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ECE Prof. Manjunath and director of the UCSB Center for Multimodal Big Data Science & Healthcare research featured in the COE’s Convergence magazine

June 26th, 2018

illustration of big data
UCSB researchers awarded a $3.4 million grant from the National Science Foundation’s Office of Advanced Cyberinfrastructure to fund a broadly interdisciplinary Large-scale IMage Processing Development (LIMPID) project

Increasingly, big data and its partner, machine learning, are driving and enabling collaboration. Advances in sensors, cameras, scientific instrumentation, software platforms, deep neural networks, and computing power have made the promise of artificial intelligence real. The results show up in platforms that can identify patterns and scour meaning from millions or even billions of data points to better understand and manage a vast range of dynamical systems, from smart buildings and new materials to human biology and social systems.

Big data can take the form of simple data points that record, say, click-throughs on websites or entries on a spreadsheet, or it can be digital imagery, such as video, photographs, remotely sensed lidar images, or microscopy images. UCSB researchers are on the front lines of this data-fueled revolution, developing systems that make such multimodal big data a powerful tool for engineering.

According to B. S. Manjunath, professor in the Department of UCSB Electrical and Computer Engineering and director of the campus’s Center for Multimodal Big Data Science and Healthcare, big-data approaches require three main elements: experts in the field under study who can frame the research questions and form hypotheses; computational-science experts to design algorithms and data structures; and information-processing experts to address the signaling and information-theory components. 

Because so much science-related data takes the form of digital images, the center was awarded the NSF grant to fund the LIMPID project with the work based on a platform called BisQue (Bio-Image Semantic Query User Environment), developed by Manjunath’s group. BisQue had its roots in microscopy imaging and was developed to support a wide range of image informatics research for the life sciences. With its ability to process databases and perform image analysis, BisQue makes it easy to share, distribute, and collaborate around large image datasets.

“You can think of BisQue as Google Docs for scientific images,” Manjunath notes. “Imaging data has become ubiquitous, and much of big-data science is image-centric. Working with such data should be as simple as working with text files in Google Docs, so that people can collaborate and share information in real time. Not too many places have that kind of infrastructure for data science. It has taken us twelve years to build, and it’s something that sets us apart.”

COE Convergence – "The Long Reach of Big Data" (full article)

Manjunath's COE Profile

Center for Multimodal Big Data Science and Healthcare