<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pietro Vischia | PHINDER EIC Project</title><link>https://phinder-eic.github.io/author/pietro-vischia/</link><atom:link href="https://phinder-eic.github.io/author/pietro-vischia/index.xml" rel="self" type="application/rss+xml"/><description>Pietro Vischia</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://phinder-eic.github.io/author/pietro-vischia/avatar_hu_59522777ea239a6b.jpg</url><title>Pietro Vischia</title><link>https://phinder-eic.github.io/author/pietro-vischia/</link></image><item><title>Pietro Vischia</title><link>https://phinder-eic.github.io/author/pietro-vischia/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/author/pietro-vischia/</guid><description>&lt;p&gt;Ramón y Cajal senior researcher at the Universidad de Oviedo and ICTEA (Spain), Adjunct Professor at IITM. Graduated in 2016 from IST. He is the coordinator of the MODE (Machine-Learning-Optimized Design of Experiments) Collaboration, and the Machine Learning Coordinator of the CMS Experiment at CERN. Specialist in Machine Learning applied to High Energy Physics. Researcher in high-dimensional spaces via gradient descent, eventually powered by quantum algorithms, and on the extension of machine learning methods to realistic neurons with spiking networks, to be then implemented in neuromorphic hardware devices. Within CMS, he focuses on plugging inductive bias in machine learning algorithms for standard model Higgs physics (including the 2018 observation of the ttH process) and beyond-the-standard-model new physics searches in the Top, Higgs, and vector boson sectors.&lt;/p&gt;</description></item><item><title>Differentiable Surrogate for Detector Simulation and Design with Diffusion Models</title><link>https://phinder-eic.github.io/publication/nguyen-2026-wsv/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/nguyen-2026-wsv/</guid><description/></item><item><title>On the Codesign of Scientific Experiments and Industrial Systems</title><link>https://phinder-eic.github.io/publication/dorigo-2026-codesignscientificexperimentsindustrial/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/dorigo-2026-codesignscientificexperimentsindustrial/</guid><description/></item><item><title>Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming</title><link>https://phinder-eic.github.io/publication/aehle-2025/</link><pubDate>Mon, 01 Dec 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/aehle-2025/</guid><description/></item><item><title>Hadron Identification Prospects with Granular Calorimeters</title><link>https://phinder-eic.github.io/publication/de-vita-2025/</link><pubDate>Thu, 01 May 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/de-vita-2025/</guid><description/></item><item><title>AI-assisted design of experiments at the frontiers of computation: methods and new perspectives</title><link>https://phinder-eic.github.io/publication/vischia-2025/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/vischia-2025/</guid><description/></item><item><title>Automatic Optimization of a Parallel-Plate Avalanche Counter with Optical Readout</title><link>https://phinder-eic.github.io/publication/particles-8010026/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/particles-8010026/</guid><description/></item><item><title>Bias Reduction Using Expectation Maximization in the Optimization of an AI-Assisted Muon Tomography System</title><link>https://phinder-eic.github.io/publication/dela-puente-santos-20252-q/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/dela-puente-santos-20252-q/</guid><description/></item><item><title>End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters</title><link>https://phinder-eic.github.io/publication/schmidt-2025-endtoenddetectoroptimizationdiffusion/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/schmidt-2025-endtoenddetectoroptimizationdiffusion/</guid><description/></item><item><title>End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters</title><link>https://phinder-eic.github.io/publication/particles-8020047/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/particles-8020047/</guid><description/></item><item><title>Hadron Identification Prospects with Granular Calorimeters</title><link>https://phinder-eic.github.io/publication/particles-8020058/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/particles-8020058/</guid><description/></item><item><title>Neuromorphic Readout for Hadron Calorimeters</title><link>https://phinder-eic.github.io/publication/particles-8020052/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/particles-8020052/</guid><description/></item><item><title>Optimisation of Muon Tomography Scanners for Border Control Using TomOpt</title><link>https://phinder-eic.github.io/publication/particles-8020053/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/particles-8020053/</guid><description/></item><item><title>TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography</title><link>https://phinder-eic.github.io/publication/strong-2024/</link><pubDate>Mon, 01 Jul 2024 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/strong-2024/</guid><description/></item><item><title>Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper</title><link>https://phinder-eic.github.io/publication/dorigo-2022-endtoendoptimizationparticlephysics/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/dorigo-2022-endtoendoptimizationparticlephysics/</guid><description/></item><item><title>Toward Machine Learning Optimization of Experimental Design</title><link>https://phinder-eic.github.io/publication/baydin-02012021/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/baydin-02012021/</guid><description/></item></channel></rss>