Marco Serafini Receives NSF CAREER Award for Scalable AI-Powered Data Processing
Serafini’s project will develop software that helps existing database systems efficiently search and analyze complex data using AI.
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Marco Serafini, associate professor at the Manning College of Information and Computer Sciences (CICS), received a $666,930 National Science Foundation (NSF) CAREER Award to develop new software that makes AI-powered data processing faster, more efficient, and easier to integrate into existing database platforms.
Data powers everything from medical records and scientific research to legal documents and online services. Traditional database systems excel at working with structured information organized in rows and columns, which users can search and manipulate using languages such as SQL. But these systems were not designed to work as readily with unstructured data, such as natural language text and images.
Serafini's research will develop "bolt-on" software components that can be added to existing database systems, allowing organizations to take advantage of AI-powered capabilities without redesigning their entire data infrastructure.
His project, “Bolt-On Scalable Systems for AI-Powered Query Processing over Structured and Unstructured Data,” aims to bridge that gap by bringing AI-powered analysis of text, images, and other complex data into traditional database systems.
"A lot of valuable data is stored as natural language text, images, or other types of unstructured data that computers have traditionally struggled to understand," Serafini said. "This is why we want to use AI for data processing."
While AI has become increasingly capable of analyzing these types of data, integrating AI into database systems remains expensive and difficult to scale as data volumes continue to grow.
"With SQL, users only need to describe the information they want and delegate the efficient execution of their queries to the database system," Serafini said. "This project will develop tools that extend SQL-like languages with AI-powered operators and automatically choose an efficient and scalable way to execute queries."
These tools are important, according to Serafini, because AI requires fundamentally different approaches to storing and processing information than traditional data analysis systems.
"AI models require organizing, storing, and processing data in a way that is fundamentally different from traditional data analysis tools," he said. "Rather than representing data explicitly, they use numerical representations that capture important features of the data. Managing this type of data at scale requires novel solutions."
The hardware used for such computations is specialized, Serafini added, so it’s a challenge to write software that optimizes the hardware and lowers costs in terms of money and energy.
"These challenges require a new kind of data infrastructure that rethinks traditional system design principles," Serafini said.
The software developed through the project will be open source, making the tools broadly available to researchers, developers, and organizations seeking to integrate AI into their data systems.
The NSF CAREER Award is among the foundation's most prestigious honors supporting early-career faculty who demonstrate excellence in both research and education.
