Physics Events
[PAST EVENT] DS JLAB Faculty Candidate Finalist: Cristiano Fanelli - Colloquium Talk [Zoom]
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- Open to the public
Join Zoom Meeting – Colloquium Talk: https://cwm.zoom.us/j/92254718540
Title: Leverage Data Science in complex Nuclear Physics experiments: from JLab to the future EIC
Abstract: Jefferson Lab and the future Electron Ion Collider are flagship US-based scientific facilities to study the nature of the strong interaction and understand the 'glue' that binds us all. These modern experiments are intrinsically complex and are characterized by unique challenges during different stages, spanning from detector design and simulations to data quality monitoring, calibration and analysis of streams of data in near real-time. Recent years have been characterized by a growing utilization of interdisciplinary approaches to extract knowledge and insights from our data, which in some cases has also fostered the development of novel custom architectures. At the same time data science techniques are being leveraged to create intelligent and automated pipelines for optimization problems. JLab built in the years an AI community and has led many initiatives. A new working group on AI for EIC has been formed after the successful experience of the first workshop on AI4EIC, which saw the participation of physicists, data and computer scientists. My research is centered around the above topics and I will give an overview of some ongoing projects and opportunities to collaborate with JLab and EIC.
Bio: Cristiano is a Research Scientist at the MIT Laboratory for Nuclear Science and at the AI Institute for Artificial Intelligence and Fundamental Interactions. He is Adjunct Professor at the University of Regina. He uses AI to do research on the fundamental properties of the strong force in leading nuclear physics experiments in the US such as Jefferson Lab (JLab) and the future Electron Ion Collider (EIC). He is a former member of the CMS Collaboration at CERN, where as a student he contributed to the Higgs boson analysis in one of the golden channels for its discovery. He is the recipient of the best PhD thesis prize at JLab and the postdoctoral prize at JLab for his research on deep learning used for particle identification. He has also been the recipient of the inaugural EIC-C Fellowship and led the efforts of the first AI-optimized detector design for EIC. He is the co-convener of the Computing Team of ECCE, a proto-collaboration at EIC. He is the convener of the AI working group at the EIC User Group and the organizer of the first workshop on AI applications for EIC. He co-authored more than 100 publications in peer-reviewed journals.
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Lianne Ashburne