📫 [email protected]

𝕏 / GitHub / Google Scholar / ORCID / CV / 🇨🇿

I’m a doctoral candidate at the Helmholtz Zentrum München and Technische Universität München. I’m also part of MUDS and HELENA.

My research interests include inverse problems in computational microscopy, image generation and image reconstruction, and self-supervised and unsupervised learning. The focus of my work is to create and refine imaging techniques that will lead to better image quality, facilitating progress in the biomedical field.

🚨 Currently, I’m available to supervise driven students, offering thesis topics for both bachelor’s and master’s degrees in the fields of image processing and restoration with the focus on biomedical imaging.

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Featuered projects

Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations.
One-shot low-light image enhancement based on the HSV color space.
Accepted to ECCV ‘24.

Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration.
Self-supervised network for microscopy image synthesis.
Accepted to ICCV ‘23 BioImage Computing Workshop.

A Feature-Driven Richardson-Lucy Deconvolution Network.
Volumentric microscopy image restoration model.
Accepted to MICCAI ‘23.

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Education & previous projects

Prior to my doctoral studies, I completed my master’s degree in Data Engineering and Analytics from TUM.

Additionally, I had the opportunity to contribute to several projects that involve:

• Privacy-preserving machine learning
• Transfer learning
• Statistical modeling

I was also fortunate to spend time at The Hong Kong Polytechnic University.