Klaus Mueller Awarded SEED Grant for Innovative Visualization Research

Klaus Mueller, a professor in Stony Brook University's Department of Computer Science, has been honored with a prestigious SEED grant to research innovative methods for the interactive visualization of autonomous X-ray scattering experiments. The effort pairs Mueller with Kevin Yager from Brookhaven National Laboratory's Center for Functional Nanomaterials.

The project aims to develop visualization tools that make interpreting high-dimensional data generated during autonomous experimentation (AE) processes easier. AE is a new methodology that combines artificial intelligence (AI) and machine learning to automate the entire experimentation process, from preparing samples to making decisions for subsequent experiments.

"Our goal is to create a Multivariate Transfer Function Editor that combines spatial and information visualization techniques, allowing scientists to map complex data into intuitive, colorized displays," Mueller explained. "This integration will provide a holistic view of experimental outcomes, enabling researchers to interactively explore and refine their models in real-time."

The project promotes a more interactive and dynamic scientific discovery process by enabling scientists to quickly and accurately interpret imaging outcomes in the context of underlying physics.

The SEED grant program, sponsored by the Office of Brookhaven National Laboratory Affairs, supports joint initiatives between scientists from Stony Brook University and Brookhaven National Laboratory. With a history spanning more than 25 years, this program has been instrumental in fostering collaboration between the two institutions' scientific communities.

"We're excited about the potential of this project," said Samir Das, professor and chair of the Department of Computer Science. "By bridging the gap between automated experimentation and human expertise, Professor Mueller's work exemplifies the innovative spirit and collaborative ethos that the SEED grant program aims to foster."

The project has the potential to significantly advance the field of scientific visualization and experimental automation, paving the way for more efficient and insightful scientific discoveries across various disciplines.

- Yuganshu Jain