Eleven new doctoral students at the University of Helsinki are specialising in multidisciplinary applications of artificial intelligence. They were selected from the joint call of the 32 doctoral programmes of the university, in which AI-themed positions were available to applicants from all fields of science and scholarship.

As part of deliberate efforts to nurture multidisciplinary research, FCAI participated in selecting the students and is offering mentoring support.

The new doctoral candidates and their research topics:

Anton Björklund, Faculty of Science
Interpretable machine learning

Mattia Cordioli, Faculty of Medicine
A nationwide artificial intelligence assessment of cardiometabolic risk

Evgeni Grazhdankin, Faculty of Pharmacy
Harnessing machine learning and artificial intelligence to tackle lead compound discovery challenges

Tuomo Lehtonen, Faculty of Science
Computational aspects of structured argumentation

Siiri Rautio, Faculty of Science
Interpretable deep learning for computed tomography

Luisa Fernanda Rodriguez Carrillo, Faculty of Biological and Environmental Sciences
Movement ecology meets community ecology: does movement behaviour leave imprints into species distributions?

Santeri Räisänen, Faculty of Social Sciences
Practices of algorithmic knowledge production

Janine Siewert, Faculty of Arts
Synchronic and diachronic Low Saxon dialectometry

Tuisku Tammi, Faculty of Arts
Learning under uncertainty – an experimental approach to compare human and AI learning

Alexander Thesleff, Faculty of Law
Tekoälyteknologioiden ja laajentuvan tekijänoikeusteollisuuden vaikutus tekijänoikeuteen (‘The impact of artificial intelligence technologies and the expanding intellectual property industry on copyright’)

Tianduanyi Wang, Faculty of Pharmacy
Deconvoluting complex disease mechanisms via machine learning methods for finding targeted therapeutic approaches