ALPACA: modern algorithms in machine learning and data analysis: from medical physics to research with accelerators and in underground laboratories

Speaker List

• Lucio Anderlini (INFN Firenze)
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    Generative models at the LHC

• Ilya Auslender (University of Trento)
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    Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis

• Andrea Barontini (University of Milan)
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    Neural networks techniques for parton distribution functions evaluation

• Fabio Catalano (CERN)
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    ML activities in the ALICE experiment

• Marco Cristoforetti (FBK)
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    Prominence of data preparation in geomagnetic storm prediction using deep learning

• Andrea Di Luca (FBK)
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    Deep Learning for Flavor Tagging at ATLAS experiment

• Tommaso Dorigo (INFN Sezione di Padova)
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    Toward the end-to-end optimization of detectors for fundamental physics

• Kamil Dulski (LNF)
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    Analysis of positronium decays by the J-PET detector for the medical and fundamental physics studies

• William Korcari (University of Hamburg)
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    Generative Models for Particle Physics Data

• Wojciech Krzemien (National Centre for Nuclear Research)
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    ML applications: from Particle Physics to Medical Imaging

• Daniel Lang
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    Computational Imaging and AI in Medicine

• Bartosz Leszczynski (M. Smoluchowski Institute of Physics, Jagiellonian University)
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    Challenges in modern preclinical and clinical imaging

• Alessio Lugnan (University of Trento)
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    Photonic spiking neural networks using silicon microring resonators.

• Laura Manduchi
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    Informed Representation Learning with Deep Generative Models

• Simone Manti
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    ML in SIDDHARTA-2: the monitoring challange

• Margherita Mele (University of Trento)
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    Loss is more: exploring the network weight space to harvest information on its training dataset

• Roberto Menichetti (Physics Department, University of Trento)
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    Simplify to understand: extracting insight from high-dimensional simulation datasets of biological systems via information-theoretic approaches

• Fabrizio Napolitano (INFN - Laboratori Nazionali di Frascati, Italy)
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    ML and Differentiable Programming optimisations for X-ray spectroscopy

• Andrea Palladino (GSK)
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    Machine learning and mathematical models: the vaccines of the future

• Takehiko Saito (RIKEN and GSI)
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    Hypernuclear physics with machine learning

• Guido Sanguinetti
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    Machine learning methodologies for single-cell ‘omic data

• Andre Scaffidi (SISSA)
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    Anomaly aware machine learning for dark matter direct detection at DARWIN

• Oliver Schulz (MPI for Physics, Munich)
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    Bayesian statistics in Julia

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    Hands-on

• Florin Sirghi (INFN-LNF)
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    The SIDDHARTA-2 Experiment at DAFNE

• Jakub Skowronski (Universita degli Studi di Padova)
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    Neural networks for experimental nuclear astrophysics

• Thomas Spieker (Robert Bosch GmbH)
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    The ML validation challenge

• Sofia Vallecorsa
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    Quantum Computing for Machine Learning

• Kavya Valsan Eliyan
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    Investigation of o-Ps Decays for Improving CP Symmetry Test Precision with the J-PET Detector

• Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
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    AI-assisted design of experiments at the frontiers of computation: methods and new perspectives