Manjunath – NSF Grant for Multimodal Imaging Data
ECE Prof. B. S. Manjunath, an expert on computer vision/AI, along with collaborators Materials Prof. Tresa Pollock and BioEngineering Prof. Beth Pruitt, receive a National Science Foundation (NSF) award to develop the next generation cyberinfrastructure for multimodal imaging data
From the College of Engineering News article "Grant Supports Advances in Cyberinfrastructure for Multimodal Imaging Data"
The grant will bring to the UCSB BisQue platform current advances in AI and large language models (LLMs). A meeting in February hosted by the Center for Multimodal Big Data Science and Healthcare at UCSB marked the launch of the BisQue Deep Learning (BDL) cyberinfrastructure (CI) project.
“Our goal with the BisQue Deep Learning cyberinfrastructure is to make powerful AI tools accessible and usable for scientists across disciplines,” said Manjunath, principal investigator of the project. “By integrating scalable deep-learning capabilities into a unified platform, we’re enabling researchers to focus on discovery rather than data wrangling. This effort represents a major step toward accelerating scientific progress through open and sustainable infrastructure.”
Incorporating a scalable software infrastructure that enables the use of advanced deep-learning techniques to manage and analyze complex datasets, BDL CI is set to transform scientific research across multiple fields, including materials science, environmental science, and bioimaging.
“Cyberinfrastructure is a critical element for integrating powerful new AI tools with the terabyte-scale materials-science datasets that we generate,” said Pollock. “In the future, it will enable us to build materials ‘one grain at a time and to predict the corresponding suites of properties essential for designing advanced engineering components for space, nuclear, and advanced propulsion applications.”
Incorporating cutting-edge deep-learning and computer-vision techniques, the BDL CI offers a user-friendly platform to manage and analyze vast, complex datasets. This initiative tackles significant challenges, such as data curation, specialized domain expertise, and the need for scalable solutions for high-dimensional data. The BDL CI significantly enhances national scientific capabilities, while also supporting education and diversity through comprehensive training programs, efforts that will make advanced analytical tools accessible to a wider research community, thus promoting scientific progress.
Manjunath said that the infrastructure is built for scalability and efficiency to support machine learning and AI for large scale imaging data. That translates into support for such advanced functionalities as spatio-temporal annotations, computer-vision methods for object detection, segmentation, localization, classification, and tracking, all underpinned by a robust database backend that ensures data integrity and provenance.
This project, driven by a multidisciplinary team from UC Santa Barbara, UC Riverside, and the Smithsonian Institution, is intended to ensure broad access and long-term sustainability through strategic collaborations and the integration of community feedback into ongoing development.
The BisQue Deep Learning platform can benefit society greatly in the area of healthcare by advancing diagnostics, enabling early disease detection, and supporting personalized treatments. In environmental science, the platform aids in monitoring biodiversity and addressing climate challenges, while in materials science, it accelerates discoveries that are crucial for developing sustainable technologies. By democratizing access to a suite of advanced AI tools, BDL fosters innovation, education, and solutions to pressing global challenges.
