Working with humans is incredibly difficult for robots. This project aims to develop new ways for robots and intelligent agents to understand, interact with, and adapt to the complex and collaborative ways we humans work together to get through our everyday lives.

Can AI help us come to terms with fraud? In this project, we use machine learning models to identify indicators for unclear insurance claims in order to deter suspicious damage payments and insurance fraud.

Digital twins and artificial intelligence are two of the key driving technologies of the fourth industrial revolution. This project connects the two fields in an industry–academia collaboration between Stockholm University and Atrium Ljungberg.

The shift towards renewable energy comes with technical challenges. We investigate how AI-algorithms can be used in the electric networks of our cities for smart coordination and control of the more volatile, renewable energy resources.

New data-driven solutions have the potential to improve the situation for co-morbid elderly people. In this project we identify, analyze and pilot test such innovative data-driven solutions for elderly by supporting collaboration between care providers.

With massive amounts of personal data being generated, privacy has become a great challenge. This project studies how machine learning can be used for sharing language models without risking to share information that may identify individuals.

Are you interested in breaking historical ciphers? Check out our project aiming at the automatic analysis and decipherment of historical secret writings.

AI and data-driven autonomous decision-making will play a leading role in the digitalization of critical infrastructures for electricity, water, and transportation. This project explores our future smart societies.

This project is aimed at elderly with multiple illnesses. We identify and design innovative services, based on data-driven solutions. The goal is to proactively prevent mistreatment in health care and re-enrollment in advanced hospital care.

This action research project will transform mental healthcare in Uganda. We develop a digital solution to facilitate care and support, and give access to expertise to those most in need. Solutions will be modelled on Swedish systems, but informed by a local needs assessment to ensure relevance in Uganda and user centred care.

The focus of this PhD thesis project is on distributed intelligence in the Internet of Things (IoT), edge computing, and distributed computing. Currently, we are investigating how to optimize the architecture of the network at the edge of IoT to provide more efficient distributed intelligence.

The e-SHARE project explores electronic identification for patient centric e-health services, specifically in Brazil. They are compared and contrasted with similar services in Sweden and Europe in general.

Electronic health records are full of important information about diagnoses and treatments of patients. Sharing this information in a federated health data network, across hospitals in the Nordic countries, will improve the quality of health care.

The aim of this project is to enable the practical use of methods for de-identification of clinical free text written in Swedish. The HB Deid tool will be tested on textual patient data from Region Stockholm and not only in a laboratory environment at Stockholm University.

Medical devices collect huge sets of patient data every day, at every hospital, around the world. What if these data could be used to improve diagnostic tools? This PhD thesis project explores how medical data can be used in a safe way.

Machine learning has been developed and adopted in different areas where both accuracy and interpretability is required. However, most high-performance models lack interpretability. Therefore, additional algorithms are required to guarantee the complex models’ interpretability.

How can technical solutions help medical doctors make more accurate diagnoses, generate prognoses and estimate the probability of various outcomes? In this project, we use machine learning and language technology to develop prediction models for sepsis and COVID-19.

Internet of Things (IoT) allows machines to communicate directly with other machines, without involving humans. This technology provides excellent opportunities to do things faster and smarter. But it also raises questions concerning security and privacy.

The purpose of the project is to develop and evaluate scientifically based pedagogical approaches, methods and tools within the domain of air combat training, to systematically plan, evaluate and follow up training and training results.

How can patients with severe and long-term cognitive problems after Covid-19 be rehabilitated? In this project, we design an online-rehabilitation system. It helps patients to better understand their problems, the system supports their cognitive training, and increases their motivation to work with rehabilitation online.

With better descriptions of a patient’s state and history, more efficient recommendations can be provided. We explore how AI tools can be put to practical use in healthcare. We focus on complex and multimodal data and use cases such as COVID-19 public health interventions or patient phenotyping for adverse drug events, sepsis, or cancer.

Autism is one the most common neurodevelopment diagnoses, and it implies severe disabilities for many people. This project is about creating a platform to help people with autism to be more independent in everyday life situations.

Recent breakthroughs in AI have been driven mainly by large language models. While they can be very useful, they also threaten privacy – they leak private information. This project aims to identify these risks and develop privacy-preserving techniques.

Less accidents on the road, and more operational uptime. That is the expected outcome of this research project which uses data from trucks to develop new machine learning models. The models will let us know when maintenance is needed – before the vehicle breaks down.

In this project, we investigate the next-generation distributed AI and Machine Learning algorithms in complex networks. Our aim is to find more sustainable solutions.

During the COVID-19 pandemic, the use of digital assessment in higher education increased dramatically. What are the teachers’ experiences from this? And what are the key opportunities and challenges in designing and implementing digital assessment?

This project aims to produce a concrete and scalable example of how IoT (Internet of Things) can be used to develop teaching and learning in schools. By putting sensors on teachers, we can produce heatmaps of their classroom movements. This shows how they interact with students, and gives important feedback to their teaching. 

Many existing digital applications leverage machine learning algorithms to improve their solutions. This project explores how these algorithms can also help understand user behaviour in virtual reality environments to design more effective systems.

Recent developments in AI affect us in many ways, also in higher education. The research project "WASP-ED work area 4: Pedagogical development and learning analytics" is a part of the national WASP-ED program which aims to increase AI capability and capacity in Sweden.