Scholar
Tomáš Chobola
Google Scholar ID: KoL2wdQAAAAJ
Technical University Munich, Helmholtz AI
Citations & Impact
Citations
81
H-index
5
i10-index
4
Publications
11
Co-authors
0
Publications
Resume
Academic Achievements
- Publications: ECCV, ICCV, AAAI, MICCAI, etc.
- Projects: CoLIE, Noise2Detail, Quantifying Privacy Risks in Medical AI, 2nd Place in AAAI 2021 MetaDL Challenge
Research Experience
- 1. CoLIE: Fast, Zero-Shot Low-Light Image Enhancement (ECCV’24)
- 2. Noise2Detail: Ultra-Lightweight Data-Free Denoising (MICCAI’25)
- 3. Quantifying Privacy Risks in Medical AI (AISec 2023)
- 4. 2nd Place, AAAI 2021 MetaDL Challenge (Few-Shot Learning)
Education
- PhD candidate at Technical University of Munich and Helmholtz Munich
Background
- AI Scientist (PhD candidate) specializing in computer vision. His research focuses on solving real-world problems where data and compute are limited. He builds efficient, lightweight models and leverages self-supervised and zero-shot learning to create robust AI that can learn from unlabeled data and adapt to new challenges.
Miscellany
- Technical Proficiencies: Self-supervised learning, zero-shot learning, compute- and data-constrained environments, efficient ML, computer vision, image generation and restoration, large-scale and foundational vision models
- Programming languages: Python, C/C++, SQL, Bash
- Libraries: PyTorch, Scikit-Learn, NumPy, Pandas, OpenCV, Matplotlib
- Developer Tools: Git, HPC, LaTeX, LLM-assisted coding