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Invited Speakers
Invited Speakers
will talk about incorporating constraints from physics into learning and representation in machine learning.
Title: Guiding the search for patterns: (Machine) Learning from examples
Title: Opening New Potential for Discovery of New Physics: Machine Learning for HEP
Machine learning (ML) is becoming an integral part of particle physics. These techniques are emerging as a competitive tool for analyzing and extracting information from large volumes of complex high-dimensional data. In the last few years, the High Energy Physics community has adopted and customized a variety of ML techniques for various steps of data analysis, e.g., event reconstruction, particle identification, jet tagging, signal/background classification, etc. In this talk, I plan to give an overview of recent developments using ML to perform model-independent searches and how these new ideas can open new potential for discovering new physics.
will talk about the impact of machine learning in HEP.
Title: Introduction to Machine Learning for High Energy Physics
Title: Investigating phase transitions with machine learning methods
Phase transitions occur in a variety of settings and situations in particle physics, condensed matter and statistical mechanics. The traditional approach to phase transitions, based on elegant principles such as scaling and universality, assumes some basic a priori knowledge of the physical system, in particular of the Hamiltonian and its underlying symmetries. While this information is usually given for granted, a data-driven approach has wider applicability. In this talk, I will present some applications of machine learning starting from Monte-Carlo generated data for phase transitions in simple (but far from trivial!) statistical and quantum field theoretical systems. More in details, I will discuss (a) identification of symmetries; (b) constructions of order parameters; and (c) precise determination of critical temperature and critical exponents. I will conclude showing how machine learning can be used to invert a renormalisation group flow.
Title: Experiences of Data Science in Industry and Academia
Data Scientists are in high demand worldwide in both industry and academia. How do you get into industry? What is it like? What are the differences? Am I skilled enough? Can I go back to academia? What if I want the best of both worlds? In this talk I draw on some of my own experiences to try to answer these questions.
Title: Challenges in gravitational-wave data analyses
Gravitational-wave analyses require significant computational effort; from searches to find the signals, to parameter estimation techniques used to describe the systems they come from. We then take the signal properties and use them to infer properties of our universe; e.g. how accurate is general relativity? and what is the distribution of masses for black holes? I will discuss the methods used and computational effort required for gravitational-wave searches, and the techniques used in further gravitational-wave data analyses.
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