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

Website
https://agenda.infn.it/event/32043/

Related events
Machine Learning at GGI (Conference) - Sep 05, 2022






Talks

Date	Speaker	Title	Type	Useful Links
Aug 22, 2022 - 14:30-14:45		Welcome	Introduction
Aug 22, 2022 - 14:45-16:15		Gong show	Introduction
Aug 23, 2022 - 14:30-16:00	Andrea Wulzer (Padova University) , Gaia Grosso (CERN)	Anomaly detection	Lecture					Video
Aug 24, 2022 - 11:00-12:30	Marco Letizia (University of Genova)	Efficient large scale kernel methods for high energy physics	Lecture					Video
Aug 25, 2022 - 11:00-12:30	Lorenzo Giambagli (University of Firenze) , Matilde Signorini (University of Firenze)	Non-parametric analysis of the Hubble Diagram with Neural Networks	Lecture					Video
Aug 26, 2022 - 11:00-12:30	Lorenzo Buffoni (University of Lisbon)	Deep Learning techniques for Genomics	Lecture					Video
Aug 29, 2022 - 11:15-12:45	Manuel Szewc (Jozef Stefan Institute, Ljubljana)	Interpretable graphical models for collider studies	Lecture					Video
Aug 30, 2022 - 11:00-12:30	Jesse Thaler (MIT)	Machine learning for HEP	Lecture					Video
Aug 31, 2022 - 11:00-12:30	Alessandra Cappati (LLR, Ecole Polytechnique, Paris) , Robert Schöfbeck (HEPHY Vienna)	Exploring EFT with ML at the LHC	Lecture					Video
Sep 01, 2022 - 11:00-12:30	Marat Freytsis (Rutgers University)	Recurrent NNs for fast signal discovery in pulsar timing arrays	Lecture					Video
Sep 02, 2022 - 11:00-12:30	Eliska Greplova (TU Delft)	Learning of Phases of Matter: What’s Next?	Lecture					Video
Sep 12, 2022 - 11:00-11:15		Welcome	Introduction
Sep 12, 2022 - 11:00-12:30	David Shih	Introduction to normalizing flows and some applications to LHC and Gaia	Lecture		Slides			Video
Sep 13, 2022 - 11:00-12:30	Guido D'Amico	The Cosmological Analysis of Large-Scale-Structure Data	Lecture					Video
Sep 14, 2022 - 11:00-12:30	Jeff Byers (University of Padova)	Machine Learning and Physics: A Faustian Bargain?	Lecture		Slides
Sep 15, 2022 - 11:00-12:30	Uros Seljak	Deterministic Langevin and Hamiltonian methods for sampling	Lecture					Video
Sep 16, 2022 - 11:00-12:30	Lorenzo Giambagli	Spectral Learning for Neural Network	Lecture					Video
Sep 19, 2022 - 11:00-11:15		Welcome	Introduction
Sep 19, 2022 - 11:15-12:45	Lorenzo Chicchi	Spectral Learning	Lecture					Video
Sep 20, 2022 - 11:00-12:30	Giacomo Mazzamuto	Large-scale imaging and feature extraction using advanced high-resolution microscopy techniques	Lecture					Video
Sep 21, 2022 - 11:00-12:30	Nayara Fonseca, Veronica Guidetti	Generalization in Similarity Learning	Lecture					Video
Sep 21, 2022 - 15:00-16:00	Mara Salvato	ML applied to Identification/characterisation of X-ray sources and open problems	Talk					Video
Sep 22, 2022 - 11:00-12:30	Stefano Forte	PDF Determination as Machine Learning	Lecture					Video
Sep 26, 2022 - 11:00-11:15		Welcome	Introduction
Sep 27, 2022 - 11:00-12:30	Mario Krenn	TBA	Lecture					Video
Sep 28, 2022 - 11:00-12:30	David Berman	On the Dynamics of Inference and Learning	Lecture					Video
Sep 28, 2022 - 15:00-16:00	Sebastiano Ariosto	Universal mean-field upper bound for the generalization gap of deep neural network	Lecture
Sep 29, 2022 - 11:00-12:30	Benedetta Camaiani	Model independent measurements of Standard Model cross sections with Domain Adaptation	Lecture
Sep 29, 2022 - 15:00-16:00	Paolo Nesi	TBA	Lecture