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1st AI-INFN Advanced Hackathon
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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
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Description
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 10th. 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
- Machine Learning models for experimental Physics
- Quantum Machine Learning
- Machine Learning in Medical Physics
- 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):
- Generative adversarial models as a tool to unfold detector effects;
- Use of a multidimensional CNN for particle identification in the LHCf experiment;
- Quantum Machine Learning applications: classification, anomaly detection and QUBO problems
- 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.
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