engineering Design, KTH.