About the project

Objective
We have two key objectives:

To this end besides extensive literature surveys we will organize thematic workshops to find synergies among digital future researchers. Besides, we will bring together international experts in the area for a conference at Digital Futures.

Background
Biological systems are shaped by their evolutionary history and adaptation animals acquire throughout their life. This implies that even if two beings exhibit similar behavior the underlying physiological mechanism could be different and vice versa. ‘Degeneracy’ and diversity can be seen at every scale in biological systems: from gene expressions to behavior and can be quantitatively observed in heavy-tailed distribution of physiological parameters. Parameters that describe a biological system usually do not follow the classical Gaussian (or exponential family) distribution.

This biological reality is in stark contrast to how we build computational models of biological systems: Either take a data-driven or normative approach. Data-driven models rely on extensive data but typically we end up with an average model. Normative modeling on the other hand seeks to model biological systems assuming (based on data or some physical/chemical principles) that the system is ‘optimized’ in some sense. Both approaches ignore the fact that cohorts of biological systems do not follow normal distribution and each animal is optimized in their own way.

In this project we want to better understand the structure of the non-normal nature of biological data and how it can be better captured in our computational models.

Cross-disciplinary collaboration
We will focus on three specific spatial scales i.e. molecular level (i.e. genome, transcriptome and proteome), tissue/organ level (i.e. neural networks, cellular physiology, multiscale tissue mechanics, computational human models) and behavior level (i.e. decision-making, motor control). While the data at each level is different we expect there will be some common principles that relate diversity at different levels.

Contacts: Arvind Kumar, Xaiogai Li and Lanie Gutierrez-Farewik

About the project

Objective
This project aims to characterise the brain’s mechanical properties through Magnetic resonance elastography (MRE) in adolescents and older children and correlate them with risk factors for developing anxiety disorders. Our long-term goal is to characterize the brain’s mechanical properties at different ages to improve the diagnosis and treatment of anxiety disorders in the young population. Our long-term aim is to extend this project to characterize the mechanical properties of the brain at different stages of brain development, which can be used to improve the diagnoses of various neuropsychological diseases, especially at early stages where treatments are more likely to have an effect and to track the response of patients to treatments better.

Background
Very little is currently known about the evolution of the mechanical tissue properties of the brain in the first two decades of life. Such information can be valuable in improving the diagnosis of prevalent neuropsychiatric disorders in the young population, particularly anxiety disorders. MRE in the brain is a new technique in which mechanical properties of the brain tissue are estimated non-invasively. MRE has barely been used in children. MRE for the brain is only available in a few sites worldwide. This Spring, KTH will become the only site in Sweden with this technology. This opens a tremendous strategic opportunity for KTH to take the lead in its use for brain diseases.

Crossdisciplinary collaboration
The researchers in the team represent the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH and the Psychology Department at Stockholm University.

About the project

Objective
This research project aims to realize faster-than-real-time (simulation time less than physical flow time) and high-resolution fluid flow simulation in engineering applications, with indoor climate as a pilot. The expected outcome of this project is a static CNN for super-resolution to achieve fast prediction of steady-state indoor airflow, a hybrid RCNN for super-resolution to achieve faster-than-real-time prediction of transient indoor airflow and standards for low-resolution input data by numerical simulation and experimental data. Besides the indoor flow simulations, this project would open a broad spectrum of engineering accurate computational fluid dynamics (CFD) applications complementary to today’s standard application of RANS turbulence models.

Background
Fast, high-resolution heat and mass transfer prediction are critical in many engineering applications. For example, in creating a desired indoor climate, fast and high-resolution airflow and contaminant transport simulation would help sustain life, reduce cost, and minimize energy consumption. In specific scenarios such as emergency management, conceptual design, heating, ventilation, air conditioning and refrigeration (HVAC&R) system control, faster-than-real-time simulation is desired.

Borrowed from computer vision and image recognition, super-resolution is a promising novel approach for the fast analysis of fluid mechanics problems. Super-resolution refers to techniques that obtain a high-resolution flow image output from a low-resolution flow image input. However, super-resolution implementation in real engineering applications faces two major challenges. Firstly, the input low-resolution flow data could be either obtained by fast and computationally inexpensive simulation, e.g., coarse grid CFD simulation, which might be associated with wrong physics or by experimental measurements, where the least number of sensors needed should be identified. Secondly, for transient indoor/outdoor airflow, super-resolution fails to extrapolate the flow fields that belong to a different statistical distribution. Therefore, this study aims to solve the two challenges that state-of-the-art super-resolution models face.

Crossdisciplinary collaboration
The researchers in the team represent the KTH of Architecture and the Built Environment, Department of Civil and Architectural Engineering, KTH School of Engineering Sciences, and Department of Engineering Mechanics.

About the project

Objective
The project envisions a mobile cyber-physical system where people carrying mobile sensors (e.g., smartphones, smartcards) generate large amounts of trajectory data to sense and monitor human interactions with physical and social environments. The project aims to develop a causal artificial intelligence (AI) methodology to analyze and model human mobility behaviour dynamics (decision-making) using individual travel trajectory data and develop the causal diagrams of human mobility behaviour under disturbances that could help design effective strategies for sustainable and resilient urban mobility systems. The research challenges are learning the complex ‘hidden’ human decision-making mechanism from pervasive ‘observed’ trajectories and developing effective, scalable causal AI models and algorithms.

Background
The ever-changing mobility landscape and climate change continue challenging existing operating models and the responsiveness of city planners, policymakers, and regulators. City authorities have growing investment needs that require more focused operations and management strategies that align mobility portfolios to societal goals. The project targets the root cause of traffic (human) and novel analytic techniques to learn and predict human mobility behaviour dynamics from pervasive mobile sensing data that can help cities meet both sustainability challenges (through predicting congestion, emissions, and energy consumption) and improve urban resilience to disruptive events (such as infrastructure failures, natural disasters, or pandemics).

The human mobility area witnessed active developments in two broad but separate fields: transport and computer science. They work with different data, use different methods, and answer different but overlapping questions, i.e., mobility behaviour modelling using ‘small’ data in transport and mobility pattern analysis using ‘big’ data in computer science. A solid bridge between these is beneficial and needed but is still an open challenge. Mobile sensing and information technology have enabled us to collect much mobility trajectory data from human decision-makers. The predictive AI techniques show the potential to learn and predict human mobility using these trajectory data efficiently. However, they continually run up against the limits of what they observe (correlations, not causal relationships), thus hindering any serious applicability for preparedness and response policies for cities without understanding the causal mobility dynamics.

cAIMBER will bridge the two human mobility research streams in Transport Science and Computer Science. Also, it will develop the causal AI methodology, merging the RL and Causal Inference research fields. Integrating interdisciplinary expertise and techniques will derive generalizable insights about human behaviour dynamics that contribute to the scientific communities’ theoretical conceptualization of travel choices and decision-making mechanisms. Practically, cAIMBER conducts extensive empirical analysis using a comprehensive dataset covering different types of system disturbances for seven years. The accumulated knowledge of human mobility under these situational contexts would help city planners and service operators to make more informed decisions for sustainable and resilient travel.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Architecture and Built Environment (ABE), Civil and Architectural Engineering Department, Transport Planning Division and KTH School of Engineering Science (SCI), Mathematics Department, Mathematics for Data and AI Division. Strategic research partners at KTH iMobility Lab and MIT Transit Lab support the project.

About the project

Objective
Digital humans and AI are becoming an integrated part of society. Modern chatbots such as GPT3 (Generative Pre-trained Transformer 3) have shown a remarkable capability of generating human-like responses and may even be prompted to act sarcastic, depressed or shy. To bring such systems into an embodied agent, such as a digital human, the agent’s body motion should reflect the same psychological inner state. This is, however, lacking in modern synthesis systems of non-verbal behaviours, which only generate generic motion based on a neutral speaker.

In this project, we propose to enhance virtual agents’ state of the art by giving them a psychological inner state that colours their nonverbal behaviour. We term these agents artificial actors – virtual digital humans that can take directions and produce expressive and convincing acting behaviour, much like a real actor takes instructions from a director. This means that it should be possible to instruct a character not only what to do or talk about but also how these actions should be performed (e.g. as a shy person with social phobia).

The project includes

We will specifically create a virtual agent simulating a therapy patient and evaluate its performance with therapists or therapists in training.

Background
To be announced

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, KTH and the Psychology Department at Stockholm University.

About the project

Objective
This project aims to combine magnetic resonance elastography (MRE) and advanced diffusion magnetic resonance imaging (dMRI) to develop the next generation of MRE methods to improve the biomechanical characterization of brain tissue.

More specifically, A) we will propose computational models that consider the diffusion processes to estimate biomechanical properties of the brain at a sub-voxel level and B) we will use advanced dMRI data to predict biomechanical properties without the expensive equipment required by MRE.

We will apply these new methods to data we are acquiring from Parkinson’s disease (PD) and cancer patients and healthy subjects and test their potential to improve the diagnosis and treatment of patients and to characterize biomechanical changes during ageing.

Background
Neurological disorders affect millions of persons worldwide. While MRI is crucial in clinical diagnostics, it is largely limited to morphological features. Understanding how the mechanical properties of tissues change due to disease can give valuable information for improving their early detection and diagnosis. A promising tool for non-invasively estimating these mechanical properties through magnetic resonance imaging (MRI) is Magnetic Resonance Elastography (MRE). The current MRE methods generate limited mechanical information about the brain tissue at a relatively low image resolution. The proposed methods will provide a better mechanical characterization of tissues for improved diagnosis. Moreover, using additional MRI modalities, we can better understand the mechanism behind the changes in mechanical properties due to diseases. This can eventually lead to better treatments for patients. MRE for the brain is only available in a few sites worldwide. In Autumn 2022, the KTH-owned MRI scanner became the first one in Sweden capable of performing MRE examinations in the brain.

Crossdisciplinary collaboration
In this project, we are establishing a new cross-disciplinary and complementary collaboration:

About the project

Objective
The aim of this project is to analyse and understand the environmental impacts of increased digitalisation and the use of Information and Communication Technologies (ICT) from a life cycle perspective. The project will include both method development and application through case studies. It will build on established life cycle assessment (LCA) approaches and further develop methods to estimate the environmental and resource impacts of both existing and future ICT systems and solutions. The assessments will address a broad spectrum of impacts, including climate change and energy use, but also other environmental and resource-related impacts.

The project will analyse not only the direct (first-order) environmental impacts of ICT, including those related to raw material extraction, production, use, and end-of-life treatment, but also indirect effects such as substitution and optimisation (second-order), as well as broader transformative and rebound effects (higher-order). Particular attention will be given to the enabling potential of digital solutions, meaning their ability to reduce environmental impacts in other sectors, and to identifying the conditions under which such benefits are achieved or undermined by unintended consequences.

Several methodological challenges will be addressed in this project. These include the development of simplified LCA methods suitable for use in product development and design, prospective LCA approaches to evaluate potential impacts of future systems, and the inclusion of broader environmental and resource indicators. The project will also assess how digitalisation affects consumption patterns and explore the environmental implications of those changes. Case studies may vary in scale, focusing on specific devices, applications, and broader sectoral or societal assessments.

Background
The ICT sector is currently responsible for an estimated 1.4 percent of global greenhouse gas emissions (Malmodin et al. 2024). While the future size of this footprint is uncertain, digitalisation is also regarded as a key enabler of sustainability through improved efficiency, reduced material consumption, and the introduction of new low-carbon solutions. This dual role presents both opportunities and risks, making it essential to apply robust and scientifically grounded methods for evaluating environmental consequences.

The environmental effects of digitalisation can be categorised into direct effects such as emissions and resource use during production, use and disposal, second-order impacts (substitution and optimisation effects that occur when digital systems replace traditional ones), and higher-order impacts (including systemic changes such as rebound and induction effects, as well as positive effects like encouraging sustainable lifestyles). A comprehensive understanding of these effects is necessary for sound decision-making and policy formulation.

Life cycle assessment (LCA) is a widely accepted and standardised method for assessing environmental and resource impacts throughout the life cycle of a product, service, or system, from raw material extraction to end-of-life management. This methodology will be used to evaluate both direct and indirect impacts of ICT.

This project will advance LCA methodologies and provide improved tools for assessing how digitalisation influences environmental sustainability. The collaboration between KTH and Ericsson builds on ongoing joint efforts and aims to strengthen the knowledge base and academic ecosystem in the Stockholm region. Outcomes from the project will contribute to both research and education, supporting science-based evaluations of digital solutions and their alignment with climate and environmental goals.

Cross-disciplinary collaboration
This project is inherently cross-disciplinary, as it brings together expertise from environmental sciences, engineering, digital technologies, futures studies, and industrial practice to address the complex interplay between digitalisation and sustainability. Understanding the environmental life cycle impacts of ICT systems requires not only advanced methodological knowledge in life cycle assessment (LCA), but also technical understanding of digital technologies, insights into social and behavioural change, and practical perspectives from industry.

This cross-disciplinary setup also extends to the societal dimension of the project. The analysis of consumption patterns, substitution effects, and rebound phenomena requires insights from social sciences and sustainability transitions research. By integrating these perspectives with engineering and environmental science approaches, the project aims to provide a more holistic understanding of the environmental impacts of digitalisation.

PI
Göran Finnveden is a Professor of Environmental Strategic Analysis at the Department of Sustainable Development, Environmental Sciences and Engineering at KTH. He is also the director of the Mistra Sustainable Consumption research program. His research is focused on sustainable consumption and life cycle assessment, and other sustainability assessment tools. The research includes method development and case studies in different areas, including the environmental impacts of ICT. He is the PI of this project.

Co-PIs
Mattias Höjer is a Professor and an expert in environmental strategies and futures studies at KTH Royal Institute of Technology in Stockholm, Sweden. He has been a professor since 2012 and is affiliated with the Department of Sustainable Development, Environmental Science and Engineering (SEED), as well as KTH Digital Futures and the KTH Climate Action Centre. His research encompasses smart sustainable cities, digitalization, energy use, climate change mitigation, and the development of futures studies methodologies. He has a particular interest in how digital technologies can support sustainability transitions in urban environments. He is a co-PI of this project.

Jens Malmodin is a Senior Specialist in Environmental Impacts and LCA at Ericsson and has over 30 years of experience in energy-efficient design, life cycle assessment (LCA), environmental assessments, and environmental data reporting. He has published numerous papers and articles on the LCA of ICT products, systems, and services, including studies of the energy and carbon footprint of the ICT sector and how ICT can help society reduce its environmental impact. Jens holds an M.Sc. in material engineering from the Royal Institute of Technology (KTH), Stockholm, Sweden. He is a co-PI of this project.

Partners
Shaoib Azizi has the experience of working in the industry on large-scale refrigeration and heat pump systems and as an entrepreneur with solar pumps.  His PhD at Umeå University included research on the opportunities for digital tools to improve buiding management and energy efficiency. He defended his thesis “A multi-method Assessment to Support Energy Efficiency Decisions in Existing Residential and Academic Buildings” in September 2021. Shoaib became a Digital Futures Postdoc researcher in digitalization and climate impacts at the Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH working on lifecycle assessment methodology to understand various aspects of digitalization and its impacts on the environment. He continues, as a researcher, to work in this research area, building on the knowledge and experience gained from the previous project as part of his current research initiatives.

Anna Furberg defended her PhD thesis in 2020 at Chalmers University of Technology. Her thesis, titled “Environmental, Resource and Health Assessments of Hard Materials and Material Substitution: The Cases of Cemented Carbide and Polycrystalline Diamond”, involved Life Cycle Assessment (LCA) case studies and method development. After her thesis, she worked at the Norwegian Institute for Sustainability Research, NORSUS, on various LCA projects and, in several cases, as the project leader. In 2022, she was awarded the SETAC Europe Young Scientist Life Cycle Assessment Award, which recognizes exceptional achievements by a young scientist in the field of LCA. Anna became a Digital Futures Postdoc in digitalization and climate impacts at the Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH in 2023. She continues working in this area now employed as a researcher at SEED, focusing on environmental impacts of ICT with the aim to contribute with increased knowledge about potential current and future impacts of digitalization.

Nina Lövehagen joined Ericsson Research in 2000 and works as a Master Researcher focusing on the climate impacts of ICT. Her work involves understanding the energy use and greenhouse gas emissions of the ICT sector, developing methodologies to assess the enablement effect of ICT in other sectors, and creating simplified methodologies to understand the full environmental footprint of ICT. She is also active in the International Telecommunication Standardization (ITU). Nina holds an M.Sc. in electrical engineering from the Royal Institute of Technology (KTH), Stockholm, Sweden.