About the project
Objective
This research aims to extract Parkinson’s disease (PD) related temporal features from brain activity and develop brain activity-based biomarkers for diagnosis and prognosis by leveraging advances in machine learning that can form the basis for classifying PD patients.
Background
PD is the second most common neurodegenerative disorder with debilitating consequences. PD diagnosis is based on behavioural symptoms and chemical/genetic markers. Such a diagnosis can be erroneous and often does not capture the disease severity. Despite being a brain disease, the brain’s electrical activity (e.g. fMRI, EEG/MEG) is completely ignored in PD diagnosis. This oversight is intriguing, considering experimental evidence indicating that PD-related changes profoundly impact the temporal dynamics of brain activity.
While biomarkers derived from brain activity usually focus on frequency domain parameters like oscillations and coherence, temporal features have been surprisingly neglected. In fact, except for certain forms of epilepsy, usually, brain activity is not considered in the diagnosis of brain diseases. However, currently, brain activity-based biomarkers are not specific enough. This may be because these are defined in the frequency domain (e.g. oscillations and coherence). Unlike in epilepsy diagnosis, temporal features of brain activity are ignored. Therefore, in my project I would like to change this. I hypothesize that brain region-specific temporal features will provide highly specific information about the disease severity and can provide a more precise diagnosis and prognosis of PD.
About the Digital Futures Postdoc Fellow
Satarupa Chakrabarti, after completing her postgraduation in Computer Science engineering, undertook a PhD degree in Computer Science from KIIT Deemed to be University, Bhubaneswar, India, where she defended her thesis related to designing a generalized epileptic seizure detection method with different feature-extracting techniques from biosensor data that showed the presence of temporal changes. She simultaneously worked as a Junior Research Fellow (JRF) with DST-SERB, India, in an interdisciplinary project (2018–2020).
She has a strong and diverse background in research, with primary research interests in biomedical engineering, signal processing, image processing, space physics, machine learning and deep learning. Satarupa is a Digital Futures Postdoctoral Research Fellow at KTH, based in the Computational Brain Science group at the Division of Computational Science and Technology. Her research experiences involved collaboration with multidisciplinary teams and conducting research on various electrical engineering and space physics projects that led to innovative, beneficial, and cutting-edge endeavours.
Main supervisor
Arvind Kumar, Associate Professor, Division of Computational Science and Technology at KTH.
Co-supervisor
Saikat Chatterjee, Associate Professor, Division of Information Science and Engineering at KTH.
About the project
Objective
Wildfire monitoring involves two main problems, i.e., active fire detection and burnt area mapping. Active fire detection aims to find the ongoing wildfire hotspots, while burnt area mapping is expected to detect the areas affected by wildfire.
This project mainly focuses on large-scale wildfire burnt area mapping and near real-time wildfire monitoring. In view of the limited transfering performance of the existing wildfire monitoring algorithms on a larger scale and various climate zones, this project aims to develop large-scale or even globally applicable models by exploiting global coverage, multi-source satellite remote sensing data and advanced machine learning/deep learning techniques.
Background
Wildfire has coexisted with human societies for more than 350 million years, always playing an important role in shaping the Earth’s surface and climate. Across the globe, wildfires are becoming larger, more frequent, longer-duration, and tend to be more destructive in terms of lives lost and economic costs because of climate change and human activities.
To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even in real-time, to support fire-fighting and keep everything under control. Satellite remote sensing enables cost-effective, accurate, and timely monitoring of the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8, and Sentinel-1/2 satellite data opens a new era for global land surface monitoring, providing an opportunity to analyse wildfire impacts around the globe.
About the Digital Futures Postdoc Fellow
Puzhao Zhang is a research fellow at the Division of Geoinformatics, Royal Institute of Technology. He received his PhD in Pattern Recognition and Intelligent Systems from Xidian University, China, at the end of 2019. His PhD research was focused on remote sensing and deep learning for change detection.
Since fall of 2017, he has worked on wildfire monitoring with radar and optical remote sensing and deep learning as a joint PhD student at KTH. His research interests include satellite imagery analysis, change detection, machine learning, deep learning, and spatio-temporal modelling for monitoring environmental change and biomass carbon dynamics.
Main supervisor
Yifang Ban, Professor, Division of Geoinformatics, KTH.
Co-supervisor
Josephine Sullivan, Associate professor, Division of Robotics, Perception and Learning, KTH.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
This research aims to develop network monitoring technologies that provide deep, real-time insights into network activity. It focuses on designing in-hardware algorithms and tailored analysis tools to detect and analyze critical factors, including security intrusions, network anomalies, and root causes of failures.
Background
Computer networks are vital to the operation of modern industries and society. Failures and security breaches can range from costly inconveniences to catastrophes. For this reason, networks are continuously monitored to ensure reliability.
Unfortunately, the high speeds of modern networks make comprehensive monitoring difficult, leaving blind spots that hinder effective analysis. Achieving fine-grained, real-time traffic analysis could transform how intrusions are detected and failures are mitigated, enabling faster responses and enhancing network resilience.
About the Digital Futures Postdoc Fellow
Jonatan Langlet is a researcher with expertise in on-hardware monitoring algorithms and data structures. He earned his PhD in computer science from Queen Mary University of London, during which he focused on high-speed data collection, probabilistic streaming data analysis, and in-network artificial intelligence. His research interests span network programmability, systems, and algorithms, particularly on real-world deployability.
Main supervisor
Marco Chiesa, Associate Professor, Division of Software and Computer Systems, KTH.
Co-supervisor
Dejan Kostić, Professor, Division of Software and Computer Systems, KTH.
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
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.