sanaz dt

Design and implement the analysis pipeline for Parkinson's subtyping on PPMI data, including preprocessing, feature engineering, and unsupervised clustering. Adapt published analysis methods to study data requirements and perform manual 3D brain segmentation on clinical MRI data using 3D Slicer.

Clinical internship focusing on intensive care and anesthesia, as well as oral, maxillofacial, and facial surgery.

Development and maintenance of a mobile learning platform using React Native, Expo, and Supabase. Implemented features and created medical engineering learning content, and conducted user testing to identify and resolve UI/UX issues.

Development of the Open Source Registry System for Rare Diseases (OSSE) in Java, including bug fixing and new feature implementation. Created MDR-compliant medical data entry forms for rare disease registries.

Activities and societies: AI in Medicine competition: built a two-branch deep learning system (per-channel CNN-LSTM on log-spectrograms + MLP on hand-crafted connectivity features) for seizure detection and onset localization; raised competition score from 0.43 to 0.61; applied patient-disjoint cross-validation; final F1 0.67, mean onset error 32.4 s.

Pursuing a Master of Science in Medical Engineering with a focus on epileptic seizure detection from clinical EEG using deep learning.

Grade: 2.01

Completed a Bachelor of Science in Medical Engineering with a thesis on biomechanical modelling to enhance machine learning for squat movement quality.

Grade: 1.7

Completed the Feststellungsprüfung and DSD Language Diploma, passing the assessment test (T-Course) with a grade of 1.7.

Grade: 1.2

Earned a high school diploma with a focus on mathematics, assessed by Uni-assist as equivalent to German Abitur with a GPA of 1.2.