Machine Learning at GGI - Conference

Europe/Rome
Descrizione

 

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

    • 1
      Registration
    • 10:30
      Coffee break
    • 2
      Welcome
    • Condensed Matter/QI
      • 3
        Machine Learning and Large Scale Quantum Systems
        Relatore: Eliska Greplova
      • 4
        Machine Learning for quantum noise discrimination
        Relatore: Stefano Martina
      • 5
        Spectral Pruning for Neural Network
        Relatore: Lorenzo Giambagli
    • 13:00
      Lunch
    • Astro/Cosmo
    • Astro/Cosmo
      • 8
        Recurrent NNs for fast signal discovery in pulsar timing arrays
        Relatore: Marat Freytsis
    • 16:15
      Coffee break
    • HEP
      • 9
        Using Machine Learning for Physics: The Underlying Mathematical Assumptions
        Relatore: Jeff Byers
      • 10
        Product Jacobi-Theta Boltzmann machines with score matching
        Relatore: Andrea Pasquale
    • 10:10
      Coffee break
    • Condensed Matter/QI
      • 11
        Quantum Computing & Quantum Machine Learning
        Relatore: Filippo Caruso
      • 12
        Algorithmic exploitation of the energy landscape in non-convex neural networks
        Relatore: Riccardo Zecchina
      • 13
        Coarse-graining for classical and quantum systems
        Relatore: Cecilia Clementi
      • 14
        Bridge between Classical & Quantum Machine Learning
        Relatore: Jack Araz
    • 13:00
      Lunch
    • Astro/Cosmo
      • 15
        Cosmological simulations of Galaxy formation supervised by Machine Learning algorithms
        Relatore: Luca Graziani
      • 16
        Reconstructing blended galaxies with Machine Learning
        Relatore: Lavanya Nemani
      • 17
        Digging into data cubes with Deep Learning
        Relatore: Giuseppe Longo
    • 16:15
      Coffee break
    • HEP
      • 18
        Style-based quantum generative adversarial networks for Monte Carlo events
        Relatore: Stefano Carrazza
      • 19
        PDF determination: from NN fitting to posterior sampling
        Relatore: Alessandro Candido
    • 10:10
      Coffee break
    • HEP
      • 20
        The LHC Physics case
        Relatori: Marco Zanetti (Istituto Nazionale di Fisica Nucleare), Marco Zanetti (CERN)
      • 21
        Learning New Physics from a Machine
        Relatore: Gaia Grosso
      • 22
        Efficient nonparametric methods for statistical anomaly detection
        Relatore: Marco Letizia
      • 23
        Challenges for unsupervised anomaly detection in particle physics
        Relatore: Katherine Fraser
    • 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
        Relatore: Federico Ricci Tersenghi
      • 25
        Quantum associative memory for quantum image recognition
        Relatore: Sreetama Das
      • 26
        Reinforcement Learning for Quantum Technologies
        Relatore: Florian Marquardt
    • 16:15
      Coffee break
    • Astro/Cosmo
      • 27
        Applying DNN to extract nuclear matter properties from neutron stars
        Relatore: Marcio Ferreira
    • 28
      Visit to Galileo's Villa il Gioiello
    • 10:10
      Coffee break
    • Astro/Cosmo
      • 29
        NCPA mitigation using PCA and Neural Network
        Relatore: Alessandro Terreri
      • 30
        Accelerating cosmological inference with Deep Learning
        Relatore: Marco Bonici
      • 31
        Graph-Convolutional Neural Networks as a new tool to extract clustering information of the Cosmic web
        Relatore: Farida Farsian
      • 32
        Optimal compression of the cosmic 21-cm signal
        Relatore: David Prelogovic
    • 13:00
      Lunch
    • HEP
    • 16:15
      Coffee break
    • HEP
    • 10:10
      Coffee break
    • Astro/Cosmo
      • 38
        Translation and Rotation Equivariant Normalizing Flows for cosmology and beyond
        Relatore: Uros Seljak
    • HEP
      • 39
        Particle Cloud Generation with Message Passing GANs
        Relatore: Raghav Kansal
      • 40
        Gradient estimators for normalising flows
        Relatore: Piotr Bialas
      • 41
        Machine learning for supersymmetric matrix models
        Relatore: Enrico Rinaldi
    • 13:00
      Lunch
    • Condensed Matter/QI
      • 42
        Rapid Exploration of Topological Band Structures Using Deep Learning
        Relatore: Vittorio Peano
      • 43
        Renormalized Mutual Information for Artificial Scientific Discovery
        Relatore: Leopoldo Sarra
      • 44
        Quantum density peak clustering
        Relatore: Lorenzo Buffoni
    • 16:15
      Coffee break