About the project
Objective
This project aims to improve a Non-Axiomatic Reasoning System design and combine it with state-of-the-art Deep Learning models for perception. This allows the system to be applied in real-world environments, intending to enhance the autonomy of robots where human intervention is to be kept at a minimum. Application-wise, the system is expected to autonomously perform inspection and maintenance operations of city infrastructure such as power plants. This will ultimately lead to new digitisation technology, which can help solve environmental and societal problems.
Background
The human’s ability to reason has evolved to adapt to difficult situations and changes in the environment faster than current AI models allow. Animals that reason effectively outsmart other species and gain key survival advantages. Non-Axiomatic Reasoning can explain most of these cognitive abilities and provides a roadmap for cognitive enhancements based on psychological and neuroscientific insights. Also, Learning can be explained as inductive reasoning using Non-Axiomatic Logic, an aspect most reasoning systems lack while being a key aspect of intelligence.
About the Digital Futures Postdoc Fellow
Patrick Hammer is a postdoc researcher at Stockholm University, Department of Psychology, working with Robert Johansson and Pawel Herman. Before joining Stockholm University, he got his PhD in Computer Science (AI track) at Temple University, Pennsylvania, United States, where he was a full-time research assistant of Pei Wang. His research interests include Artificial Intelligence, Reasoning Systems, Autonomous Robots, Machine Learning, Deep Learning and Cognitive Science.
Main supervisor
Robert Johansson, Associate Professor at Stockholm University.
Co-supervisor
Pawel Herman, Associate Professor, Computer Science, Division of Computational Science and Technology at KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
In this project, we shall develop, implement, and evaluate a mobile health (mHealth) platform for educating patients about cancer and mental illnesses in Uganda, linkage of patients to peer support workers (“expert patients” and survivors), and collecting patient-reported outcomes (e.g. self symptom assessment and quality of life surveys). We shall follow a design science research approach and principles of user-centred design. We shall use familiar and feasible technologies such as SMS, USSD and IVR. The health information content and communication flows shall also be developed and iteratively evaluated with the target users of the system to ensure it is contextually appropriate and correctly translated. Evaluation of the project will be qualitative and quantitative, including assessment of usability, fidelity, and the clinical impact, such as the impact of the intervention on patient self-efficacy, loss to follow-up, quality of life, and satisfaction with care.
Background
The health and economic development challenges of infectious diseases in Africa and other LMICs are well recognized. Controlling these infectious diseases (especially HIV/AIDS, Malaria and Tuberculosis) in Africa has thus been the priority for many national and global players, such as the US CDC and PEPFAR, the Bill and Melinda Gates Foundation, and the Global Fund. Consequently, significant progress has been made in the past decades in controlling infectious diseases in Africa. In contrast, non-communicable diseases (NCDs) such as cancer and mental illnesses have remained under-prioritized.
Today, cancer kills approximately 10 million people per year globally. This is more than deaths from HIV/AIDS, Malaria, Tuberculosis, and COVID-19 combined. In Africa, the ongoing socio-economic transitions (urbanization, ageing population, and westernization of lifestyles) are escalating the cancer burden (incidence predicted to rise 38% over the next decade) faster than in any other part of the world. Similarly, approximately one out of every four persons globally has a mental disorder, leading to over 8 million deaths and about 2.5 trillion US dollars lost in the loss of productivity. In Africa, 85% of people with mental illnesses do not have access to the necessary healthcare. Disruptions in healthcare, e.g. due to COVID-19, as well as social inequalities and marginalization, further exacerbate the problem.
Mobile phones are ubiquitous in Africa and have allowed leapfrogging of technological limitations. Mobile solutions are accelerating finance (mobile money), the energy sector (pay-as-you-go solar mobile solutions), and agriculture (access to market prices, micro-insurance), among others. Mobile technologies in healthcare (mHealth) are also gaining traction in Africa with a demonstrated positive impact on patient treatment adherence, provision of health education and awareness to the general public, data collection and reporting, drug supply chain and stock management, and disease surveillance. However, most implementations have been isolated pilots, focused on infectious diseases, and lacked robust evaluation methods. Most evaluations have focused on feasibility, usability and acceptability, with limited focus and evidence on clinical outcomes.
About the Digital Futures Postdoc Fellow
Johnblack K. Kabukye is a medical doctor and health informatics specialist at the Uganda Cancer Institute in Kampala, Uganda. He did a Bachelor of Medicine and a Bachelor of Surgery from Makerere University, a Master of Science in Health informatics from Karolinska Institute and Stockholm University, and a PhD in medical informatics from the University of Amsterdam.
His research interests are designing, implementing and evaluating digital health solutions for healthcare providers and patients in developing countries, including electronic medical records, patient advice telephone lines, and telehealth and artificial intelligence-enabled apps to support cervical cancer screening.
Main supervisor
John Owuor, PhD, Director, SPIDER Department of Computer and Systems Sciences (DSV) Stockholm University.
Co-supervisor
Susanne Nilsson, Researcher, Integrated product development and design, Machine design, School of Industrial Engineering and Management, KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
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
Dimos Dimarogonas, Professor in the Division of Decision and Control Systems, KTH
Ivan Stenius, Associate Professor in Vessel Engineering & Solid Mechanics, KTH
Co-supervisor
Anna Ståhl, Senior Researcher, Digital Systems, RISE
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This project aims to design and develop shape-changing textile devices that generate rich and dynamic body-centered interactions for children. Physical interactions play a vital role in children’s well-being and contribute significantly to their mental, physical and emotional development. Yet, current digital technologies often prioritise visual and auditory senses over tactile or movement-based modalities. As children’s lives become increasingly mediated by technology, we wish to explore and encourage the design of technologies that enable children (and adults too) to express themselves fully and engage in meaningful physical interactions within tech-mediated environments. We will do this by conducting co-design workshops with children and engaging in soma design methodologies that foreground the body and lived experience in the design process.
Background
This research intersects the emerging fields of soft robotics, e-textiles, soma design and machine learning. It will draw from fabrication techniques in soft robotics and e-textiles to produce shape-shaping textile artefacts that offer versatile on- and off-body interactions. Drawing from the field of machine learning and recent work in Human-Robot Interaction, we will explore ways to interpret sensor data from the devices, enabling the generation of responsive, context-aware behaviours of the interfaces. On the design side, we take a soma design approach to develop technologies that promote genuine connection, using strategies such as mediated touch and shared embodied experiences.
About the Digital Futures Postdoc Fellow
Alice Haynes studied Engineering Mathematics at the University of Bristol, UK, going on to specialize in soft robotics – the development of robotic devices made of soft materials – and haptic interfaces – the development of devices that can produce tactile feedback. Alongside her research, she worked in a local arts charity called KWMC, co-facilitating workshops and teaching fabrication skills in their community makerspace. After defending her PhD thesis in 2022, she moved to Germany for a postdoctoral position in the Human-Computer Interaction Lab at Saarland University. There she explored techniques for fabricating shape-changing textiles that could move and adapt to the body and environment. Increasingly interested in the role of our body and felt experience in interactions with such soft, tactile interfaces, she is excited to bring a soma design approach to this project.
Main supervisor
Kristina Höök, Professor, Division of Media Technology and Interaction Design, KTH
Co-supervisor
Iolanda Leite, Associate Professor, Division of Robotics, Perception and Learning, 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 purpose of this project is to develop better methods for reconstructing time-resolved medical images with multiple image channels. Traditional methods often process these measurement subsets separately and then combine the results, but this approach doesn’t always lead to the best images. The challenge is to integrate the extra information from the start, during the image reconstruction process itself, in a way that enhances the final result. This is where artificial intelligence (AI) and deep learning come in.
Deep learning models have shown great promise in tackling complex tasks by learning patterns from large amounts of data. However, in medical imaging, data is often scarce, and the computational challenges are significant. A fully data-driven approach is unlikely to succeed. Instead, our project will explore ways to build AI models that incorporate the known relationships between measurement subsets directly into their design. This will allow us to develop efficient and lightweight models that improve image quality while remaining practical to use in real-world clinical settings.
By applying these new methods to spectral CT and PET, we aim to produce medical images with greater diagnostic power, helping doctors detect and treat diseases more effectively. Additionally, our approach will be designed in a flexible, “plug-and-play” manner, so that it can be adapted for other types of imaging in the future. With this research, we hope to take an important step toward more accurate, reliable, and informative medical imaging for patients and healthcare providers alike.
Background
Medical imaging techniques like Computed Tomography (CT) and Positron Emission Tomography (PET) allow doctors to see inside the human body without surgery. However, these images are not captured directly like a photograph. Instead, they are computed from indirect measurements of light particles (photons) passing through the body.
Imagine looking at the shadows cast by an object in different directions and trying to piece together what the object looks like in three dimensions. This is similar to how medical images are reconstructed from projection data. Some examinations build on acquiring multiple image channels, such as in spectral CT where X-ray images are acquired at multiple energy levels, or combining different modalities such as PET and CT. It is also possible to acquire a video sequence of multiple images in rapid succession. Combining these different kinds of information has the potential to improve image quality, making diagnoses more accurate, but how this should be done in an effective way is far from a simple question.
Cross-disciplinary collaboration
Developing novel medical imaging methodology is a highly cross-disciplinary activity which requires involvement of physics, mathematics, computer science, engineering, and medical science. In this project, which is a collaboration between the department of physics (SCI), the department of biomedical engineering and health systems (CBH), and the department of mathematics (SCI) at KTH, we bring together expertise in mathematics and in two different imaging modalities, CT and PET, to develop common methodology that can be applied to multiple medical imaging modalities.