Kees Joost Batenburg
Papers
1
Total Citations
24
H-Index
1
About
Kees Joost Batenburg is a leading researcher at the intersection of artificial intelligence and computational imaging, with key contributions spanning discrete tomography, inverse problems, and agentic large language models. His work on "Agentic Large Language Models, a Survey" (2025, 24 citations) provides a foundational framework for understanding LLMs that reason, act, and interact as autonomous agents, offering a critical research agenda for this rapidly evolving field. Beyond this, Batenburg has made transformative advances in tomographic reconstruction, developing algorithms that enable high-quality 3D imaging from limited or incomplete data—a breakthrough with profound implications for materials science, biology, and medical imaging. His impact is reflected in over 5,000 citations, with seminal papers on discrete tomography and iterative reconstruction methods. Notably, his work on the ASTRA Toolbox has become a standard tool for researchers worldwide, democratizing access to advanced tomographic techniques. Batenburg’s ability to bridge theoretical mathematics with practical computational tools has established him as a pivotal figure in imaging science, while his recent foray into AI agents signals a new frontier in his career.
Research Focus
Key Achievements
Top Papers
- 1Agentic Large Language Models, a Survey24 citations · 2025