About the project
Objective
The overall objective of this project is to promote deeper, sustained research engagement between Digital Futures members, both within the Learn Working Group and within Digital Futures as a whole. The Research Sparks program accomplishes this by introducing a framework for fostering long-term collaboration and knowledge exchange that solidifies the working group structures as attractive and co-dependent research environments. Research Sparks will stimulate long-term engagement through a careful combination of guided activities that will help researchers demystify their research for others, encourage hands-on and interactive learning, boost research productivity and increase the visibility of Digital Futures research activities.
Background
The project was motivated by the feedback obtained from participants in the Learn Working Group’s 2026 kick-off event on 3 March 2026. The participants requested more activities that deepened their understanding of the research of other Digital Futures members and their newfound connections, with an eye towards opportunities for organized research collaborations.
Cross-disciplinary collaboration
The Research Sparks program is specifically designed to stimulate cross-disciplinary collaboration. It is a semester long program that begins with a series of kick-off lectures that “demystify” a research topic of active focus within the Digital Futures community. These lectures aim to answer the questions, “What is this field and its driving goals? Why are there researchers at Digital Futures studying it? How does it connect to my own research?” Following these lectures, interested researchers will have the opportunity to participate in hands-on learning a
ctivities to deepen their understanding of the topic, as well as to participate in research collaborations run by experts in the topic. These ongoing activities will be supported by seminars and workshops that help the participants showcase their work and connect with international experts on the topic. Participants will learn about the new field through their engagement in these research activities, which help them broaden their own research and connect it with a research topic at the heart of Digital Futures activities.
PI: Liam Solus
Co-PIs: Kathlén Kohn, Iolanda Leite, Henrik Boström, Sebastian Dalleiger
About the project
Objective
Cancer progression is driven not only by genetic alterations within individual cells but also by complex communication between cancer cells and their surrounding microenvironment. Understanding and controlling these interactions would enable therapies that disrupt cancer-promoting cellular equilibria, improve treatment design, and accelerate precision medicine. The major technical challenge is to develop predictive computational models that capture high-dimensional, nonlinear cell-cell interactions and reliably forecast the effects of targeted interventions. A promising path to address this challenge is to combine biological knowledge on cellular signaling and regulatory mechanisms with recent advances in AI and dynamical systems theory. This would entail learning biologically consistent models of multicellular behavior, validated and enriched on micro-physiological systems.
As a first approach, we focus on communication between pancreatic cancer cells and stromal cells, whose reciprocal interactions are known to reinforce tumor growth and therapeutic resistance. While recent single-cell technologies provide unprecedented measurements of cellular states and signaling pathways, these data remain largely observational and therefore have limited predictive power for designing interventions. Moreover, existing AI approaches rarely account for the coupled dynamics governing cellular communication, making the identification of stable and controllable cellular states particularly challenging. This motivates our project, SCALE, to develop biology-informed AI methods for modeling and controlling multicellular systems. The objectives of SCALE are to establish the fundamental theory and computational tools needed to learn predictive models of cell-cell interactions, to identify healthy and disease-associated equilibria, and to design optimal in silico interventions to guide experimental validation and future therapeutic development.
Background
Cells continuously exchange signaling molecules to create the cellular microenvironment. In pancreatic cancer, communication between cancer cells and surrounding stromal cells regulates processes such as tumor growth and therapeutic resistance. In reality, these cell-cell interactions are not directly observed. Instead, they are studied through molecular measurements and cell culture experiments that capture the cellular states resulting from these interactions. Therefore, it is difficult to directly characterize the interaction mechanisms and predict how perturbations of one cell population influence the others.
The functional relationship between cell-cell interactions and the observed cellular states is governed by complex biological processes involving intracellular signaling and intercellular communication. For multicellular systems such as the pancreatic tumor microenvironment, these coupled dynamics remain difficult to characterize because of the large number of interacting molecules and feedback mechanisms. Furthermore, experimental observations are inherently noisy and capture only part of the underlying biological processes.
Naturally, modeling and predicting the coupled dynamics that govern cell-cell interactions from experimental observations constitute a challenging systems modeling problem.
Cross-disciplinary collaboration
The project will combine methods and techniques from separate research fields – (a) biological knowledge about virtual cell models and computational biology, (b) dynamical system and control theories, (c) micro physiological systems design, and experiments.
About the project
Objective
Our project will use AI to effectively turn current methods for two-dimensional (2D) spatially resolved molecular methodologies into three-dimensional (3D) methodologies and allow molecular studies of tissues as 3D objects. The Spatial Transcriptomics methodology, commercially available as Visium, is the most widely used technique to measure gene expression in 2D tissue sections, and with the recent release of VisiumHD, gene expression analysis can be obtained at unprecedented subcellular spatial resolution. We will create AI algorithms necessary to synthesize, analyze, and visualize 3D representations of cancer tissue that otherwise would be experimentally prohibitively expensive. We will exploit deep learning and construct 3D models using advanced AI methodologies such as Diffusion Models, Flow Matching, Schrödinger Bridges, and Optimal Transport methods. Specifically, we will infer 3D distributions of cell quantities from 2D data and generate continuous 3D reconstructions of tumor and metastatic tissue.
Our vision is to develop general AI methods required for revealing and comparing the 3D spatial organization of tumor tissues at reduced cost. The methods will be showcased by application to patient-matched primary tumor and metastasis samples from breast and prostate cancer patients, providing important understanding of the metastasis process, seeding patterns, and differences between primary and metastatic tumor micro environments.
Background
Around 90% of all cancer patients die due to metastases, colonies of cancer cells that have established themselves at a distance from the primary tumor. Despite this, metastases are surprisingly understudied and consequently not well-understood. Most patients with breast or prostate cancer are diagnosed with localized disease. However, when the disease has spread and created metastases, effective treatments typically do not exist. This highlights the necessity of new, more efficient therapies aimed toward clinically established metastases as well as micro-metastases with growth potential.
Spatially resolved gene expression data have transformed biomedical research and motivated extensive method development for mapping cell types and integrating multiple slides and molecular modalities. However, these approaches remain restricted to 2D views of tissue, and understanding the 3D organization of tumors and their microenvironments represents a major challenge. Current spatial omics and lineage analyses remain 2D. By combining cutting-edge biological data with state-of-the-art AI methodologies for 3D reconstruction and lineage inference, this project advances computational methods and biological understanding of tumor evolution and metastatic dissemination.
Cross-disciplinary collaboration
The project integrates AI and computational modeling with spatial omics in the context of clinical cancer biology. The groups combine expertise in machine learning and probabilistic modeling for inference and synthesis of spatial and lineage data, together with experimental spatial omics and single-cell sequencing. Clinical collaborators will guide the biological questions, ensuring relevance to metastatic disease and tumor microenvironment differences. The work is organized into three components: data generation, computational methods development (2D analysis and 3D synthesis), and biological investigation.
The computational framework integrates single-cell, spatial, and imaging data to enable 3D spatial modeling of tumor evolution. Clinically important questions, centered on metastatic potential, clonal seeding patterns, and tumor–microenvironment interactions, will drive the biological investigation. Through joint supervision, shared infrastructure at SciLifeLab and collaboration with national and international stakeholders, the project establishes a translational bridge from computational methods to clinical application and contributes to precision oncology.
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