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Machine Learning at GGI - Conference

→ Europe/Rome
Description

 

Abstract
Machine learning (ML) is nowadays an important toolbox for theoretical and experimental physics, and its importance is expected to steadily grow in the coming years. Thanks to its effectiveness and extreme flexibility, it allows for applications covering a huge set of topics, ranging from statistical data analysis, to simulation and modeling. For this reason ML has been successfully used in very different research areas, such as high-energy physics, astrophysics and cosmology, condensed matter and statistical physics.

Applications in different domains often share strong similarities either in the problems to be solved or in the methodology employed. This motivates a fruitful exchange of ideas, which however is seldom achieved in practice due to the distance among different research communities.

The aim of the workshop is to bring together researchers with interests and expertise in ML from different fields in physics, strongly encouraging and promoting cross-topic exchange of ideas and collaborations. Three broad research areas will be covered:
- High-Energy Physics
- Astrophysics, Cosmology and Astroparticles
- Condensed Matter and Statistical Physics (including Quantum Information)

The distinctive trait of the workshop will be the focus on theoretical physics in a broad sense, including data analysis as well as simulation and modelling.



Topics
- Methods for regression and statistical analysis
- Monte Carlo integration and simulation
- Anomaly detection
- Classification
- Time series analysis
- Clustering and multi-dimensional visualization
- Equation solving
- Artificial intelligence-inspired and -augmented science
- Statistical physics algorithms for optimization and learning problems
- Quantum machine learning


Organizers
Massimo Brescia (INAF Napoli)
Filippo Caruso (U. Firenze)
S. George Djorgovski (Caltech)
Duccio Fanelli (U. Firenze)
Alessandro Marconi (U. Firenze)
Florian Marquardt (Max Planck Erlangen)
Giuliano Panico (U. Firenze)
Jesse Thaler (MIT)
Andrea Wulzer (CERN & U. Padova)


Local organizer
Giuliano Panico (U. Firenze)

Contact
giuliano.panico@unifi.it

  • Monday, 5 September
    • 1
      Registration
    • 10:30
      Coffee break
    • 2
      Welcome
    • Condensed Matter/QI
      • 3
        Machine Learning and Large Scale Quantum Systems
        Speaker: Eliska Greplova
      • 4
        Machine Learning for quantum noise discrimination
        Speaker: Stefano Martina
        slides_martina.pdf
      • 5
        Spectral Pruning for Neural Network
        Speaker: Lorenzo Giambagli
    • 13:00
      Lunch
    • Astro/Cosmo
    • Astro/Cosmo
      • 8
        Recurrent NNs for fast signal discovery in pulsar timing arrays
        Speaker: Marat Freytsis
    • 16:15
      Coffee break
    • HEP
      • 9
        Using Machine Learning for Physics: The Underlying Mathematical Assumptions
        Speaker: Jeff Byers
        Byers_Conf_Talk_GGI_Sep5.pptx
      • 10
        Product Jacobi-Theta Boltzmann machines with score matching
        Speaker: Andrea Pasquale
        talk_Pasquale.pdf
  • Tuesday, 6 September
    • 10:10
      Coffee break
    • Condensed Matter/QI
      • 11
        Quantum Computing & Quantum Machine Learning
        Speaker: Filippo Caruso
      • 12
        Algorithmic exploitation of the energy landscape in non-convex neural networks
        Speaker: Riccardo Zecchina
      • 13
        Coarse-graining for classical and quantum systems
        Speaker: Cecilia Clementi
      • 14
        Bridge between Classical & Quantum Machine Learning
        Speaker: Jack Araz
        jackaraz_ggi.pdf
    • 13:00
      Lunch
    • Astro/Cosmo
      • 15
        Cosmological simulations of Galaxy formation supervised by Machine Learning algorithms
        Speaker: Luca Graziani
      • 16
        Reconstructing blended galaxies with Machine Learning
        Speaker: Lavanya Nemani
        GGI Presentation_Nemani.pdf
      • 17
        Digging into data cubes with Deep Learning
        Speaker: Giuseppe Longo
        ML@GGI-Longo.pdf
    • 16:15
      Coffee break
    • HEP
      • 18
        Style-based quantum generative adversarial networks for Monte Carlo events
        Speaker: Stefano Carrazza
      • 19
        PDF determination: from NN fitting to posterior sampling
        Speaker: Alessandro Candido
        talk_Candido.pdf
  • Wednesday, 7 September
    • 10:10
      Coffee break
    • HEP
      • 20
        The LHC Physics case
        Speakers: Marco Zanetti (Istituto Nazionale di Fisica Nucleare), Marco Zanetti (CERN)
        AnomaliesAtLHC_GGI.pptx
      • 21
        Learning New Physics from a Machine
        Speaker: Gaia Grosso
        ML_GGI_conference_GGrosso.pdf
      • 22
        Efficient nonparametric methods for statistical anomaly detection
        Speaker: Marco Letizia
      • 23
        Challenges for unsupervised anomaly detection in particle physics
        Speaker: Katherine Fraser
        Fraser_GGI_2022_Talk.pdf
    • 13:00
      Lunch
    • Condensed Matter/QI
      • 24
        A theory explaining the limits and performances of algorithms based on simulated annealing in solving sparse hard inference problems
        Speaker: Federico Ricci Tersenghi
      • 25
        Quantum associative memory for quantum image recognition
        Speaker: Sreetama Das
      • 26
        Reinforcement Learning for Quantum Technologies
        Speaker: Florian Marquardt
    • 16:15
      Coffee break
    • Astro/Cosmo
    • 28
      Visit to Galileo's Villa il Gioiello
  • Thursday, 8 September
    • 10:10
      Coffee break
    • Astro/Cosmo
      • 29
        NCPA mitigation using PCA and Neural Network
        Speaker: Alessandro Terreri
      • 30
        Accelerating cosmological inference with Deep Learning
        Speaker: Marco Bonici
      • 31
        Graph-Convolutional Neural Networks as a new tool to extract clustering information of the Cosmic web
        Speaker: Farida Farsian
      • 32
        Optimal compression of the cosmic 21-cm signal
        Speaker: David Prelogovic
    • 13:00
      Lunch
    • HEP
    • 16:15
      Coffee break
    • HEP
  • Friday, 9 September
    • 10:10
      Coffee break
    • Astro/Cosmo
      • 38
        Translation and Rotation Equivariant Normalizing Flows for cosmology and beyond
        Speaker: Uros Seljak
    • HEP
      • 39
        Particle Cloud Generation with Message Passing GANs
        Speaker: Raghav Kansal
        ML_GGI_09_09_22.pdf
      • 40
        Gradient estimators for normalising flows
        Speaker: Piotr Bialas
      • 41
        Machine learning for supersymmetric matrix models
        Speaker: Enrico Rinaldi
    • 13:00
      Lunch
    • Condensed Matter/QI
      • 42
        Rapid Exploration of Topological Band Structures Using Deep Learning
        Speaker: Vittorio Peano
      • 43
        Renormalized Mutual Information for Artificial Scientific Discovery
        Speaker: Leopoldo Sarra
      • 44
        Quantum density peak clustering
        Speaker: Lorenzo Buffoni
    • 16:15
      Coffee break
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