معرفی
Victoria Oberländer serves as a Visitor (Faculty) in the Department of Computer Science at the School of Science and a Doctoral Researcher in the Professorship of Jaakko Lehtinen. She concurrently holds Doctoral Student status, reflecting her active pursuit of a doctoral degree while contributing to faculty-level research initiatives.
Her educational foundation includes a licensed medical doctor degree in Human Medicine from Kiel University and a Licentiate degree in Medical and Health Sciences from Christian-Albrechts-Universität zu Kiel, awarded December 15, 2016. This dual expertise in medicine and computational science shapes her interdisciplinary approach.
Dr. Oberländer specializes in developing self-supervised deep neural network models to denoise electromagnetic brain recordings where ground truth data is unavailable. Her research addresses the critical challenge of decomposing multi-sensor time-series data into brain activity (signal of interest), sensor noise, and environmental noise—demonstrating how non-linear deep learning methods surpass traditional linear techniques like PCA and ICA. Core research areas include neural network architectures for time-series analysis, electromagnetic signal processing, and denoising algorithms specifically tailored for EEG/MEG data in clinical neuroscience contexts.
Her sole publication to date (2022) investigates cortical cross-frequency coupling alterations due to in utero antidepressant exposure, revealing significant developmental neuroscience implications. This work exemplifies her interdisciplinary methodology, merging pharmacology, neurodevelopment, and computational modeling.
- Cited 8 times (Scopus)
- Covered by 16 news outlets
- Shared by 44 X users and 2 Facebook pages
- Read by 125 Mendeley users
As a doctoral researcher under Professor Jaakko Lehtinen, she operates at the intersection of computer science and medical research, leveraging her clinical background to drive innovation in neurotechnology. Her work contributes to the Professorship's focus on computational neuroscience solutions within the Department of Computer Science infrastructure.
