AISIS2019
21-25 October, 2019
Universidad Nacional Autónoma de México, Mexico City, México
published January 28, 2021
Universidad Nacional Autónoma de México, Mexico City, México
published January 28, 2021
The recent progress in Artificial Intelligence and Machine Learning has provided new ways to process large data sets. The new techniques are particularly powerful when dealing with unstructured data or data with complex, non-linear relationships, which are hard to model and analyse with traditional, statistical tools. This has triggered a flurry of activities both in industry and science, developing methods to tackle problems which used to be impossible or extremely hard to deal with.
Due to this situation, we are proposing to organise a meeting where the following key elements would be covered:
- The progress of AI in several scientific domains connected with big data like astro physics, high energy physics, astronomy, and life sciences
- The service providers with their hardware and software developments
- The policy aspects of the AI implementation.
The meeting is oriented towards scientists and educators as well as industry and policy makers in R&D.
Editorial Board
Boris Escalante , CViCom-UNAM
Federico Carminati , CERN
Guy Paic , ICN-UNAM
Lukas Nellen , ICN-UNAM
Rafael Mayo , CIEMAT
Steven Schramm , University of Geneva
Zeljko Ivezic , University of Washington
Editorial Board
- Gergely Gábor Barnaföldi
Wigner Research Centre for Physics
| Sessions |
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| Preface |
| Day 1 |
| Day 2 |
| Day 4 |
| Day 5 |
| Preface |
|---|
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Editor’s Note for the Proceedings of the Artificial Intelligence for Science, Industry and Society – AISIS2019
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| Day 1 |
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Galaxy Morphology classification using CNN
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Generative Model Study for 1+1d-Complex Scalar Field Theory
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Studying the parton content of the proton with deep learning models
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Online Estimation of Particle Track Parameters based on Neural Networks for the Belle II Trigger System
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Generative Adversarial Networks for Fast Simulation: distributed training and generalisation
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Portraying Double Higgs at the Large Hadron Collider
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| Day 2 |
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Trustworthy AI. The AI4EU approach
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Regulating Emerging Technologies: Opportunities and Challenges for Latin America
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Deep learning for cosmology
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A machine learning approach for the feature extraction of pulmonary nodules
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Skin Lesion Detection in Dermatological Images using Deep Learning
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QUA³CK - A Machine Learning Development Process
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Regularization methods vs large training sets
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| Day 4 |
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Large-Scale Scientific endeavours: the production and dissemination of advance computer sciences knowledge
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Machine Learning-Based System for the Availability and Reliability Assessment and Management of Critical Infrastructures (CASO)
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| Day 5 |
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Robotics, AI and Machine Vision
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Machine learning in accelerator physics: applications at the CERN Large Hadron Collider
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Policies for Artificial Intelligence in Science and Innovation
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Quantum Computing Future-Proofing What Lies Beyond SuperComputing
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