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<!DOCTYPE html>
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<p>1st International Workshop on</p>
<h1 class="mb-4 pb-0"><span>M</span>achine <span>L</span>earning for <span>I</span>rregular <span>T</span>ime <span>S</span>eries (ML4ITS2021)</h1>
<p class="mb-4 pb-0">Co-located with Virtual <a href="https://2021.ecmlpkdd.org/">ECML PKDD 2021</a></p>
<p class="mb-4 pb-0">Submission <a href =" https://easychair.org/conferences/?conf=ml4its2021">HERE</a> </p>
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<h2>Multivariate (graph) time-series imputation</h2>
<p>Cesare Alippi</p>
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<p>Dealing with missing values and incomplete time series is a labor-intensive and time-consuming inevitable task when handling with data coming from real-world applications, such as those related to cyberphysical systems and the Internet of Things.
<p>Indeed, effective spatio-temporal representations allow imputation methods to reconstruct missing temporal data by exploiting information coming from sensors at different locations. However, standard methods fall short in capturing the nonlinear time and space dependencies existing within networks of interconnected sensors and do not take full advantage of the available – and often strong – relational information.</p>
<p>Starting from state-of-the-art imputation methods including those based on deep learning that do not explicitly model relational aspects, we gently move to graph neural networks and show how the technology can be the ultimate ground in the context of multivariate time series imputation. </p>
<h2>About</h2>
<p>CESARE ALIPPI received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Professor with the Politecnico di Milano, Milano, Italy and Università della Svizzera italiana, Lugano, Switzerland. He is a visiting professor at the Guangdong University of Technology, Guangzhou, China. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN). </p>
<!--<p> Alippi is an IEEE Fellow, Member of the Administrative Committee of the IEEE Computational Intelligence Society, Board of Governors member of the International Neural Network Society, Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, past associate editor of the IEEE Transactions on Emerging topics in computational intelligence, the IEEE Computational Intelligence Magazine, the IEEE-Transactions on Instrumentation and Measurements, the IEEE-Transactions on Neural Networks.
In 2018 he received IEEE CIS Outstanding Computational Intelligence Magazine Award, the 2016 Gabor award from the International Neural Networks Society and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. </p>
<p>Current research activity addresses adaptation and learning in non-stationary environments, graph learning and Intelligence for embedded, IoT and cyber-physical systems.</p>
<p>He holds 8 patents, has published one monograph book, 7 edited books and about 200 papers in international journals and conference proceedings.</p>-->
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