About the project
Objective
With the OrganoFeed project, we aim to leverage our joint expertise regarding microfluidic engineering & integration, and predictive algorithms development, to help address a core problem in biomedical research: reproducibility. Specifically, we aim to greatly reduce the variability of organoid cultures, which otherwise hold great promise for improving both fundamental research and drug development, by shifting the paradigm from a homogenous chemical environment to individualized, data-driven feedback control.
Background
Organoids are miniaturized, self-assembled, and self-organized cellular constructs. They can recapitulate key morphology, cellular composition, and biological function of human organs, improving greatly upon the simplistic mono-cellular models in use for early drug development. At the same time, organoids’ human origin avoids the species mismatch inherent to animal testing, which currently contributes significantly to poor translatability from drug candidates to human clinical trials (not to mention inherent ethical concerns). Last but not least, being derived from individual human donors’ cell samples, organoids can be used to model both fully personalized response as well as true population-level sampling. Organoids are, however, sensitive to even small variations in their culture conditions over the often weeks-long course of their maturation, resulting in high variability of morphology, cell composition, and function.
Current mitigation approaches have focused on providing more homogenous conditions. We propose instead an entirely different approach, based on feedback-driven control of the chemical environment at the level of each individual organoid. This ability to generate highly homogenous organoid populations should further increase organoids’ attractiveness in replacing both overly simplistic cell models as well as ethically and functionally suspect animal models with something more meaningful.
Crossdisciplinary collaboration
In this project, we are establishing a new cross-disciplinary and complementary collaboration:
- Ioanna Miliou, Assoc. Prof., is an expert in data analysis and modeling, with proficiency in advanced statistical methods, machine learning, and predictive modeling, enabling the extraction of meaningful insights from complex datasets.
- Thomas E Winkler, Assoc. Prof., is an expert in microsystems integration for biomedical applications, such as organs-on-chips, with a focus on electrochemical sensors, microfluidic materials, and human-relevant cells or samples.
- Karolinska Institutet Stem Cell Organoid (KISCO) facility will lend its expertise regarding a range of cutting-edge organoid models and culture methods.
About the project
Objective
RECOPS aims to provide evidence-based insights into the benefits of open-source software in the power sector. The project focuses on identifying impactful areas for open-source applications, such as HVDC control, distributed renewable energy (DER) simulation, and hardware operations. Through case studies and a novel assessment methodology, RECOPS will evaluate system robustness and cost benefits from open-source approaches. The project is structured into three work packages, incorporates stakeholder engagement, simulations, and interdisciplinary collaboration.
Background
Open-source software has gained widespread use in industries like robotics and general software engineering but remains underutilized in the power sector. While academic initiatives exist, their limited maintenance and lack of industry relevance hinder broader adoption. The power sector’s need for robustness and focus on proprietary, closed software solutions further constrain open-source integration. However, there could be benefits with open-source software to enhance cost-effectiveness and robustness in energy systems.
Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Industrial Engineering and Management, Unit of Energy Systems and the KTH School of Electrical Engineering and Computer Science, Division of Electric Power and Energy Systems.
About the project
Objective
This project aims to develop a Risk Prediction Tool to use as an early warning system in primary care, aimed at cancer using Colorectal Cancer as the first test case. The tool will be based on data collected at primary care units, such as care visits such as history, symptoms, medication, lab tests and results, as well as free text from physician notes. The tool will use machine learning (ML), large language models and advanced statistical analyses to assess the risk of patients having an underlying yet undiagnosed CRC. Despite the promise and potential of primary care data to assess risk and improve screening, very few studies use this potential to better inform diagnostics.
Existing risk assessment tools designed for use in primary care have been developed and validated with a limited dataset (ICD-10 codes that were present in primary care datasets, covering a smaller percentage of patients), but are not yet in clinical use. The project aims to gain an understanding of the most salient data points for assessing risk of CRC in primary care, as well as insight into the potential benefits of such analysis for the healthcare system. Through this analysis, the project also aims to obtain a validated, novel risk prediction tool for CRC, which can be widely deployed and feed into national guidelines for colorectal cancer. By using CRC as the initial test case, the project aims to develop methods and tools through the results of the project which can be modified for other cancers and applications.
Background
A majority of patients with cancer present with early symptoms in primary care, before receiving their diagnosis. Despite the fact that Sweden has fast track for cancer diagnosis since 2015 (cancer care pathway, CCP) primary care physicians lack decision support to assess cancer risk for individual patients. Colorectal cancer (CRC) is the third most common cancer in Sweden. Despite screening, most patients with CRC are diagnosed after symptom presentation. The survival is excellent in patients diagnosed with CRC limited to the bowel wall (stage I-II) and intermediate when it has spread to regional lymph nodes (stage III) while the prognosis is poor in the approx. 25% who are diagnosed with CRC with distant metastases (stage IV).
Cross-disciplinary collaboration
The project is a highly inter-disciplinary collaboration between KTH, Regional Cancer Centrum Stockholm-Gotland, Regional Cancer Centrum Väst and Karolinksa Institutet. The project team comprises primary care clinicians, statisticians, modelers, software engineers and planners at RCC.
About the project
Objective
AsthmaTuner is an existing remote digital tool owned by MediTuner AB in Sweden. AsthmaTuner consists of a handheld spirometer connected to a mobile app that can be used by patients with asthma and other respiratory diseases to diagnose and manage their health condition.
Explainable Artificial Intelligence (XAI) has high potential to enhance existing digital tools previously developed without AI or with black-box AI. The existing AsthmaTuner system does not use any AI so far, and naturally the scope of using black-box AI has a high potential. In this proposed project, we pursue a significant step ahead. We will explore how explainable AI (XAI) will enhance both the diagnostic and management capabilities of the AsthmaTuner system. That means, XAI will help both patient users and their health care providers further improve asthma care, with the target of reduced need for unnecessary health care visits, or costly and potentially deadly asthma attacks or exacerbations. The XAI-based digital tools are expected to prevent further health disparities and will include the addition of social determinants of health to the design, implementation, and evaluation to AsthmaTuner to improve the performance.
Background
Asthma is a large-scale health care problem, affecting around 10% of the European population, and moreover; asthma and other respiratory diseases are increasing due to environmental deterioration worldwide. Asthma is a complex problem, which is characterized by airway inflammation and respiratory symptoms of wheezing, dyspnea, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation. Potentially effective ways to address asthma care are to better understand the day-to-day lung function (a longitudinal data) and asthmatic symptoms of individuals and more effectively identify, diagnose, and manage asthma by exploring potential disparities in symptoms, lung function, and outcomes that may exist by social determinants for patients. AsthmaTuner (AT) is the existing, validated digital tool in this regard.
The general purpose of the project is to design an explainable AI (XAI)-based smart AT system that uses a mobile app and a handheld spirometer. We refer to the proposed project as “A3S: AI-based Asthma App using Spirometer”. For the A3S system development, XAI algorithms will perform patient data analysis and guide decisions. The term “explainable” in XAI refers to the ability of these algorithms to provide transparent and interpretable explanations to patients and health care professionals for their predictions or decisions. The XAI algorithms will provide person-centred interventions precluding the onset or minimizing the risk of asthma in disadvantaged groups. It will help to improve respiratory health by training person-centred AI-support in asthma diagnosis and management of asthma in disadvantaged groups based on social determinants. Overall, the A3S project addresses a pressing health care problem with potentially widespread impact.
Cross-disciplinary collaboration
This is a highly collaborative project with a team of data scientists, clinicians, and AI specialists. Partners are PI Magnus Jansson, Co-PIs Saikat Chatterjee, PostDoc Zhendong Wang of EECS KTH, Ioanna Miliou of DSV SU, Docent Björn Nordlund of KUH and KI and his team, along with MediTuner AB.
About the project
Objective
- Reallocate maintenance resources from purely scheduled (preventive) activities to condition‐based and predictive strategies, thereby freeing time for future upgrades and investments.
- Identify cost drivers within the pump systems to target the layers where maintenance improvements yield the highest savings.
- Develop a predictive maintenance algorithm that incorporates a reliability index and measurable parameters to classify pump status and estimate remaining service life, assigning each pump a condition score (1 = new, 5 = worn).
- Pilot and validate the new solution at Högdalen pump station, installing and evaluating additional sensing equipment as needed.
- Ensure compatibility with Stockholm’s IoT platform, positioning the project as a first step toward broader adoption across city departments and external companies.
Background
Wastewater collection networks are essential for ensuring public health and wellbeing, yet they are susceptible to numerous faults including pipe bursts, pump malfunctions, and valve failures. Traditionally, preventing these issues has depended on frequent inspections and reactive repairs. However, there is growing recognition that a more proactive strategy—one rooted in predictive and condition-based maintenance—can both enhance the reliability of wastewater infrastructure and streamline the resources required to operate it. Such an approach can significantly reduce unexpected downtime, extend equipment lifespans, and ultimately lower overall lifecycle costs.
Despite the promise of prognostic models for predictive maintenance in many industries, water infrastructure has not received as much attention as manufacturing or other sectors. Current diagnostic tools in this domain are often tailored to a specific component or pump type, requiring specialized local measurements such as vibrations, oil temperature, or power consumption. In wastewater networks with diverse types of stations and pumps, designing a model for each component can be time-consuming. Moreover, missing measurements or uncertain behavior pose additional challenges. Consequently, the need has emerged for a flexible, data-driven solution capable of handling variations in station design, measurement availability, and environmental conditions.
The DECORUM (Optimized Predictive Maintenance for Wastewater Pump Stations) project, establishes the cooperation between the City of Stockholm, the Stockholm water utility operator (SVOA), the international water technology firm Xylem, and KTH to fill these gaps. SVOA alone operates roughly 300 wastewater pump stations, each with multiple pumps crucial to the city’s sewage system. Through a six-step development plan, SVOA has already taken steps to reduce maintenance costs while preserving high operational reliability. The next milestone is to move from largely manual, reactive procedures toward data-driven, predictive strategies that detect anomalies early, recommend targeted maintenance, and help technicians make informed decisions about when and how to service equipment.
Crossdisciplinary collaboration
The DECORUM project brings together a multidisciplinary team spanning academia, industry, and municipal stakeholders. KTH researchers contribute expertise in systems modeling, predictive algorithms, and robust control, while SVOA provides domain knowledge of large-scale wastewater operations and real-world operational data. Xylem, as a leading water technology company, offers more in-depth insights into cutting-edge pump hardware and software solutions. By uniting these diverse perspectives, the project can address both theoretical and practical challenges, ultimately delivering a flexible, scalable, and impactful predictive maintenance framework for critical urban infrastructure.
About the project
Objective
Towards the digital transformation, this research program aims to establish theory and methods for privacy-preserving localization in wireless networks. A privacy-preserving localization method is supposed to preserve user privacy while enabling communication and localization functionalities.
Background
Telecommunications providers possess vast amounts of wireless connection data that can be used for localization. Users will benefit from this location awareness, gaining access to a plethora of convenient and personalized services that will drive the digital transformation towards more intelligent, economically viable, and socially sustainable societies and industries. The scope of these services spans the gamut from industry 4.0 and home automation to comprehensive healthcare monitoring. However, it is crucial to acknowledge the potential dangers associated with such location-centric data. In the wrong hands, this information could raise privacy concerns, including data misuse, security breaches, and threats to property. In light of these risks, this research program aims to establish theory and methods for privacy-preserving localization in wireless networks.
About the Digital Futures Postdoc Fellow
Hanying Zhao is a postdoc at the Division of Information Science and Engineering at KTH. Her research interests include statistical inference, privacy-preserving technology, and localization.
Main supervisor
Tobias Oechtering, Professor, Division of Information Science and Engineering at KTH
Co-supervisor
Mats Bengtsson, Professor, Division of Information Science and Engineering at KTH
About the project
Objective
This project aims to understand better how people assess safety in a particular area of Stockholm – Järva, more specifically, how people’s safety perceptions relate to the quality of the physical and social environment of the area. We investigate the nature of safety on two fronts: an intra-area focus where we examine micro-level safety perceptions by people living and working in Järva and a city-wide focus where we explore ways to capture how people living elsewhere in Stockholm perceive Järva. The research will combine multiple sensors and data types, including phone apps, map-based surveys, and machine-learning models.
Background
Safety is a core component of people’s quality of life, affecting physical and mental health and restricting mobility and accessibility to public places. As such, safety is also a fundamental quality of the urban environment – what happens in places depends on how safe they are (or are perceived to be). Research has long pointed to the fact that indicators of poor maintenance or signs of physical deterioration are more important determinants of poor safety perceptions than actual instances of crime. The buildings’ façades, design, and the sense of ownership they promote are bound to affect crime and safety. Hence, some questions arise: which settings promote safety and for whom? What do these settings look like from a safety perspective?
Järva constitutes an interesting case study for several reasons: the area is undergoing great growth in the coming years – with more than 15,000 housing units being developed, new transportation links, workplaces and schools. However, a significantly higher share of Järva’s population feels unsafe outdoors at night and is more likely to avoid certain places where they live than the Stockholm average. In a previous study where Stockholm citizens were asked to assess Google Street View images regarding safety, the findings showed that the physical environment in Järva was ranked the lowest across Stockholm. Therefore, this project seeks to produce several diagnostics of the safety conditions in Järva and contribute to a better understanding of the spatiotemporal variations of the population’s safety perceptions.
Crossdisciplinary collaboration
Sensoring Safety Perceptions is a collaboration between research teams at KTH and MIT Senseable City Lab (part of the Stockholm Senseable Lab), as well as Stockholm City, Mapita (Maptionnaire), Kista Science City, and CityCon, and other local stakeholders based in the study area.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event: