“By managing resources more effectively in the cloud, the end user gets more predictable performance from the application running on their smartphone,” she adds.
Mathematical beginnings
Delimitrou grew up in a midsized town within the vast plains of northern Greece. Her early interest in math and science was sparked, in part, by the ancient history of her homeland, where Euclid and Pythagoras studied mathematical problems more than 2,000 years ago.
“In Greece, there is a long tradition of geometry,” she says.
She also drew scientific inspiration from her parents. Her mother worked as a chemical engineer and her father as a pharmacist — and both encouraged their daughter’s innate curiosity.
Her early affinity for math led Delimitrou to study computer engineering at the National Technical University of Athens, even though she didn’t know much about the field. She quickly gravitated toward courses that focused on the applied science of engineering.
For her diploma thesis — a project all students complete during their fifth and final year of study — she studied resource management in a computer when multiple applications are running at once.
“A lot of the challenges I was looking at then would get much harder if, instead of a single system, you had 100,000 of these systems. That was a problem that piqued my interest,” she says.
Seeking to make a bigger impact as a researcher, Delimitrou pursued a graduate degree at Stanford University. She began tackling inefficiencies in cloud computing systems and large-scale data centers, which was a rapidly growing area of research.
Through that work, Delimitrou and her research mentor, Christos Kozyrakis, the Leonard Bosack and Sandy K. Lerner Professor of Engineering, realized many large computing systems were underutilized.
“You would expect, with all the demand for these systems, that they should be running close to 100 percent capacity. But we found that most were running at only about 15 percent capacity,” she says. “This is not a resource-efficient or sustainable way of scaling these systems.”
Applying AI
To push that utilization closer to 100 percent, she began investigating machine-learning solutions to streamline cumbersome computational processes. Machine learning could automate resource management operations in the cloud, identifying solutions that developers might miss on their own.
“Applying machine learning to solve a large-scale system problem was a novel approach at the time. It was a bit risky because people had not yet shown that these techniques would work,” Delimitrou says. “But empirical approaches require a lot of expertise, and the scale of the system is so large that it is difficult for users to manage. This is why machine learning is often the best solution.”
After earning her PhD, Delimitrou continued this line of work as an assistant professor at Cornell University.
One tool her group developed, Seer, uses deep learning to anticipate and prevent problems in web applications before they happen. This averts widespread slowdowns that may occur if a developer tries to fix a problem manually.
As she delved deeper into cloud computing, Delimitrou observed that cloud applications were changing. Developers were now splitting applications into smaller pieces to spread across multiple servers, which increases the speed of deployment.
“But the servers were not built for this new style of application design. So, I rethought some of my earlier work to build machine-learning systems for this new class of applications,” she says.
To tackle these new challenges, she found herself collaborating more often with faculty members who had different software and hardware expertise. Those collaborations opened exciting new research areas.
A few years later, she decided to join MIT because of the opportunity to collaborate with researchers at the top of their fields in hardware and software engineering. She became an assistant professor in EECS in 2022.
Creative approaches
At MIT, Delimitrou also enjoys the teaching aspect of her role. One of her favorite courses to teach is 6.191 (Computation Structure), a popular undergraduate class with about 350 students each semester.
While it’s challenging to keep the course material fresh when the field constantly evolves, she strives to inspire creativity in her students.
“I want the students to learn how to think and learn on their own. Part of that involves shifting away from formulaic assignments and making classes more open-ended. I’d rather give the students something to make them think more deeply,” she says.
In the lab, a creative mindset helps Delimitrou and her team identify novel solutions to problems in cloud computing that others might overlook.
