Konstantinos Spiliopoulos on the Future of Scientific AI

By Brendan Galvin, CISE Staff

High above the ground, a sudden jolt of turbulence can unsettle even the calmest airline passenger. But for scientists, that moment of instability represents something far more significant: a deeply complex and still-unsolved problem in physics. Despite having equations that describe airflow, the chaotic nature of turbulence makes it nearly impossible to predict even seconds ahead. This challenge reflects a broader issue in science—how to understand and model randomness in the natural world.

At Boston University, Konstantinos Spiliopoulos, Professor and Director of Statistics, is working to tackle exactly that kind of uncertainty. Spiliopoulos is a faculty affiliate of the Center for Information and Systems Engineering and the Hariri Institute.

“One of the unifying themes between statistics, probability, applied mathematics, and machine learning,” Spiliopoulos explained, “is trying to model and quantify the effect of randomness on different phenomena.” 

Instead of trying to fix the chaos, we should be treating it as a ‘structured’ kind of randomness, finding clear patterns in what looks like total noise to the outside world.

Large Language Models (LLMs) are large, expensive, and consume enough energy to power small cities. Spiliopoulos compared how these models currently function to the 100 Doors Problem.

“Imagine you are entering a room that has 100 doors,” he said. “A couple of them take you to the right answer, but you don’t know which ones. Currently, models try to open and close all 100 of them until they find the right combination.”

Corporations have massive “compute” power, so they can afford to open every door. They find the patterns, but they do it inefficiently.  Spiliopoulos’ goal is to mathematically determine which doors are essential and which are just “noise” that can be avoided.

“In certain cases, we can sacrifice a bit of accuracy for a tremendous gain in computational complexity,” he noted.

By accepting a tiny margin of error, Spiliopoulos’s work focuses on using statistical and probabilistic tools to reduce the amount of processing power a model needs. This makes AI much more efficient, allowing it to run faster while using a fraction of the electricity required by today’s ‘brute force’ methods.

Moving away from this power-hungry approach is becoming a necessity as the digital and physical costs of AI continue to climb. The recent surge in AI popularity has created a strange paradox on college campuses.

More students are using these tools than ever, but fewer understand how it actually works. Spiliopoulos noticed this gap back in 2014. “There was a gap in the training of students,” he recalled. “Many students focus on the computational aspect, but they don’t necessarily know why it is working.”

This realization led to a new course at BU and, eventually, a formal textbook, Mathematical Foundations of Deep Learning Models and Algorithms. 

For Spiliopoulos, it wasn’t just about teaching code, but about teaching the “why.” 

“Without understanding the mathematical foundations, we are simply users,” he noted. The textbook serves as a roadmap for the next generation of researchers to understand the statistical mechanics of the tools they use every day.

You are reading an excerpt of a story published on CISE.  Read the full story here: Opening the Right Doors: Konstantinos Spiliopoulos on the Future of Scientific AI.