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.

About the project

Objective
The main objective of this project is to improve the productivity of two packaging lines within the SweOps Steriles function, as measured by Overall Equipment Effectiveness (OEE) and competence in line staffing. We aim to achieve the objective by enabling next-best-action decision support for front-line operators.

Background
Modern pharmaceutical packaging lines are complex systems with multiple intricate physical and digital components. Operators gain domain expertise through extended exposure and interaction with the systems. They see, touch, and listen to the operating parts of the system. With time, they develop deep procedural knowledge and reach the level where they can predict when the physical systems need maintenance. How do they do this? This is the basic question of the project SMART: Smart Predictive Maintenance for the Pharmaceutical Industry, a collaboration between AstraZeneca and KTH.

Our approach deploys three pillars: 1) sensor networks in manufacturing, 2) machine learning predictive models, and 3) interactive immersive and contextual visualizations. We will observe and interview expert operators to acquire their procedural knowledge and focus on the sensing and machine learning tools to produce a rich sensor-based predictive model that we visualize peripherally in the plant and immersively to the operators. The project aims to enhance the operators’ abilities to perform predictive maintenance and expedite the transfer of these skills to novice operators via novel digital tools.

Partner Postdocs

Main supervisors

Background and summary of fellowship
Behaviour Trees (BTs) represent a hierarchical way of combining low-level controllers for different tasks into high-level controllers for more complex tasks. The key advantages of BTs have been shown to include the following:

In this project, we will use the properties of BTs listed above to synthesize controllers that combine the efficiency of reinforcement learning with formal performance guarantees such as safety and convergence to a designated goal area.

Background and summary of fellowship
Reinforcement Learning (RL) is concerned with learning efficient control policies for systems with unknown dynamics and reward functions. RL plays an increasingly important role in a large spectrum of application domains including online platforms (recommender systems and search engines), robotics, and self-driving vehicles. Over the last decade, RL algorithms, combined with modern function approximators such as deep neural networks, have shown unprecedented performance and have been able to solve very complex sequential decision tasks better than humans. Yet, these algorithms are lacking robustness, and are most often extremely data inefficient.

This research project aims at contributing to the theoretical foundations for the design of data-efficient and robust RL algorithms. To this aim, we develop a fundamental two-step process:

  1. We characterize information-theoretical limits for the performance of RL algorithms (in terms of sample complexity, i.e., data efficiency)
  2. We leverage these limits to guide the design of optimal RL algorithms, algorithms approaching the fundamental performance limits

Background and summary of fellowship
In many application areas, it is not sufficient to present the output of machine learning models to the users without providing any information on what leads to the specific predictions or recommendations and how (un)certain they are. The strongest machine learning models are however often essentially black boxes. In order to enable trust in such models, techniques for explaining the predictions in the form of interpretable approximations are currently being investigated. Another cornerstone for enabling trust is that the uncertainty of the output of the machine learning models is properly quantified, e.g., that the output prediction intervals or probability distributions are well-calibrated.

Motivated by collaborations with Karolinska Institutet/University hospital on sepsis prediction, Scania on predictive maintenance and the Swedish National Financial Management Authority on gross domestic product (GDP) forecasting, techniques for quantifying uncertainty and explaining predictions will be developed and evaluated. In addition to scientific papers, the output of the project will be Python packages to support reliable machine learning, enabling predictions of state-of-the-art machine learning models to be complemented with explanations and uncertainty quantification.

Background and summary of fellowship
Social robots and virtual agents are currently being explored and developed for applications in a number of fields such as education, service, retail, health, elderly care, simulation and training and entertainment. For these systems to be accepted and successful, not only in task-based interaction but also to maintain user engagement, in the long run, it is important that they can exhibit varied and meaningful non-verbal behaviours, and also possess the ability to adapt to the interlocutor in different ways. Adaptivity in face-to-face interaction (sometimes called mimicry) has for example been shown to increase liking and affiliation.

This work addresses how style aspects in non-verbal interaction can be controlled, varied and adapted, across several modalities including speech, gesture and facial expression. The project entails novel data collection of verbal and non-verbal behaviours (audio, video, gaze tracking and motion capture) with rich style variation, but also makes use of existing datasets for base training. Synthesis models trained on this data will be based primarily on deep probabilistic generative modelling, conditioned with relevant style-related parameters. Multimodal generation paradigms, that produce congruent behaviours in more than one modality at a time, e.g. both speech and gesture, in a coherent style, will also be explored and evaluated in perceptual studies or experiments with real interactive contexts.