Cristiano Fanelli

Dr. Cristiano Fanelli is a Professor of Data Science and Director of Technology at William & Mary, where he leads the Dataphys Group. His research bridges data science and physics, focusing on machine learning applications in nuclear and high-energy physics, including collaborations with CERN, Jefferson Lab, and the ePIC experiment at the Electron Ion Collider.

His research interests include developing advanced machine learning algorithms for physics applications such as detector optimization, particle identification, anomaly detection, and uncertainty quantification. He actively explores the use of Graph Neural Networks and Retrieval-Augmented Generation (RAG) systems for grounding Large Language Models in physical truth, minimizing hallucinations in scientific contexts. His work integrates high-performance computing with Bayesian optimization and deep learning for simulations and data analysis in experimental physics.

Dr. Fanelli has supervised numerous postdoctoral researchers, PhD and master’s students, and undergraduate researchers from William & Mary and international institutions such as the University of Regina and the University of Messina. His group works on projects including AI-assisted Detector Design (AID2E), AI-Optimization of Polarization (AIOP), and deep learning for calorimeter clustering and deep inelastic scattering. Former students have gone on to positions at UC Berkeley and in industry.

Dr. Fanelli received his PhD from Sapienza University and previously worked at the Massachusetts Institute of Technology and the Institute for Artificial Intelligence and Fundamental Interactions at MIT. He maintains strong collaborative ties with major experimental facilities and is contributing to next-generation physics experiments through data science innovation.