About the project
Objective
This project aims to propose innovative distributed learning methods based on adaptive gradient coding techniques. Within this framework, workers’ participation is fluidly adjusted in real-time during training to enhance learning performance under practical constraints. We will offer rigorous theoretical proofs to ensure the convergence of the proposed methods, solidifying their reliability. We will also test the performance of the proposed methods on both simulated and actual datasets in real-world scenarios. This evaluation will benchmark the effectiveness of our techniques and underscore their superiority over current practices.
Background
In the framework of distributed learning, a central server aggregates computational results from various workers to update the trained model. However, in practical scenarios, “stragglers”—workers who are slow or unresponsive—can significantly impede overall training time. Addressing these slowdowns is crucial for real-time processing requirements in the healthcare and smart transportation sectors.
While current distributed learning methods employ gradient coding to mitigate the effects of stragglers, they rely on a fixed number of the fastest workers throughout the entire training process, which have limited flexibility in balancing training time and loss. Based on that, our research question is how to transcend the limitations inherent in existing distributed learning methods and to reduce the training time required to achieve a specified training loss.
About the Digital Futures Postdoc Fellow
Chengxi Li received a PhD in 2022 from the Department of Electronic Engineering at Tsinghua University and a bachelor’s degree in 2018 from the University of Electronic Science and Technology of China. Her research interests lie in distributed learning, federated learning, signal processing and information theory.
Main supervisor
Mikael Skoglund, Professor, Head of Department, Division of Information Science and Engineering, EECS, KTH.
Co-supervisor
Ming Xiao, Professor, Division of Information Science and Engineering, EECS, KTH.
About the project
Objective
The goal of the project is to design reactive, intelligent planning and control algorithms for underwater vehicles which quantify and reason about risk as well as incorporate machine learning. This will enable the use of AUVs for more autonomous environmental data collection with reduced human involvement and, therefore, reduced human risk.
Background
Autonomous underwater vehicles have great potential for environmental monitoring and exploration, but there are important technical challenges that prevent their widespread use. Some of the major challenges are that GPS location information is not available underwater, communication underwater is limited, and there may be significant vehicle drift due to local hydrodynamic disturbances. As a result, it is difficult to ensure high levels of reliability for these vehicles. To bridge the reliability gap, this project aims to design and test planning and control algorithms that explicitly reason about uncertainty and produce intelligent policies to minimize that uncertainty while gathering information about the vehicle’s environment.
About the Digital Futures Postdoc Fellow
Chelsea Sidrane began her studies with a Bachelor’s degree in mechanical engineering at Cornell University, where she developed an interest in dynamical systems and control theory. She went on to study machine learning and robot planning in her Master’s studies at Stanford University before beginning a PhD in the Stanford Intelligent Systems Laboratory focused on verifying neural networks. She defended her thesis, “Neural Network Verification for Nonlinear Systems”, in the summer of 2022. She is now a Digital Futures Postdoctoral Research Fellow at KTH based in the Planiacs group at the Division of Robotics, Perception and Learning (RPL).
Main supervisor
Jana Tumova, Associate Professor in the Division of Robotics, Perception and Learning, KTH
Co-supervisor
Ivan Stenius, Associate Professor in Vessel Engineering & Solid Mechanics, KTH
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This project aims to engineer the rules by which intelligent robots interact with each other in public spaces. In particular, this project focuses on tools from the field of mechanism design, which strives to set rules for interaction between rational agents. Designing rules via mechanism design allows for systems where robots collaborate toward global goals, even when their individual goals and specifications differ. Mechanism design, coupled with tools from formal methods and planning, can help achieve global goals like safely sharing resources, minimizing the risk of failures in uncertain systems, and engendering the trust of humans.
Background
Multi-robot planning is a complex optimization problem that must consider both the goals of each robot and the interaction between those goals. The first step toward solving this problem requires understanding the realities of modern-day robots. The second step requires meta-reasoning through game theory. This problem becomes more complicated with the introduction of humans, who interact with robots in unique and often unpredictable ways.
About the Digital Futures Postdoc Fellow
Anna Gautier is a postdoctoral researcher in artificial intelligence and robotics. She works in the Robotics, Perception and Learning division at KTH Royal Institute of Technology. Anna obtained her PhD from the University of Oxford in July 2023, with a thesis entitled “Resource Allocation for Constrained Multi-Agent Systems.” Her research interests include multi-agent systems, human-robot interaction, planning under uncertainty, formal methods and mechanism design.
Main supervisor
Jana Tumova, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Co-supervisor
Iolanda Leite, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Watch the recorded presentation at the Digitalize in Stockholm 2023 event
About the project
Objective
This research will develop design considerations and policy recommendations for designing with and applying (inter)personal and intimate data. The project will follow a participatory approach centered around people’s experiences interacting with and sharing intimate technologies that collect and store (inter)personal data.
Background
In today’s digital society, most people routinely interact with connected products and services that collect and indefinitely store personal data. Increasingly, these products and services — and the data they produce — permeate intimate spaces, such as smart vibrators collecting sensor data about people’s arousal and orgasm, and AI romance chatbots collecting self-reported information about people’s mental and sexual health. Moreover, these intimate spaces are often shared and relational. For instance, a connected voice assistant in a shared household collects data from the primary user, other household members, and even occasional visitors. Thus, data becomes both intimate and (inter)personal, shaped by and shared across (inter)personal relationships around shared experiences and spaces.
These characteristics raise critical questions for data, design, and policy. Although connected intimate technologies — and the data they produce — are often designed for individual use(rs), their use is often shared and relational: How can we design intimate technologies that empower their users to care for and share their data? Similarly, regulations such as the GDPR established several rights to empower individuals to control their data, such as the right to access: Who should access (inter)personal data?
About the Digital Futures Postdoc Fellow
Alejandra Gómez Ortega is a Design and Human-Computer Interaction researcher. She holds a PhD in Industrial Design Engineering from the Delft University of Technology in The Netherlands. Her research investigates individual experiences interacting with and sharing intimate data, privacy perceptions and considerations around data, and data themselves through playful and creative approaches. Alejandra has applied various methods and approaches in her design research journey, including Participatory Design and Research through Design. Alejandra enjoys designing, developing, deploying, and exhibiting provocative artifacts and digital prototypes that enable individuals and communities to experience a specific situation as a starting point for reflection and discussion.
Main supervisor
Airi Lampinen, Associate Professor, Department of Computer and Systems Sciences (DSV), Stockholm University.
Co-supervisor
Madeline Balaam, Professor, Division of Media Technology and Interaction Designs, KTH.
About the project
Objective
The aim of this project is to analyse the environmental impacts of increased digitalization and the use of Information and Communication Technologies. The project can include both method development and case studies. The impacts will be analysed using life cycle assessment and life cycle thinking. Case studies can vary on different scales and include specific devices, applications and sectoral assessments. Initially, the focus will be on climate impacts and energy use, but it may also be broadened to a larger spectrum of environmental impacts. Assessments will include the direct impacts of ICT but also different types of indirect impacts, including rebound effects.
Background
The ICT sector has an environmental footprint. The future development of this footprint is debated, and it is important that the discussions have a scientific basis. Digitalisation may be a tool for reducing environmental impacts. By improving efficiencies and dematerialising products and services, new ICT applications can reduce the footprints of other sectors. More studies are, however, needed in order to understand when this actually leads to decreased impacts and when there is a risk for indirect rebound effects that increase use and footprints. Environmental life cycle assessment is a standardised method for assessing potential environmental impacts of products, services and functions “from the cradle to the grave”, i.e. from the extraction of raw materials via production and uses to waste management. It is used for analysing the environmental footprints, i.e. the direct impacts, of ICT. It can also be used for analysing different types of indirect effects.
Partner Postdocs
After working in the industry on large-scale refrigeration and heat pump systems and as an entrepreneur with solar pumps, Shoaib Azizi undertook a master’s program in Sustainable Energy Engineering at KTH. He moved to Umeå in northern Sweden for a multi-disciplinary PhD project on energy-efficient renovation of buildings. His PhD included research on the opportunities for digital tools to improve management and energy efficiency in buildings. He defended his thesis “A multi-method Assessment to Support Energy Efficiency Decisions in Existing Residential and Academic Buildings” in September 2021. Now Shoaib is a Digital Futures Postdoc researcher in digitalization and climate impacts at the Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH. His research involves lifecycle assessment methodology to understand various aspects of digitalization and its impacts on the environment.
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 has a Digital Futures Postdoc position in digitalization and climate impacts at the Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH.
Supervisor
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.
About the project
Objective
The SENZ-Lab project develops and validates a cost-efficient, real-time, dynamic sparse sensing approach for urban traffic monitoring and environmental footprint assessment in Stockholm’s Environmental Zone Class 3. Using acoustic sensors and AI-driven modelling, it seeks to establish a 2D digital twin of the city’s traffic, enabling real-time monitoring of noise, air pollution, and vehicle-level activity. The goal is to enhance traffic management, reduce emissions, and support sustainable urban mobility.
Background
As cities expand, noise and air pollution pose significant health risks. Traditional monitoring methods struggle with real-world complexity, requiring new solutions. Building on previous research in Stockholm’s Hornsgatan innovation zone, this project integrates IoT, AI, and real-time traffic simulations to improve monitoring accuracy and inform urban policy. The initiative aligns with Stockholm’s environmental goals and KTH’s strategic pillars of sustainability and digitalization.
Crossdisciplinary collaboration
The project brings together experts from multiple fields, including urban sensing, traffic modeling, AI, and GIS-based visualization. The consortium includes two research teams at KTH specialized in Acoustics and Geoinformatics, supported by the City of Stockholm, combining academic research with real-world urban planning needs. By integrating cutting-edge technology with policy-driven insights, the project provides practical solutions for creating quieter, healthier, and more sustainable cities.
About the project
Objective
Mediverse will showcase how clinical and medical researchers can integrate multimodal health data into a unified knowledge graph. Our showcase will give a live example of advanced data exploration, complex medical searches, and predictive analytics across diverse modalities, such as genomic data, clinical studies, and medical imaging. Leveraging Graph Neural Networks (GNNs) and black-box uncertainty estimation, Mediverse aims to also establish trust in AI-driven healthcare insights, accelerating medical discovery while ensuring transparency and reliability, two instrumental needs in the health sector.
Background
Health data is typically fragmented and siloed across various institutions and data formats, making integration and large-scale analysis highly challenging. Despite regulatory efforts, the problem tends to worsen over the years. At the same time, existing approaches to interoperability struggle to capture the full spectrum of clinical relationships across different medical domains. Mediverse addresses these challenges by embracing data diversity. At its core, our method is based on a unified future-proof hierarchical model built using knowledge graph representations that link diverse healthcare datasets, enabling seamless interaction and in-depth exploration. By incorporating state-of-the-art graph representation learning techniques, Mediverse facilitates cross-ontology mapping, providing a powerful tool for clinicians and researchers to uncover previously inaccessible insights.
Crossdisciplinary collaboration
Mediverse is a collaboration between KTH’s Data Systems Lab (EECS) and the Center for Data-Driven Health (CBH). It brings together valuable expertise in health informatics, knowledge graph modeling, machine learning, and data systems engineering. The project is further strengthened by partnerships with Karolinska Institute, Region Stockholm, and the private sector, enabling real-world validation and deployment within Sweden’s healthcare ecosystem.