Researcher (Open to Work) | PhD in Medical Image Analysis
I recently earned my PhD in Electrical Engineering from the University of Ljubljana (Slovenia). During my doctoral studies, I completed two international research fellowships: as an ASEF Junior Fellow at the Computer Laboratory, University of Cambridge (UK), and as a Fulbright Scholar at Johns Hopkins University (USA). which is currently ongoing. I am currently transitioning to a new role and am open to new opportunities.
My research focuses on medical image analysis, 3D semantic segmentation, modeling of human anatomy, development of validation metrics, and synthetic data creation. I am particularly interested in virtual imaging trials, digital patient twins, and their applications in radiotherapy, disease diagnosis, AI tool validation, and the creation of next-generation tools for precision and personalized medicine.
For a full list of my publications, please refer to my CV or Google Scholar. Feel free to reach out if you’d like to collaborate! 😃
Experience
Research Intern (Fulbright Scholarship)
Johns Hopkins UniversityDeveloped AnatomyGen, a generative model of human anatomy using implicit neural representations, capable of producing realistic, high-resolution anatomical phantoms for use in virtual imaging trials in medicine and in silico experimentation.
Advised by Assoc. Prof. Dr. Mathias Unberath.
Research Intern (ASEF Junior Fellow)
University of CambridgeResearch on tumor segmentation in mammograms using deep learning.
Advised by Prof. Dr. Mateja Jamnik and co-advised by Assoc. Prof. Dr. Nikola Simidjievski.
Education
PhD in Electrical Engineering | Medical Image Analysis
University of LjubljanaThesis: Multi-modal Medical Image Segmentation Using Deep Learning. Advised by Prof. Dr. Tomaž Vrtovec and co-advised by Assoc. Prof. Dr. Bulat Ibragimov.
My doctoral research focused on improving the accuracy and clinical reliability of organ-at-risk (OAR) segmentation for radiotherapy planning, particularly in the anatomically complex head and neck region. I investigated the potential and limitations of combining computed tomography (CT) and magnetic resonance (MR) data for this task, addressing challenges such as image registration, modality-specific artifacts, and clinical validation. As part of this work, we developed the HaN-Seg dataset - a public paired CT–MR dataset with expert annotations - and organized a computational challenge on multi-modal segmentation. We also designed in-house multi-modal models to explore the integration of CT and MR information. On the validation side, we conducted geometric, dosimetric, and psychometric analyses of organ-at-risk auto-segmentation and introduced MeshMetrics, a mesh-based implementation of distance-based metrics for 2D and 3D segmentation evaluation.
Summer School on Machine Learning
Eastern European Machine Learning Summer School (EEML)Presented a poster, participated in lectures and coding tutorials on various topics including: Diffusion Models and Generative AI, Reasoning with Deep Learning, Bayesian Deep Learning, Deep Reinforcement Learning, Transformers, Self-Supervised Learning, Visual-Language Models, Geometric Deep Learning, Advanced Architectures, Robustness & Fairness, and Causality.
These sessions were led by some of the field’s leading experts, such as Alfredo Canziani, Sander Dieleman, Nenad Tomašev, Petar Veličković, Matko Bošnjak, Yee Whye Teh, Aleksandra Faust, Çağlar Gülçehre, Chris Dyer, Jovana Mitrović, Michael Bronstein, Razvan Pascanu, Martin Vechev, Ivana Malenica, and others.
MSc Electrical Engineering | Biomedical Engineering
University of LjubljanaRead ThesisGPA: 10.0/10.0
Courses included:
- Neurocybernetics
- Medical Image Analysis
- Intelligent Methods in Biomedicine Data-mining
- Biomedical Imaging Technologies
- Biomedical Sensors and Electronics
- Biomedical Signal Processing
Master’s Thesis: Regression models for predicting cerebrospinal fluid biomarkers of Alzheimer’s disease
Summer School on Biomedical Imaging
ETH ZürichLectures and practicals on various biomedical imaging modalities; convered a range of cutting-edge techniques and analysis tools.BSc Electrical Engineering
University of LjubljanaGPA: 10.0/10.0
Courses included:
- Control Systems
- Signal Processing
- Intelligent Systems
- Semiconductor Electronics
- Optoelectronics
Activities and societies:
- Student Council,
- Faculty’s Senate,
- Study Affairs Committee