2nd workshop on Artificial Intelligence for the Electron Ion Collider

Speaker List

• Corentin Allaire (IJCLAB)
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    Machine Learning in ACTS

• Roberto Ammendola (INFN/Roma2)
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    AI for streaming readout: an architectural perspective

• Siodmok Andzrej (Jagiellonian U.)
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    Modeling Hadronization Using ML and the Cluster Model

• E. C. Aschenauer (BNL)
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    EIC schedule and overview

• Max Balandat (Meta/AI Facebook )
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    Tutorial on Ax/Botorch: Multi-Objective Optimization

• Joo Balint (ORNL)
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    AI/Ml with HPC

• Mariangela Bondi (INFN/Catania)
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    Streaming Readout for Next Generation Electron Scattering Experiments

• Nathan Branson (Messiah U.)
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    ML for calorimetry

• Thomas Britton (JLAB)
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    AI for Experimental Controls

• Anja Butter
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    overview talk on ML-based unfolding

• Cameron Dean (MIT)
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    ML for Heavy Flavor Identification

• Markus Diefenthaler (Jefferson Lab)
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    INDRA-ASTRA

• Rolf Ent (Jefferson Lab)
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    EIC schedule and overview

• Cristiano Fanelli (W&M)
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    Future

• Manouchehr Farkhondeh (US Department of Energy, Office of Nuclear Physics)
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    DOE perspective on opportunities for AI in nuclear physics

• Elena Fol (CERN)
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    ML application for beam optics control in the LHC

• Sergey Furletov (JLab)
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    Fast ML for FPGA

• James Giroux (U. Ottawa)
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    Data-driven learning: Flux+Mutability

• Astrid Hiller Blin (Thomas Jefferson National Accelerator Facility)
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    A(I)DAPT

• Jin Huang (Brookhaven National Lab)
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    AI-based data reduction for streaming DAQ

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    Panel Discussion

• Raghav Kunnawalkam Elayavalli (Vanderbilt University)
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    Tagging heavy flavor jets @ RHIC

• Frank Liu
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    AI/ML hardware co-design

• simonetta liuti (university of virginia)
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    ML for QCD Analysis - 3D Imaging

• Matthew McEneaney (Duke)
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    Lambda event tagging at CLAS12

• Tony Menzo (U. Cincinnati)
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    Modeling Hadronization Using ML and the Lund String Model

• Tia Miceli (Fermilab)
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    Panel Discussion

• Bogdan Mihaila (National Science Foundation)
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    NSF perspective on opportunities for AI in nuclear physics

• Williams Mike (MIT)
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    Panel Discussion

• Vinicius Mikuni (Other@lbl.gov;Staff@lbl.gov;Member@lbl.gov)
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    Tutorial on Unfolding

• Benjamin Nachman
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    AI-driven detector design for the EIC

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    Differentiable Simulations

• Connor Pecar (Duke University)
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    Reconstructing DIS and SIDIS properties

• Chao Peng (Argonne National Laboratory)
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    ML particle identification with measured shower profiles from calorimetry

• William Phelps (Christopher Newport University/Jefferson Lab)
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    Machine Learning in Spectroscopy and Partial Wave Analysis

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    Muon Identification with Deep Learning at EIC

• Kishansingh Rajput (Jefferson Lab)
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    Tutorial on MLFlow

• Yihui Ren
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    Tutorial on Graph Neural Networks

• nobuo sato (Jefferson Lab)
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    Femtoscale Imaging of Nuclei using ML and Exascale Platforms

• Todd Satogata (Brookhaven National Lab)
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    AI/ML overview for accelerator design activities

• David Shih (Rutgers)
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    Fast Detector Simulations with Machine Learning

• karthik suresh (University of Regina)
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    Adaptive experimentation to assist detector design at EIC: from ECCE to ePIC and beyond

• Fernando Torales - Acosta
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    Tutorial on Unfolding

• Nhan Tran (Fermilab)
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    Machine Learning with FPGA

• Svitlana Volkova
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    Foundation Model Infrastructure

• Daniel Whiteson (UC Irvine)
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    Interpretable Networks for Identifying Leptons