AI4EIC 2023 Annual Workshop

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Welcome and Introduction

Tutorial 3

Hackathon

AI/ML for Data Analysis and Theory

AI/ML for ePIC and Beyond

Tutorial 2

Tutorial 1

AI/ML for Accelerators

Foundation Models and Trends in Data Science with Tutorials

AI/ML in Production, Distributed ML

Calibration, Monitoring, and Experimental Control in Streaming Environments

Closing

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Contributions*

A Large Language Model-based Assistant for the Electron Ion Collider (in session "Foundation Models and Trends in Data Science with Tutorials")

Adversarial Methods for Generative Data Understanding (in session "Foundation Models and Trends in Data Science with Tutorials")

AID(2)E: AI-Assisted Detector Design at EIC (in session "AI/ML for ePIC and Beyond")

Anomaly detection at an X-ray FEL (in session "AI/ML for Accelerators")

ATLAS data analysis using a parallelized workflow on distributed cloud-based services with GPUs (in session "AI/ML in Production, Distributed ML")

Autonomous selection of physics events: A RHIC demonstrator for EIC physics (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

Bayesian Optimization Techniques for Accelerator Control and Characterization (in session "AI/ML for Accelerators")

Beam Condition Forecasting with non-destructive measurements at FACET-II (in session "AI/ML for Accelerators")

Chatlas - the ATLAS AI Assistant (in session "Foundation Models and Trends in Data Science with Tutorials")

Decoding inverse problems in QCD with ML-based algorithms (in session "AI/ML for Data Analysis and Theory")

Discussion (in session "AI/ML for Accelerators")

Discussion (in session "AI/ML for Data Analysis and Theory")

Discussion (in session "AI/ML in Production, Distributed ML")

Discussion (in session "AI/ML for ePIC and Beyond")

Discussion (in session "Foundation Models and Trends in Data Science with Tutorials")

Discussion (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

Flash Talks (in session "AI/ML for ePIC and Beyond")

Hadronization Models Using Machine Learning (in session "AI/ML for Data Analysis and Theory")

High Dimensional Unfolding using Machine Learning (in session "AI/ML for Data Analysis and Theory")

Hydra: Computer Vision for Data Quality Monitoring (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

Interpretable Machine Learning applications to Jet Background Subtraction (in session "AI/ML for Data Analysis and Theory")

Introduction and Overview of Foundation Models (in session "Foundation Models and Trends in Data Science with Tutorials")

Introduction: ePIC Overview (in session "AI/ML for ePIC and Beyond")

LHCb Calibration/Alignment (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

Machine Learning applications for collider luminosity maximization (in session "AI/ML for Accelerators")

Machine learning as a service for HEP using cloud (in session "AI/ML in Production, Distributed ML")

Machine learning for digital twin development and polarization optimization at BNL hadron injectors (in session "AI/ML for Accelerators")

ML-based Calibration and Control of the GlueX Central Drift Chamber (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

MLaaS4HEP, Machine Learning for Columnar High Energy Physics Analysis (in session "AI/ML in Production, Distributed ML")

Object Condensation for Track Building in a Backward Electron Tagger at the EIC (in session "AI/ML for ePIC and Beyond")

Online Track Reconstruction and Physics Analysis in CLAS12 (in session "AI/ML in Production, Distributed ML")

Particle identification with machine learning from incomplete data in the ALICE experiment (in session "AI/ML in Production, Distributed ML")

Performance optimization for a scintillating glass electromagnetic calorimeter at EIC (in session "AI/ML for ePIC and Beyond")

Scalable AI/ML Workflow Management Across Distributed Heterogeneous Resources With PanDA (in session "AI/ML in Production, Distributed ML")

The Exa.TrkX Project (in session "AI/ML in Production, Distributed ML")

The EXCLAIM collaboration approach to deeply virtual exclusive processes (in session "AI/ML for Data Analysis and Theory")

The Optimal use of Segmentation for Sampling Calorimeters (in session "AI/ML for ePIC and Beyond")

The Trillion Parameter Consortium (in session "Foundation Models and Trends in Data Science with Tutorials")

Toward a generative modeling analysis of CLAS exclusive 2π photoproduction (in session "Foundation Models and Trends in Data Science with Tutorials")

Towards Fast Calibration with the ePIC Barrel Hadronic Calorimeter (in session "Calibration, Monitoring, and Experimental Control in Streaming Environments")

TrackBERT - Generalist Learning of Trackers for Downstream Tasks (in session "Foundation Models and Trends in Data Science with Tutorials")

Tutorial: Continual Learning (in session "Tutorial 1")

Tutorial: Hackathon (in session "Tutorial 3")

Tutorial: Reinforcement Learning (in session "Tutorial 2")

Uncertainty estimation and RL applications at JLab (in session "AI/ML for Accelerators")

Using Machine Learning to Improve Dynamic Aperture Estimates (in session "AI/ML for Accelerators")

What can AI do for lattice-QCD parton distribution calculations? (in session "AI/ML for Data Analysis and Theory")

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28/11/2023

29/11/2023

30/11/2023

01/12/2023

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