Stratos Idreos, Associate Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has been selected to receive funding for research projects through the Department of Energy Early Career Research Program.

Idreos will receive at least $150,000 a year for the next five years as part of the program, which is aimed at supporting early career scientists whose research falls into one of the six major programs of the Department’s Office of Science: advanced scientific computing research, basic energy sciences, biological and environmental research, fusion energy sciences, high-energy physics, and nuclear physics.

Idreos is among 73 scientists from academic institutions and national laboratories nationwide who will be funded through the program.

“The award gives our lab the flexibility to explore the massive design space of data structures, one of the core areas of computer science,” said Idreos. “A data structure explains how data is physically stored. When designing algorithms for applications in business and the sciences, researchers often begin by defining a data structure that ideally minimizes computation and data movement.”

Unfortunately, however, there is no universally perfect data structure, meaning that as new data, applications, and hardware emerge, new structures must be designed from the ground up, a process that can be extremely expensive and time-consuming.

“We set out to discover the first principles of data structure design and develop learning algorithms to search through the astronomically large design space they form,” Idreos said. “Effectively, the principles and their structure form a ‘grammar’ that describes all existing data structures and their expected behavior in a principled way, as well as a massive number of designs that have not been invented yet.

“The critical benefit is the ability to reason about how to best store and access data,” he continued. “In turn, this makes it more accessible, and even automatic in some cases, to design tailored algorithms for any data-intensive problem. Data is quickly becoming a critical resource across scientific and business efforts, and our end goal is to take a step toward accelerating and democratizing the utilization of big data technology.”