1st AI-INFN Advanced Hackathon

Europe/Rome
Classrooms P2B and P4C (University of Padua, Complesso Paolotti)

Classrooms P2B and P4C

University of Padua, Complesso Paolotti

Via Paolotti, 2/A, 35121 Padova PD
Matteo Barbetti (Istituto Nazionale di Fisica Nucleare), Stefano Dal Pra (Istituto Nazionale di Fisica Nucleare), Lucio Anderlini (Istituto Nazionale di Fisica Nucleare), Francesca Lizzi (Istituto Nazionale di Fisica Nucleare), Andrea Paccagnella (Istituto Nazionale di Fisica Nucleare), Marco Verlato (Istituto Nazionale di Fisica Nucleare)
Description

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Welcome to the First edition of the Advanced Artificial Intelligence @ INFN (AI_INFN) hackathon, dedicated to INFN affiliates.  This edition is hosted at INFN Sezione di Padova.

Notably, it is the third Hackathon to happen in Person, so please apply only if you are planning to come to Padua. The logistics allow for ~ 20 participants.

AI_INFN hackathons are developed in continuity with ML_INFN hackathons. You may want to check the indico pages of the first (entry level), second (entry level), third (advanced level), fourth (entry level)  and fifth (advanced level) editions of ML_INFN hackathons, with most of the talks attached as video files.

The mandatory registration process will be open soon.

In case of a number of registrations exceeding the available positions, the applications will be ranked and selected on the basis of the scientific CV of the applicants and of the order of registration.

The successful applicant will be informed by November 1st. Please do not book hotel/flight before a positive confirmation.

The course is to be considered as "advanced level" for Machine Learning topics. The hackathon will be organized over 3 days, distributed as 

  1. Machine Learning models for experimental Physics
  2. Quantum Machine Learning
  3. Machine Learning in Medical Physics
  4. Ongoing developments towards the future of Machine Learning

 The afternoons of the first two days will be devoted to experimenting the various methods and architectures with the help of tutors.

Upon registration, users will be asked to express their preferences for a one of the use cases offered. We will try to

  • whenever possible, satisfy the preference in the order given
  • try to form groups with students with the full range of proficiencies, in order to allow for self-tutoring inside the groups

The list of available use cases for the hackathon are currently (there could be additions depending on the registration process and on the status of other opportunities):

  1. Generative adversarial models as a tool to unfold detector effects;
  2. Use of a multidimensional CNN for particle identification in the LHCf experiment;
  3. Quantum Machine Learning applications: classification, anomaly detection and QUBO problems
  4. Autism Spectrum Disorders (ASD) diagnosis using Magnetic Resonance Imaging and Radiomics

Prerequisites

Technical: access to INFN Cloud provided resources

NOTE: users will use INFN-Cloud resources and this does not require any specific INFN-Cloud authorization. 

Machine learning related knowledge

The hackathon is to be considered as advanced level for Machine Learning. Hence, students are expected to have a starting level of understanding of machine learning and on the technologies it implies. For example

  • good fluency in Python:
    • program flow, definition and use of variables
    • definition and utilization of functions
    • package management (import, install via pip)
  • fluency in numpy:
    • arrays, basic operations, reshaping
  • fluency on matplotlib:
    • how to define and plot a basic figure
  • fluency on online python notebooks
    • (like SWAN, Jupyter, Google Colab)
  • basic concepts of Deep Learning
    • (e.g.) working principle of dense and convolutional layers 
    • epochs, batches, optimizers, metrics...

Registration

Attending the hackathon is free but the registration is mandatory (see the side menu).

The number of participants can be limited, depending on the tutoring and technical capabilities.

Upon registration, we will require:

  • INFN Affiliation
  • Research interests
  • Proficiency level in Machine Learning and related tools
  • Specific interests in Machine learning  techniques
  • The preferred use case for the first and the second day (first and second choice)

Sponsors and support to the event

The event is sponsored by the Padova unit of the Italian Institute for Nuclear Physics (INFN) and by the Physics Department of University of Padua and by the Future Artificial Intelligence Research (FAIR) initiative and by the Italian Research Center on High Performance Computing, Big Data and Quantum Computing (ICSC Foundation).

We gratefully acknowledge support from INFN CNAF and ReCaS Bari for providing hardware infrastructure and technical support.

 

Registration
Register to the first AI_INFN hackathon
    • Experimental data and Artificial Intelligence: Introduction and Lectures Classrooms P2B and P4C

      Classrooms P2B and P4C

      University of Padua, Complesso Paolotti

      Via Paolotti, 2/A, 35121 Padova PD
      • 1
        Welcome
      • 2
        AI in Europe and WLCG vision
        Speaker: Tommaso Boccali (Istituto Nazionale di Fisica Nucleare)
      • 3
        Cloud Veneto and INFN Cloud
        Speaker: Marco Verlato (Istituto Nazionale di Fisica Nucleare)
      • 4
        Hands-on: accessing the Hackathon resources
      • 11:00
        Coffee break
      • 5
        Processing data from the LHCf detector
        Speakers: Mr Andrea Paccagnella (Istituto Nazionale di Fisica Nucleare), Eugenio Berti (Istituto Nazionale di Fisica Nucleare), Giuseppe Piparo (Istituto Nazionale di Fisica Nucleare), Rosa Petrini (Istituto Nazionale di Fisica Nucleare)
      • 6
        Generative models to unfold detector effects
        Speakers: Fabio Rossi (Istituto Nazionale di Fisica Nucleare), Marco Battaglieri (Istituto Nazionale di Fisica Nucleare), Tommaso Vittorini (Istituto Nazionale di Fisica Nucleare)
    • Experimental data and Artificial Intelligence: Hackathon Classrooms P2B and P4C

      Classrooms P2B and P4C

      University of Padua, Complesso Paolotti

      Via Paolotti, 2/A, 35121 Padova PD
      • 7
        Generative Adversarial Networks as a tool to unfold detector effects
        Speakers: Tommaso Vittorini (Istituto Nazionale di Fisica Nucleare), Marco Battaglieri (Istituto Nazionale di Fisica Nucleare), Fabio Rossi (Istituto Nazionale di Fisica Nucleare)
      • 8
        Use of a multidimensional CNN for particle identification in the LHCf experiment
        Speakers: Mr Andrea Paccagnella (Istituto Nazionale di Fisica Nucleare), Eugenio Berti (Istituto Nazionale di Fisica Nucleare), Giuseppe Piparo (Istituto Nazionale di Fisica Nucleare), Rosa Petrini (Istituto Nazionale di Fisica Nucleare)
      • 16:00
        Coffee break
      • 9
        Generative Adversarial Networks as a tool to unfold detector effects
        Speakers: Tommaso Vittorini (Istituto Nazionale di Fisica Nucleare), Marco Battaglieri (Istituto Nazionale di Fisica Nucleare), Fabio Rossi (Istituto Nazionale di Fisica Nucleare)
      • 10
        Use of a multidimensional CNN for particle identification in the LHCf experiment
        Speakers: Mr Andrea Paccagnella (Istituto Nazionale di Fisica Nucleare), Eugenio Berti (Istituto Nazionale di Fisica Nucleare), Giuseppe Piparo (Istituto Nazionale di Fisica Nucleare), Rosa Petrini (Istituto Nazionale di Fisica Nucleare)
    • QML and MedPhys: Quantum Machine Learning and Machine Learning in Medical Physics Classrooms P2B and P4C

      Classrooms P2B and P4C

      University of Padua, Complesso Paolotti

      Via Paolotti, 2/A, 35121 Padova PD
      • 11
        Introduction to Quantum Machine Learning
        Speakers: Laura Cappelli (Istituto Nazionale di Fisica Nucleare), Matteo Argenton (Istituto Nazionale di Fisica Nucleare), Stefano Giagu (Sapienza Università di Roma and Istituto Nazionale di Fisica Nucleare)
      • 12
        Quantum-inspired tensor-network machine learning: finding optimal hyperparameters, libraries, and hardware

        Tensor-network machine learning is a quantum-inspired method that uses data structures well-known in many-body quantum physics to tackle machine learning tasks. Various ansätze and parameters exist for tensor network algorithms in quantum mechanics, which now can be used as hyperparameters for the quantum-inspired machine learning models. We benchmark hyperparameters, parameters, different Python libraries, e.g., numpy versus torch, and hardware, i.e., CPU versus GPU, to give an intuition for successful and scalable choices amongst the options available in our Quantum TEA "qtealeaves" library.

        Speaker: Daniel Jaschke (Istituto Nazionale di Fisica Nucleare)
      • 11:00
        Coffee
      • 13
        Magnetic Resonance Imaging Seminar
        Speaker: Francesca Brero (Istituto Nazionale di Fisica Nucleare)
      • 14
        Introduction to Medical Physics exercise
        Speaker: Francesca Lizzi (Istituto Nazionale di Fisica Nucleare)
    • QML and MedPhys: Hackathon Classrooms P2B and P4C

      Classrooms P2B and P4C

      University of Padua, Complesso Paolotti

      Via Paolotti, 2/A, 35121 Padova PD
      • 15
        Autism Spectrum Disorders (ASD) diagnosis using structural and functional Magnetic Resonance Imaging and Radiomics
        Speakers: Francesca Lizzi (Istituto Nazionale di Fisica Nucleare), Ian Postuma (Istituto Nazionale di Fisica Nucleare)
      • 16
        Quantum Machine Learning applications: classification, anomaly detection and QUBO problems
        Speakers: Laura Cappelli (Istituto Nazionale di Fisica Nucleare), Matteo Argenton (Istituto Nazionale di Fisica Nucleare), Stefano Giagu (Sapienza Università di Roma and Istituto Nazionale di Fisica Nucleare)
      • 16:00
        Coffee break
      • 17
        Autism Spectrum Disorders (ASD) diagnosis using structural and functional Magnetic Resonance Imaging and Radiomics
        Speakers: Francesca Lizzi (Istituto Nazionale di Fisica Nucleare), Ian Postuma (Istituto Nazionale di Fisica Nucleare)
      • 18
        Quantum Machine Learning applications: classification, anomaly detection and QUBO problems
        Speakers: Laura Cappelli (Istituto Nazionale di Fisica Nucleare), Matteo Argenton (Istituto Nazionale di Fisica Nucleare), Stefano Giagu (Sapienza Università di Roma and Istituto Nazionale di Fisica Nucleare)
    • 19
      Social dinner
    • Future directions: Talks, seminars and discussions Classrooms P2B and P4C

      Classrooms P2B and P4C

      University of Padua, Complesso Paolotti

      Via Paolotti, 2/A, 35121 Padova PD
      • 20
        Developing Artificial Intelligence for Experiments at Future Collider
        Speaker: Dr Riccardo Torre (Istituto Nazionale di Fisica Nucleare)
      • 21
        Machine Learning in Astroparticles
        Speaker: Ilaria Viale (Istituto Nazionale di Fisica Nucleare)
      • 22
        Personal data, Ethics and Artificial Intelligence
        Speaker: Francesca Lizzi (Istituto Nazionale di Fisica Nucleare)
      • 11:00
        Coffee
      • 23
        Advanced features of the AI_INFN Platform
      • 24
        Hands-on AI_INFN Platform
    • Future directions: Demo Classrooms P2B and P4C

      Classrooms P2B and P4C

      University of Padua, Complesso Paolotti

      Via Paolotti, 2/A, 35121 Padova PD
      • 25
        Customizing the software environment with conda and apptainer
        Speaker: Lucio Anderlini (Istituto Nazionale di Fisica Nucleare)
      • 26
        Final remarks and closure
        Speaker: Francesca Lizzi (Istituto Nazionale di Fisica Nucleare)