The following story originally appeared on the website for W&M’s School of Computing Data Sciences & Physics. – Ed.
The National Science Foundation has recognized Cristiano Fanelli, associate professor of data science at William & Mary, with a Faculty Early Career Development (CAREER) Award, one of the most prestigious honors for early-career faculty.
The award provides up to $500,000 in grants to support Fanelli’s project, “Mapping the Inner Structure of Nucleons with Deep Learning and Uncertainty Quantification,” which aims to reveal new insights into the fundamental building blocks of visible matter.
Understanding how protons and neutrons are structured remains one of the great open questions in physics. Fanelli’s research uses deep learning to create three-dimensional maps of how quarks and gluons, the particles inside protons and neutrons, move and interact.
“I’m deeply honored to receive this award,” Fanelli said. “It supports a long-standing quest of mine to understand the building blocks of ordinary matter while training students to work at the intersection of physics and AI.”
Fanelli’s research group works with scientists at Jefferson Lab and the future Electron-Ion Collider where they smash particles together and study how the spins and motions of quarks and gluons affect the structure of the proton.
These collisions produce a staggering amount of data volumes, with detector data rates reaching up to 100 terabits per second at the EIC. To interpret these complex datasets, Fanelli’s team applies advanced AI tools to improve measurement accuracy and quantify uncertainty.
“We’re developing AI tools that function like a new kind of microscope,” Fanelli explained. “They don’t magnify images, instead they navigate the massive data streams from our experiments to isolate the features and patterns that matter scientifically.”
Advancing understanding of the strong force — the interaction that binds all visible matter — can drive progress in energy science, advanced materials, and future technologies. Integrating AI with physics is opening new pathways toward these discoveries.
“We have observed that the fusion of disciplines brings innovation and exciting opportunities for science,” said Fanelli. “This research sits at the nexus of AI and physics, pushing the boundaries of data processing, uncertainty quantification, and simulation while delivering tools with broad impact on science, technology, and society.”
Fanelli’s lab has already demonstrated how AI methods developed for physics can have far reaching applications, including detecting social media bots. This project will further expand those innovations by training students across disciplines in data science, machine learning and physics, preparing a new generation of researchers fluent in both scientific and computational thinking.
“(People) sometimes frame my work as either physics or data science, overlooking the fact that the most exciting opportunities emerge exactly at the intersection of the two,” stated Fanelli. “The fusion of physics with data science and AI is a rich frontier for discovery.”
Editor’s note: William & Mary is committed to preparing students for data-rich environments and an AI-driven world through thoughtful leadership and human-centered innovation. This vision is taking shape in the new School of Computing, Data Sciences & Physics (CDSP) in collaboration with the entire campus. CDSP integrates AI tools into daily work, including news writing. The CDSP communications team used OpenAI’s ChatGPT to assist in building this article. The team then reviewed and edited the article before publication.