People
Principal Investigator
Dr. Fanelli is a Professor of Data Science and Director of Technology at William & Mary. His research lies at the intersection of data science and physics, where he contributes to the development of advanced machine learning algorithms and data science tools for medium and high energy nuclear physics research. Dr. Fanelli has an extensive history of collaboration with experiments at CERN and Jefferson Lab and he is also a member of ePIC, a flagship experiment that is set to be conducted at the forthcoming Electron Ion Collider to further advance our understanding of the strong nuclear force. He received his PhD from Sapienza, and worked at the Massachussetts Institute of Technology and at the Institute for Artificial Intelligence and Fundamental Interactions at MIT.
Postdoctoral Researchers
Karthik works as a postdoctoral researcher at William and Mary, where he uses artificial intelligence to optimize detector designs for advanced physics experiments, particularly the Electron Ion Collider, with Multi Objective Bayesian methods (AI-assisted Detector Design for EIC (AID2E) project). His research intersects Bayesian methods, HPC/HTC computing and Detector response simulations methods. He collaborates with the GlueX experiment at Jefferson Lab since 2018 and is involved in the study of exotic forms of matter with partial wave analysis methods. Karthik is also involved in Large Language Models for Physics, especially methods for grounding LLM to truth and minimizing hallucinations. He is developing a Retrieval Augmented Generation (RAG) system for EIC.
Patrick is a postdoctoral researcher in the William & Mary Data Physics Group. His research interests lie at the intersection of nuclear/particle physics and data science, including Reinforcement Learning, Anomaly Detection, and Calorimetry. Patrick is currently working the AI-Optimizaton of Polarization (AIOP) project at Jefferson Lab. He received his doctorate in 2023 at the Massachusetts Institute of Technology where he studied nuclear structure and tomography at CLAS12 under the supervision of Prof. Richard Milner. He received his B.S. in Physics & Mathematics at Temple University.
Graduate Students
James is a PhD student in Data Science at William and Mary. His current area of research is deep learning development for Nuclear and High Energy Physics, including uncertainty quantification, anomaly detection, particle identification for Cherenkov detectors, and unfolding. He received his BSc. Physics in 2021 from the University of Regina, and a MASc. in Electrical and Computer Engineering from the University of Ottawa in 2023.
Hemalata Nayak, is a Data Science PhD student at William & Mary. She received her master in Physics from Central University of Karnataka, India in 2023. Her research interest lies in developing data analysis techniques in the field of experimental medium and high energy nuclear physics using Machine learning and Artificial intelligence. She is working on using AI for detector optimization in EIC (AID(2)E). She is also working on Graph Neural Networks and exploring multiple applications like jet physics.
Vinay is a Graduate Teaching Assistant in Data Science at William & Mary. He has joined our group and he is currently working on Large Language Model for the physical sciences.
Stjepan is a PhD student from University of Regina. His co-supervised by Z. Papandreou (Regina) and C. Fanelli. Stjepan is working on deep learning for deep inelastic scattering and machine learning for clusters reconstruction in calorimeters.
Antonino is a visiting PhD student from University of Messina, Italy. He is working on neutron fast simulations using deep learning techniques. He is also involved in other projects that explore Beyond Standard Model physics such as BDX at Jefferson Lab.
Undergraduate Students
Michael Martinez is majoring in Data Science at William & Mary. He is currently working for his Honors Thesis on developing Deep Learning models for Imaging Cherenkov Detectors (Spring 2024)
Azizah is working on her MSc Thesis and is supervised by Z. Papandreou with the co-supervision of C. Fanelli. The topic of her thesis is Machine Learning for PID of electromagnetic showers at the JEF experiment at Jefferson Lab. Her thesis has been defended on 6/7/2024
Undergraduate Research Projects
Lydia is working under the supervision of Dr. Fanelli on a Physics Research project (PHYS 451): ML/DL for imaging Cherenkov detectors (Fall 2023 and Spring 2024). Her thesis has been defended on 5/6/2024
Luke is a student of Data Science/Computer Science; he is collaborating with our group on Large Language Models for Physics (Spring 2024)
Neeltje is a Physics/Data Science double major; they are collaborating with our group on Large Language Models for Physics (Spring 2024)
Former Members (students, postdocs, and researchers)
- Rini Gupta (Data Science), William & Mary; Ind. Research in Data Science, DATA 490-04; Multi-objective Optimizaton of aerogel material reinforced with fibers (software stack based on Geant4 and Elmer) (Spring 2023). Current Position: UC Berkeley Data Science Master, Appian Corporation
- Marco Diez, BSc, U. Studi Torino, MIT: BSc Thesis: (year 2020, defended) Porosity Optimization in Nanoporous materials via Machine Learning [link] Advisors: G. Romano, C. Fanelli. Current position: Industry