About the project

Objective
This project explores the transformation of education by working with and learning from students and teachers with cognitive disabilities. It is part of a larger endeavour to understand how education empowers and takes in diverse people’s experiences. We focus on the digitalisation of education and how digital tools can be better used to respond to the experiences and needs of students and teachers with cognitive disabilities. Implemented as an action research project, the project’s aim is two-fold: 1) Research-informed changes to higher education for greater inclusivity and 2) knowledge of this process and the experiences of those involved. We will conduct focus group meetings and “shadow” students and teachers with cognitive disabilities to learn about their experiences and needs and collect ideas for change.

Background
About 10% of the students at KTH, and about 33% of the students getting support from the student support administration, are registered as having cognitive impairments such as dyslexia, autism or ADHD. More students are assumed to have “lived experience” of cognitive impairments. While there is quite some research on students, we know little about teachers with cognitive impairments. In light of our great sustainability challenges, education is transforming to become more relevant for sustainability—equality and high-quality education for all our sustainability goals. More so, education has a great potential to promote just societal transformation if it becomes a space where diverse people can participate and are valued.

Crossdisciplinary collaboration
The project spans research in education and human-computer interaction at KTH. Further, several organisations at KTH participate in the collaboration (administration, student union, equal opportunity and sustainability office) and the organisation “Begripsam” in Stockholm. Through different channels, we will invite all students and teachers to participate in the project, especially those with lived experiences of cognitive impairment, to develop future education for all. PI Jan Gulliksen focuses on education and research for usability and accessibility, User-centred systems design, digitalization and digital work environments. Co-PI Anne-Kathrin Peters focuses on education for sustainability, especially equality, diversity, and justice.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Background and summary of fellowship
Power electronics technology enables efficient electricity usage by controlling electronic devices with digital algorithms. The software-controlled, power-electronic converters have been vastly used in modern society and become a transformational technology for the energy transition. The proliferation of power-electronic converters transforms legacy energy systems with more flexibility and improved efficiency, yet it also brings new security challenges to energy systems. In recent years, power disruptions induced by erratic interactions of converter-based energy assets have been increasingly reported. Methods for the dynamics analysis of power electronic systems are urgently needed to screen instability and security risks in modern energy systems.

This project aims to leverage digital technologies to redefine the paradigm of dynamics analysis for power electronic systems. First, a trustworthy artificial intelligence (AI) modelling framework for converter-based energy assets will be established. Physical-domain knowledge will be combined with the recent advances in machine learning algorithms to make the AI model more reliable. Then, based on the AI models of power converters, a scalable and efficient dynamics analysis approach will be developed for power electronic systems, ranging from single converters to hundreds of thousands of converters. Finally, physics-based models of benchmark energy systems will be built to test the effectiveness of developed models and methods.

Research in the area of power electronics-controlled power systems. Wang is active in the broader community working in the area and will bring further visibility and provide strong leadership.

Xiongfei Wang has been a Professor with the Division of Electric Power and Energy Systems at KTH Royal Institute of Technology since 2022. From 2009 to 2022, he was with the Department of Energy Technology, Aalborg University, where he became an Assistant Professor in 2014, an Associate Professor in 20

Background and summary of fellowship
Soma design puts first-person aesthetic sensory experience and expertise in the front seat during the design process. It builds on the theories of somaesthetics – a combination of soma—our subjective self, body, emotion, and thinking—and aesthetics—our perceptual appreciation of the world. Through engaging with and deepening your capacity to discern sensuous experiences, you can examine and improve on connections between sensation, feeling, emotion, and subjective understanding and values. Soma design is a path to imagine – through your senses, movements and material encounters – what could be during a design process. A soma design process thrives off the aesthetic potential of the sociodigital materials and the creative process of shaping these into dynamic gestalts, orchestrated experiences.

Here, Höök is exploring two strands of work:

First, digital touch: exploring the aesthetic potential of bodyworn digital/physical materials to evoke and feel touch. It is an interdisciplinary project combining competencies: in body-worn soft and compliant sensor nodes; high-pressure microfluidics and miniaturized actuators; and their materials science; backscatter sensor-actuator network technology; and interaction design addressing touch through intimate correspondence relationships through soma design.

Second, ethics: the goal is to contribute new knowledge to the field of design in the form of an analytical, practical and pragmatic framework to grasp the ethicality of emerging, complex, felt, bodily practices that shape our corporealities and through which ethics are enacted – manifested and cultivated through our moving bodies.

Background and summary of fellowship
Over the last decade, academia and the industry of networked systems have become more and more interested in novel real-time applications. These applications arise for one in the area of Cyber-Physical Systems (CPS) where essentially time-sensitive processes are to be governed by direct actuation. On the other hand, these applications also arise in the context of providing automated feedback to human users, for instance, in augmented reality as well as cognitive assistance. Such interactive applications are very powerful with respect to their future implications for professional education, ambient intelligence as well as leisure. It is therefore likely that they will have a profound impact on networked systems. Nevertheless, from a fundamental perspective, we have very little understanding of the efficient operations of networked systems for such interactive applications today.

The goal of this project is to provide fundamental performance models for these interactive applications and the operation of underlying networked systems. In contrast to state-of-the-art, our key approach is to capture the essential trade-offs through a novel notion of utility of the received information over time, and subsequently to strive for system optimizations. Central to our application are novel sampling policies, which we derive by leveraging Markov Decision Processes. By this, we aim at providing a cornerstone for the design of future networked systems exposed to interactive applications.

Background and summary of fellowship
Sarunas Girdzijauskas’ research interests are on the intersection of distributed systems and machine learning fields and fall under “Cooperate” and “Learn” research themes, addressing “Smart Society” as well as “Rich and Healthy Life” societal contexts of Digital Futures Strategic Research Programme.

There are many societal problems plaguing current AI services provided by modern Big Tech behemoths, which collect and process user data in a centralized manner. Such data collection and processing inevitably leads to a wide spectrum of issues from data privacy, system security to severe scalability and power consumption issues. Sarunas Girdzijauskas’ research focuses on solutions enabling the transition from classical centralized machine learning to Federated and Decentralized Machine Learning technologies. A particular focus is on developing decentralized architectures for graph analytics and graph machine learning which would enable a wide range of current AI services (e.g., product recommendation systems, social network news feeds etc.) to be provided without the need of centrally collecting data.

Background and summary of fellowship
As data generation increasingly takes place on wireless IoT devices, Artificial Intelligence and Machine Learning (AI/ML) over the Internet of Things (IoT) wireless networks becomes critical. Many studies have shown that state-of-the-art wireless protocols are highly inefficient or unsustainable to support AI/ML services. There is a consensus in the forefront research communities that AI/ML for the connected world is at its infancy and much will have to be investigated in the next decade. In this research project, I will follow a research plan dived into roughly three open research sub-directions:

1. Theoretical foundations of distributed AI/ML: I will contribute to making AI/ML theory aware of the characteristics of the wireless networks, and will fundamentally rethink it.
2. Theoretical foundations of AI/ML to design wireless networks: I will contribute to radically redesign by AI/ML the future communication protocols for critical societal applications, due to the deficiencies of model-based methods. This includes also the optimisation of the current wireless protocols using AI/ML, which is at the very beginning.
3. Theoretical foundations to redesign wireless for supporting AI/ML services: future wireless networks will have to support pervasive AI/ML services. Current communication protocols are highly insufficient for such purposes. I will contribute to establishing fundamentally new wireless protocols and theories, such as “over the air function computations”, to support AI/ML services over IoT.

About the project

Objective
We propose to build a physical prototype of a modular lower-limb exoskeleton system with a digital interface to a real-time variable controller. The prototype will be equipped with motors that provide variable control to different joints and joint ranges of motion while supporting real-time control of its kinetic properties. By varying the assistive strategies in the exoskeleton system via a digital interface, we will enable human-in-the-loop (HILO) optimization to find optimal control strategies for different users and goals.

Background
Persons with physical disabilities are the largest minority group in the world, and global trends in ageing populations indicate an expected increase in the population affected by disability. In Sweden, musculoskeletal disorders are one of the most common causes of long-term disability.

Wearable robotic assistive exoskeletons have rapidly developed in the past decades, yet only a handful of products are used frequently, either within or outside research environments. A major reason is that a device must be adjusted for user compliance and efficacy for optimal effect and comfort. Using methods for automatically discovering, customizing, and continuously adapting assistance could overcome these challenges, allowing exoskeletons and prostheses to achieve their potential.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Engineering Science, Department of Engineering Mechanics and KTH School of Industrial engineering and management, department of Machine Design.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event: