Pavlos Nikolopoulos is a postdoctoral researcher working with the Network Architecture Lab (NAL) at the École Polytechnique Fédérale de Lausanne (EPFL) and the Algorithmic Research in Network Information flow lab (ARNI) at the University of California, Los Angeles (UCLA). He received his PhD from EPFL under the supervision of Prof. Katerina Argyraki, and his MSc from the University of Athens, Greece. Before that, he had worked for several years as a communications engineer for the Greek Air Force. His research interests lie at the intersection of network systems design and statistics, with a focus on network measurements, net neutrality and transparency. For the past two years, he has also worked on information theory and group testing, with a focus on how social/contact networks can improve the performance of traditional group testing algorithms.
New directions for group testing - A community-aware approach.
Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Its main building block is the “pooled test”, which is “negative” if all members that participate in it are healthy. Despite the variety and importance of prior results, traditional work on group testing has typically assumed independent infections. However, contagious diseases among humans, like SARS-CoV-2, have an important characteristic: infections are governed by community spread, and are therefore correlated. In this talk, we explore this observation and we argue that taking into account the community structure when testing can lead to significant savings in terms of the number of tests required to guarantee a given identification accuracy. In particular, we focus on information theoretic bounds on the necessary number of tests and we present community-aware group testing algorithms that can be optimal under assumptions. Moreover, we examine whether and how community-aware group testing can improve the state estimation of well-established epidemiological models.
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