Mahdieh Khanmohammadi
Førsteamanuensis i elektroteknologi
Kontakt
Telefon: 51832761
E-post: mahdieh.khanmohammadi@uis.no
Rom: KE E-427
Organisasjonsenhet
Det teknisk-naturvitenskapelige fakultet
Institutt for data- og elektroteknologi
Kort om meg
Mahdieh Khanmohammadi works with basic research in medical signal/image processing, computer graphics, neural networks, and artificial intelligence. She is involved in several major, interdisciplinary research projects, for example: Image analysis and AI to investigate acute ischemic stroke (The project is in collaboration between University of Stavanger (UiS), Stavanger University Hospital (SUH)). Estimating coronary flow reserve using angiography imaging (A collaboration between University of Stavanger (UiS), Stavanger University Hospital (SUH), University of Copenhagen (KU)). She is a part of Cognitive and Behavioral Neuroscience Lab working on investigating the dementia progression in patients using EEG signals and functional magnetic resonance images. She is s part of CLoud ARtificial Intelligence For pathologY (CLARIFY), where the main goal of the project is to develop a digital diagnostic environment that facilitates whole-slide-image (WSI) interpretation and diagnosis everywhere.
Presently, she is particularly interested in analyzing medical signals and images to provide new insight into diseases such as stroke, cancer, dementia and heart related ones and insights that poses a computer modeling challenge.
Mahdieh Khanmohammadi has been employed as associate professor at IDE since 2019. She received her Master and PhD degree from University of Halmstad and University of Copenhagen (DIKU) in 2012 and 2015, respectively. Her PhD program was carried out as a part of The Centre for Stochastic Geometry and Advance Bioimaging (CSGB). Following her PhD, she worked as a visiting researcher at the Department of Mathematics and Statistics, Aalborg University, Denmark. In 2016, she became a post doctorate fellow at IDE.
Courses:
Electrical engineering 1 (Elektroteknikk 1) ELE100; Bachelor level
Medical images and signals ELE670; Master level