Summary
Vasiliki Sideri-lampretsa is a PhD candidate at the Technical University of Munich with nine years of experience applying deep learning and geometric methods to medical imaging and healthcare. Her research focuses on medical image registration, neural fields, and generative techniques such as diffusion models to build robust, generalizable population atlases and flow estimation for biomarker discovery. She combines interdisciplinary leadership—having supervised over ten students and managed research teams—with hands-on engineering experience from startups and high-performance software projects. Recent work explores multimodal foundation models (VLMs/LLMs) for clinical reasoning and cross-modal alignment to improve explainability in decision support systems. She has a strong track record of translating advanced theory into reproducible code and publications, including open-source ultrasound simulation and reconstruction tools. Based in Munich, she bridges engineering, medicine, and applied ML with a knack for low-resource neural representations that boost accuracy in challenging biomedical modalities.
9 years of coding experience
2 years of employment as a software developer
Master's Degree, Electrical and Computer Engineering, Master's Degree, Electrical and Computer Engineering at Aristotle University of Thessaloniki
Master's Degree, Computational Science, Master's Degree, Computational Science at Technical University of Munich
Bachelor's degree, Electrical and Computer Engineering, Bachelor's degree, Electrical and Computer Engineering at Aristotle University of Thessaloniki (AUTH)
UPC - ETSETB TelecomBCN
Greek, English, German, Spanish