Inverted CERN School of Computing 2025

Speaker List

• Sten Astrand (Lund University)
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    Exercise: Under the Hood of the Snake: Behind the scenes of Python

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    Under the Hood of the Snake: Behind the scenes of Python

• Niels Alexander Buegel
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    Code You Won’t Regret (Too Much): How to Write Maintainable Code

• George Coldstream
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    Exercise: Web for the win! A crash course in building web apps

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    Web for the win! A crash course in building web apps

• Lorenz Gartner (LMU)
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    Exercise: Bayesian inference in particle physics

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    Illuminating the dark side of statistics: Bayesian inference in particle physics

• Andrea Germinario
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    Exercise: The Algorithm Advantage: Outperforming Hardware with Smarter Code

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    The Algorithm Advantage: Outperforming Hardware with Smarter Code

• Peter Hinderberger (Technical University of Munich)
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    Data Processing with FPGAs: Parallel Computing on Compact Configurable Logic

• Kaare Endrup Iversen (Lund University (SE))
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    On equivariance and explainability

• Elizabeth Mamtsits
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    Automate All the Things: CI/CD for the Bold and the Brave

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    Exercise: Automate All the Things: CI/CD for the Bold and the Brave

• Jack Charlie Munday
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    Exercise: LLMs in Production: RAG pipelines and beyond

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    LLMs in Production: RAG pipelines and beyond

• Alberto Pace (CERN)
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    Closing remarks

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    Welcome to the Inverted School of Computing

• Alberto Pimpo
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    Exercise: WASM: the future of computing

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    WASM: the future of computing

• Diogo Reis Santos (CERN)
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    Federated Learning with CAFEIN for Decentralized, Privacy preserving and Secure AI development

• Vasvi Sharma
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    Breaking RSA and picking up the pieces

• Simon Thiele (University of Bonn (DE))
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    Exercise: Maximum Likelihood Fitting

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    Maximum Likelihood Fitting

• Andrea Valenzuela Ramirez (CERN)
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    Exercise: Understanding Large Language Models and their Applications in Code Generation

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    Understanding Large Language Models and their Applications in Code Generation

• Cedric Verstege (KIT - Karlsruhe Institute of Technology (DE))
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    Efficient Workflow Management in High-Energy Physics

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    Exercise: Efficient Workflow Management in High-Energy Physics

• Joel Axel Wulff
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    Exercise: Reinforcement Learning in Particle Accelerators: a practical example

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    Reinforcement Learning in Particle Accelerators: a practical example (1/2)

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    Reinforcement Learning in Particle Accelerators: a practical example (2/2)