<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fedor Ratnikov | PHINDER EIC Project</title><link>https://phinder-eic.github.io/author/fedor-ratnikov/</link><atom:link href="https://phinder-eic.github.io/author/fedor-ratnikov/index.xml" rel="self" type="application/rss+xml"/><description>Fedor Ratnikov</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Dec 2025 00:00:00 +0000</lastBuildDate><image><url>https://phinder-eic.github.io/media/icon_hu_ebbff252c19052d0.png</url><title>Fedor Ratnikov</title><link>https://phinder-eic.github.io/author/fedor-ratnikov/</link></image><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>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><item><title>ML-assisted versatile approach to Calorimeter R&amp;D</title><link>https://phinder-eic.github.io/publication/boldyrev-2020-ydy/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/boldyrev-2020-ydy/</guid><description/></item><item><title>Using machine learning to speed up new and upgrade detector studies: a calorimeter case</title><link>https://phinder-eic.github.io/publication/ratnikov-2020/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/ratnikov-2020/</guid><description/></item></channel></rss>