Health Technology Research Group

Group leaders (PIs) Prof. Pasi Liljeberg, Assoc. Prof. Antti Airola and Asst. Prof. Matti Kaisti
The research group focuses on developing data-analytics based solutions to support health and well-being. Central to the group’s research are machine learning based analysis methods, the use of wearable IoT devices for data collection, biosignal analytics, and co nnecting the results to the practical needs of consumers and clinical caregivers. The group leverages its research for developing AI based applications that allow implementing a more personalized and preventive approach to healthcare. As a practical example, the group has developed methods for detecting atrial fibrillation at home, where the biosignal is measured and analysed using a mobile phone.

Autonomous Systems Laboratory

Group leaders (PIs) Prof. Juha Plosila, Senior Research Fellow Hashem Haghbayan
From the perspective of autonomous systems, our vision is to facilitate efficient co-adaptation between AI-based agents and humans, society, and the environment. This co-adaptation involves mutual understanding of each other’s goals and the development of knowledge for each side over time. Achieving this requires a reciprocal enhancement of understanding, reasoning, and judgment on both fronts. On one hand, it is crucial that decisions made by intelligent agents are transparent and comprehensible to humans. This transparency is ac hieved through methods such as AI explainability, as well as through the encapsulation and abstraction of knowledge from the ground up. On the other hand, it is imperative that information, goals, and constraints originating from humans, society, and the environment seamlessly transfer to the artificial agent. This transfer can be facilitated through diverse channels, including language, patterns, and signals. Furthermore, this dynamic co-adaptation is an ongoing process that unfolds over time, necessitating careful consideration of factors such as AI sustainability and ethical considerations throughout the design and runtime phases.

AI Ethics and Issues of Responsibility

Group leaders (PIs) Prof. Juha Räikkä and Senior Researcher Susanne Uusitalo
Central to the present group are questions such as whether codes of professional ethics can handle AI ethical problems exhaustively, would it be possible to include moral competency in autonomous AI agent architectures, and who is responsible and accountable of the AI-related actions and their implications. Ethical analysis is required on the role moral sentiments in responsibility attributions towa rd AI-agents. If we blamed them as we blame human beings, would it mean that they are responsible in the similar manner as human beings are? Or can the responsibility attributions be mistaken even if we are not mistaken on factual matters? Also, will the machine-human hybrid agents constitute an exception or exemption?

Regulating AI

Group leaders (PIs) Prof. Mika Viljanen
The group focuses on regulatory strategy approaches to AI regulation. The group builds its analysis on solid knowledge of existing state-of-the-art of AI and its possible future trajectories. Understanding of AI features informs the groups’ work to assess existing and proposed regulatory initiatives and developing possible regulatory approaches to harm creation. The group has focused on analysing AI-related harm generation dynamics, existing EU regulatory strategies, and robot regulation strategies.

Materials Informatics Laboratory

Group leaders (PIs) Asst. Prof. Milica Todorović
The research group combines AI and data science with data from materials simulations and experiments to optimise the functional properties of materials and boost their performance in technological devices. We typically deploy supervised learning to map materials p rocessing conditions or structure to their functional properties, so that we may infer which set of condition s produces the optimal solution. In our community, we promote high quality data digitalisation and responsible dataset curation with a balance of redundancy and diversity. Given data scarcity, we also develop probabilistic active learning methodology for materials science studies, which can be used for AI-guided experimentation. This allows us to integrate explainable, trustworthy, and uncertainty-aware AI into our workflows. Recently, our focus is increasingly shifting to multi-fidelity and multi-modal AI, and we are keen to merge information from text (NLP), images (computer vision) and scientific expertise (human-in-the-loop) with numerical models. We consult on AI-driven materials solutions for both academic and industrial partners.

Materials in Health Technology

Group leaders (PIs) Assoc. Prof. Emilia Peltola
For the Materials in Health Technology Group, the research focus is in understanding the interface of materials in biomedical applications. Our goal is to enhance sensor performance in biomedical applications through a data-driven approach, strategically designing custom surfaces. We bridge the experimental-computational gap by implementing high-throughput in situ experiments and leveraging machine learning to categorize and prioritize data. This allows us to pinpoint key parameters connecting electrode surface chemistry to electrocatalytic properties and biocompatibility. Our methodology includes proposing experiments and simulations for maximum parameter information.

Algorithmics and Computational Intelligence (ACI) Laboratory

Group leaders (PIs) Profs. Jukka Heikkonen and Tapio Pahikkala
The research group is focusing on developing data analytics and machine learning techniques to address real-world challenges with sustainability and ethical considerations in mind. In the field of machine learning, our research encompasses both traditional and deep learning methodologies, placing specific emphasis on optimizing model complexity, conducting performance evaluations, and addressing issues related to robustness and scalability. Recently we have concentrated on machine learning based sensor and data fusion techniques in different domains such as autonomous systems but also when dealing with open and big data analytics especially related to various geoinformatics applications. Additionally, our research extends to algorithm design and discrete optimization, where we employ a combination of mathematical programming and heuristics to tackle complex optimization problems. We have a wide international and national collaboration network and we have actively participated in many national and international research projects, often in close partnership with industry.

Turku Natural Language Processing Group (TurkuNLP) – Computer Science

Group leaders (PIs) Prof. Filip Ginter
The research group is focusing on algorithmic and computational aspects of natural language processing (NLP), one of the cornerstones of modern AI. Our research is centered around machine learning for NLP, in combination with work at a very large-scale corpora, regularly working with collections in the 100+ billion word range. Our most recent work includes training generative large language models in a high-performance computing environment, cross-lingual meaning models in historical language corpora and other noisy datasets, as well as the development of core language technology for Finnish and numerous other languages. By its nature, our research involves topics from deep neural network training to highly scalable algorithms for indexing and matching meaning across languages. We make extensive use of the national computing resources and have been selected for piloting the two most recent generations of GPU-accelerated supercomputers, including LUMI, the largest European supercomputer.

Turku Natural Language Processing Group (TurkuNLP) – Digital Linguistics

Group leaders (PIs) Prof. Veronika Laippala
The research group focuses on natural language processing (NLP) and its applications in linguistics. The core research targets the use of machine-learning based tools to examine language in large digital language datasets, typically adopting text linguistic and discourse analytic approaches and deep neural network –based methods. Additionally, a specific research focus is the development of extremely large web-based language datasets, in a tight collaboration with TurkuNLP / Computer science. Similarly, we develop together novel language technology methods for Finnish and other languages. The recent projects center around 1) text registers (genres) in massively multilingual web-scale corpora and in historical collections, 2) language use in specific settings, such as social media and crisis communication, 3) language and society, such as characteristics of parliamentary speeches and language and climate change. In all these, the to pics are approached in large born digital or digitized data and using machine learning.

Communication and Cyber Security Engineering

Group leaders (PIs) Prof. Seppo Virtanen
The research group conducts research and develops technologies for societal digitalization and a secure information society. The group has three main research directions: 1) application-domain specific communication and data security: ensuring the security, privacy and safety of communication and data from short range IoT systems to global internetworking, especially in application domains dealing with e-Health, autonomous traffic and smart environments; 2) cyber Security technology and assessment: development of cyber security technologies and assessment methods for future networked systems; detection technologies for new network security solutions and methods for as sessing and predicting the security levels of deployed networks; systems with security, privacy and safety considered as elements to be built in; and 3) cyber security for the society and the individual: addressing the human element and factor in cyber security to prevent the po tential massive personal and societal damage arising from the threats and risks in the wide-spread adoption of future smart environments (smart cities, smart traffic, smart spaces, e-Health, e-voting) and technologies by individuals and organizations; securing the human side of digital transformation, including research on the effectiveness, content and development of cybersecurity education.