Fysikkbygningen øst (kart)
Sem Sælands vei 24
Goal: Build a cross-disciplinary activity to develop, test, and apply new AI (Artificial intelligence) methods based on biological neural processes
Knowledge Graphs have emerged as a paradigm for representing data and semantics, providing new possibilities for a wide variety of data-driven tasks (e.g., data integration, data analytics, search, query answering, entity disambiguation, etc.). Driven by a new generation of Web and enterprise applications, advances in NoSQL graph databases, as well as the need for enhanced learning, the technology behind Knowledge Graphs is now maturing to a level that has become practical in various domains. The aim of this thesis is to study the applicability of Knowledge Graphs in the area of (network) neuroscience for modeling and analyzing how the brain works. More specifically, the thesis will cover design of Knowledge Graphs for modelling physical pathways that build functional networks from which cognitive capacities emerge, generation and storage of data in such Knowledge Graphs, as well as analytics on the data. The aim is to test the suitability of the Knowledge Graphs paradigm in helping to understand how the brain works.
Related relevant background article can be found here.
Biases, in general, describe errors in decision making. A number of cognitive biases have been identified in the area of human decision making (see, e.g., here, here, and here). In the context of Artificial Intelligence, algorithmic biases have become more and more relevant for data-driven decision (see, e.g., here).
The aim of this thesis is to categorize, organize, and interrelate various types of biases (both cognitive and algorithmic) by developing a formal ontology for cognitive and algorithmic biases. Furthermore, this ontology will be used as a foundation for the development of a system for bias management whose aim would be to help identify biases in decision making in a more automated way.
Please read more in the Norwegian project page.
Cardiac related disease is the number one cause of death in the Western world, including Norway. Echocardiography is the most important imaging tool for the cardiologist to assess cardiac function. An echo examination of the heart is real time, cost effective and can be performed without discomfort to the patient and without harmful radiation. These are great advantages compared to other medical imaging modalities.
Our goal is to contribute to the development of ever more intelligent systems. This can be systems that in specific tasks or environments can assist or replace human judgment, or it can be systems that can accomplish tasks we would like to automate.
Signal processing, image analysis, and machine learning for applications in medical imaging, sonar, seismics, and remote sensing.