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<!DOCTYPE html>
<html>
<title> MODE Collaboration Home Page</title>
<meta charset="UTF-8">
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<a href="#home" class="w3-bar-item w3-button">HOME</a>
<a href="#about" class="w3-bar-item w3-button w3-hide-small"><i class="fa fa-user"></i> ABOUT</a>
<a href="#news" class="w3-bar-item w3-button w3-hide-small"><i class="fa fa-user"></i> NEWS</a>
<a href="#collaboration" class="w3-bar-item w3-button"><i class="fa fa-user"></i> COLLABORATION</a>
<a href="#papers" class="w3-bar-item w3-button"><i class="fa fa-user"></i> PUBLICATIONS</a>
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<a href="#about" class="w3-bar-item w3-button" onclick="toggleFunction()">ABOUT</a>
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<span class="w3-center w3-padding-large w3-black w3-xlarge w3-wide w3-animate-opacity"><span class="w3-center w3-padding-large w3-black w3-xlarge w3-wide w3-animate-opacity"><br><br><br><br><br><br><br>
Machine-learning Optimized Design of Experiments</span><br>
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<h3 class="w3-center">ABOUT</h3>
MODE (for Machine-learning Optimized Design of Experiments) is a nascent collaboration of physicists and computer scientists who target the use of differentiable programming in design optimization of detectors for particle physics applications, extending from fundamental research at accelerators, in space, and in nuclear physics and neutrino facilities, to industrial applications employing the technology of radiation detection.<br>
We aim to develop a modular, customizable, and scalable, fully differentiable pipeline for the end-to-end optimization of articulated objective functions that model in full the true goals of experimental particle physics endeavours, to ensure optimal detector performance, analysis potential, and cost-effectiveness. <br><br>
The main goal of our activities is to develop an architecture that can be adapted to the above use cases but will also be customizable to any other experimental endeavour employing particle detection at its core. We welcome suggestions, as well as interest in joining our effort, by researchers focusing on use cases for which this technology can be of benefit.<br><br>
The above program has been submitted in a concise form as an <a href="http://www.pd.infn.it/%7Edorigo/eoi_jenas.pdf">expression of interest</a> to the <a href="http://nupecc.org/jenaa/?display=eois">JENAA group</a>.
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<span class="w3-xxlarge w3-text-white w3-wide">News</span>
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<div class="w3-content w3-container w3-padding-64" id="news">
<h3 class="w3-center">NEWS</h3>
<br>
<ul>
<li> Oct. 16: at 4PM there will be a kick-off meeting of the MODE collaboration.
<li> Sep. 8: We will organize a workshop on "Differentiable Programming for Design Optimization", in February or June (depending on the evolution of the COVID-19 situation) in Louvain-la-Neuve (or Padova).
<li> Sep. 6: An open kick-off meeting of the <a href="http://nupecc.org/jenaa/?display=eois">JENAA group</a>, where we will discuss ways to obtain support of the community to start our activities, is scheduled for September 15, 2-4PM CET. Feel free to participate!
The indico page of the meeting <a href="https://indico.cern.ch/event/953923/">is available here</a>.
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<span class="w3-xxlarge w3-text-white w3-wide">Collaboration</span>
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<h3 class="w3-center">COLLABORATION</h3>
<br>
The MODE Collaboration includes: <br><br>
At University of Padova Dr. <b>Tommaso Dorigo</b>, Dr. <b>Pablo De Castro Manzano</b>, Dr. <b>Lukas Layer</b>, Prof. <b>Roberto Rossin</b>, Dr. <b>Giles Strong</b>, Dr. <b>Mia Tosi</b>, Dr. <b>Hevjin Yarar</b> <br>
At Universite' Catholique de Louvain Dr. <b>Andrea Giammanco</b>, Prof. <b>Christophe Delaere</b>, Dr. <b>Pietro Vischia</b> <br>
At Universite' Clermont Auvergne, Prof. <b>Julien Donini</b> and Dr. <b>Djamel Boumediene</b><br>
At the Higher School of Economics of Moscow, Prof. <b>Andrey Ustyuzhanin</b>, Dr. <b>Denis Derkach</b> and Dr. <b>Fedor Ratnikov</b><br><br>
In addition, the following experts in computer science and physics applications of Machine Learning collaborate with MODE:<br>
At CERN Dr. <b>Jan Kieseler</b> <br>
At University of Oxford Dr. <b>Atilim Gunes Baydin</b><br>
At New York University Prof. <b>Kyle Cranmer</b><br>
At Universite' de Liege Prof. <b>Gilles Louppe</b><br>
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<span class="w3-xxlarge w3-text-white w3-wide">Publications</span>
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<div class="w3-content w3-container w3-padding-64" id="papers">
<h3 class="w3-center">PUBLICATIONS</h3>
<br>
Below is a concise list of relevant publications to the research interests of the MODE collaboration:<br><br>
<ul>
<li> <b>A. Gunes Baydin</b>, B.A. Pearlmutter, A.A. Radul, and J.M. Siskind, "Automatic Differentiation in Machine Learning: a Survey", Journal of Machine Learning Research (JMLR) 18 (153) (2018) 1, <a href="http://jmlr.org/papers/v18/17-468.html">http://jmlr.org/papers/v18/17-468.html</a></li>
<li> <b>T. Dorigo</b>, "Geometry Optimization of a Muon-Electron Scattering Detector," Physics Open 4 (2020) 100022, arXiv:200200973[physics.ins-det], <a href="http://doi.org/10.1016/j.physo.2020.100022">doi: 10.1016/j.physo.2020.100022</a>.</li>
<li> <b>T. Dorigo, J. Kieseler, L. Layer and G. Strong</b>, "Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector", <a href="http://arxiv.org/abs/2008.10958">http://arxiv.org/abs/2008.10958</a> [physics.ins-det] (2020).</li>
<li> S. Shirobokov, <b>A. Ustyuzhanin, A. Gunes Badyin</b> et al., "Differentiating the Black-Box: Optimization with Local Generative Surrogates", <a href="http://arxiv.org/abs/2002.04632v1">arXiv:2002.04632v1</a> [cs.LG] (2020).</li>
<li> <b>K. Cranmer</b>, J. Brehmer, and <b>G. Louppe</b>, "The frontier of simulation-based inference", <a href="http://arxiv.org/abs/1911.01429">arXiv:1911.01429</a>[stat.ML] (2019), Proceedings of the National Academy of Sciences.</li>
<li> <b>F. Ratnikov</b>, "Using machine learning to speed up and improve calorimeter R&D", JINST 15 (2020) C05032, <a href="http://doi.org/10.1088/1748-0221/15/05/C05032">doi: 10.1088/1748-0221/15/05/C05032</a>.</li>
<li> <b>F. Ratnikov, D. Derkach,</b> A. Boldyrev, A. Shevelev, P. Fakanov, L. Matyushin, "Using machine learning to speed up new and upgrade detector studies: a calorimeter case", to appear in proceedings of CHEP 2019, <a href="https://arxiv.org/abs/2003.05118">https://arxiv.org/abs/2003.05118</a></li>
<li> A. Boldyrev, <b>D. Derkach, F. Ratnikov,</b> A. Shevelev, "ML-assisted versatile approach to Calorimeter R&D", <a href="https://arxiv.org/abs/2005.07700">https://arxiv.org/abs/2005.07700</a></li>
<li> <b>P. Giubilato</b> et al., "iMPACT: innovative pCT scanner", IEEE Nucl. Science Symposium and Medical Imaging Conference (NSS/MIC) IEEE (2015), <a href="https://ieeexplore.ieee.org/abstract/document/7581240">https://ieeexplore.ieee.org/abstract/document/7581240</a>.</li>
<li> S. Wuyckens, <b>A. Giammanco</b>, P. Demin, and <b>E. Cortina Gil</b>, "A Portable muon telescope based on small and gas-tight Resistive Plate Chambers"<, Phil. Trans. Royal Soc. A377 (2019) 2137, arXiv:1806.06602v2[physics.ins-det] (2018), <a href="http://doi.org/10.1098/rsta.2018.0139">doi: 10.1098/rsta.2018.0139</a>.</li>
<li> <b>J. Kieseler</b>, "Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data", <a href="http://arxiv.org/abs/2002.03605">arXiv:2002.03605</a>[physics.data-an] (2020).</li>
<li> P. de Castro Manzano and <b>T. Dorigo</b>, "INFERNO: Inference-Aware Neural Optimization", Comp. Phys. Commun. 244 (2019) 170; Arxiv:1806.04743v2 [stat.ml] (2018), <a href="http://doi.org/10.1016/j.cpc.2019.06.007">doi: 10.1016/j.cpc.2019.06.007</a> .</li>
<li> J. Brehmer, <b>K. Cranmer</b> et al., "MadMiner: Machine learning-based inference for particle physics", Comput. Softw. Big Sci. 4 (2020) 1, 3, <a href="http://doi.org/10.1007/s41781-020-0035-2">doi: 10.1007/s41781-020-0035-2</a>.</li>
<li> <b>G. Louppe</b>, J. Hermans, and <b>K. Cranmer</b>, "Adversarial Variational Optimization of Non-Differentiable Simulators", PMLR 89:1438-1447, 2019, <a href="http://arxiv.org/abs/1707.07113">arXiv:1707.07113</a>[stat.ML].</li>
<li> <b>K. Cranmer</b>, J. Pavez, and <b>G. Louppe</b>, "Approximating Likelihood Ratios with Calibrated Discriminative Classifiers", <a href="http://arxiv.org/abs/1506.02169">arXiv:1506.02169</a>[stat.ML] (2015).</li>
</ul>
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<span class="w3-xxlarge w3-text-white w3-wide">Contacts</span>
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<h3 class="w3-center">CONTACTS</h3>
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<td width="100" height="55"><a HREF="mailto:dorigo@pd.infn.it">Send e-mail</a></td>
<td width="400">Dr. Tommaso Dorigo<b> </b><br>
First Researcher, INFN-Padova<br>
Email: <a HREF="mailto:dorigo@pd.infn.it"> dorigo (at) pd (dot) infn (dot) it</a>
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<td width="77" height="84">Phone:</td>
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<p> <br>
Office tel.: +39 0499677230 <br>
Mobile tel.: +39 3666995594 <br>
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<td width="77">Snail mail:</td>
<td width="150">
<br> Dipartimento di Fisica e Astronomia "G.Galilei" <br>
via Francesco Marzolo 8,
35131 Padova<br>
ITALY <br>
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