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Ágnes Németh (agnes.nemeth@uef.fi)

Her doctoral thesis dealt with issues of regional policy, relational-governance and mega-events planning. She has been involved in European research projects (European Science Foundation, FP7) in border studies focusing on cross-border cooperation processes and the social (de)construction of borders. In her post-doctoral research, she studies foreigners’ socio-economic engagement in different Finnish urban environments with the aim of producing knowledge on the local particularities and challenges of integration processes. She is managing the international project “ECoC-SME: Actions for inducing SME growth and innovation via the ECoC event and legacy” (Interreg Europe, 2019-2021).

Aki Pulkkinen (aki.pulkkinen@uef.fi)

Aki Pulkkinen’s research fields include ultrasound therapy, photo/optoacoustic and acousto-optic imaging and tomography, mathematical modelling, and inverse problems.

Aku Seppänen (aku.seppanen@uef.fi)

Professor Aku Seppänen leads a research team, which develops and applies computational and statistical methods for solving inverse problems arising from (physical) science and engineering. The main applications are: 1) environmental monitoring and modeling (especially measuring atmospheric aerosols and remote sensing of forests), and 2) tomographic imaging (especially electrical impedance tomography, industrial process tomography, non-destructive material testing and structural health monitoring; special emphasis is on concrete and other cement-based materials and structures).

Anna Mäki-Petäjä-Leinonen (anna.maki-petaja-leinonen@uef.fi)

Anna Mäki-Petäjä-Leinonen is Professor of Law and Ageing. Her research has focused on Elder Law combining jurisprudence (Civil Law and Social- and Medical Law) with social and medical sciences.
Mäki-Petäjä-Leinonen received her PhD (law) from the University of Helsinki in 2003. The title of her thesis is “Legal Rights of People with Dementia”. Her second monograph (2013) deals with the legal possibilities to anticipate aging. In autumn 2017, she published a book “Basics of Elder Law” with Anja Karvonen-Kälkäjä.
Mäki-Petäjä-Leinonen is involved in many research projects. She is sub-consortium PI in an international research project focusing on the specific issues concerning people who develop dementia or mild cognitive impairment while still working (MCI@work). She is also sub-consortium PI in national research project scrutinizing home-based palliative care of the elderly (MeRela). At the University of Eastern Finland, Mäki-Petäjä-Leinonen leads the Neuro-Ethics and Law research team, which is part of the university’s multidisciplinary Neuroscience research community.
Mäki-Petäjä-Leinonen teaches Elder and Guardianship law and is a teacher in course “Social Law Clinic”. She is docent (adjunct professor) in Family law at the University of Helsinki and docent (adjunct professor) in Elder law at the University of Lapland.

Anna Rosenberg (anna.rosenberg@uef.fi)

I work as a researcher in the Nordic Brain Network research group. My research interests include promotion of healthy aging and prevention of Alzheimer’s disease and dementia, with focus on multidomain interventions.

Between the Normal and the Abnormal – Cultural Meanings of Dementia and Old Age in Finland and Russia (DemOldCult)

The study focuses on perceptions and representations of old age and dementia. The main aim of the research project is to make visible those cultural practices and discourses that produce marginalising stereotypes and stigmatise aging people, by means of deconstruction and critical approach. The examination of two cultural spheres, Finnish and Russian, both together and apart, will help in discovering and deconstructing cultural stereotypes.

Biomedical Image Analysis

Our current research focus lies in identifying biomarkers of brain disorders from neuroimaging data, which is an exciting and rapidly growing research area at the intersection of machine learning, biomedical engineering and neuroscience. Conventional approaches towards imaging biomarkers reduce the data dimensionality by averaging the image information to one or few variables of a-priori interest – for example, the volume of Hippocampus for Alzheimers diagnosis. However, such methods discard much information present in brain images. Instead, allowing machine learning algorithms to decide what is important and decipher the predictive pattern (sometimes called statistical biomarker) is projected to be beneficial. This leads to challenging and underconstrained machine learning problems where the data dimensionality is larger than the number of samples and advanced computational techniques are required to solve these problems. We develop these techniques and apply to them to large brain image databases to help neuroscientists to find imaging markers to different brain disorders.

Bisrat Assefa (bisrat.assefa@uef.fi)

Designing freeform optics; 3D printing and characterisation of optical elements such as imaging lens, diffractive grating, slab waveguide, etc.