Publications de Pavlos Nikolopoulos

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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. Despite the variety and importance of prior results, traditional work on group testing ha ...

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

Today's Internet is not transparent: when packets get lost or delayed, there is typically no information about where the problem occurred, hence no information about who is responsible. This results in Internet service providers (ISPs) offering service lev ...

SIR (Susceptible, Infected or Recovered) stochastic network models are commonly used to describe the progression of epidemics inside a network. A task of interest in epidemiology is to use these models to estimate the state evolution, both at an individual ...

Proactive testing and interventions are crucial for disease containment during a pandemic until widespread vaccination is achieved. However, a key challenge remains: Can we accurately identify all new daily infections with only a fraction of tests needed c ...

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depe ...

Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. When determining whether a network differentiates against certain traffic, it is importa ...

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency" system. In this syst ...

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in connected communities: each individual participates in one or more communities, and the infection pro ...

We study the problem of group testing with nonidentical, independent priors. So far, the pooling strategies that have been proposed in the literature take the following approach: a hand-crafted test design along with a decoding strategy is proposed, and gu ...

We study the problem of group testing with non-identical, independent priors. So far, the pooling strategies that have been proposed in the literature take the following approach: a hand-crafted test design along with a decoding strategy is proposed, and g ...

In this paper, we introduce a "discrete-time SIR stochastic block model" that also allows for group testing and interventions on a daily basis. Our model can be regarded as a discrete version of the well-known continuous-time SIR stochastic network model [ ...

We explore a new network-performance metric: flow/path consistency, which captures whether the end-to-end performance of the flows that traverse a network is consistent with the aggregate performance of the network's elements. We propose a formal definitio ...

Pavlos Nikolopoulos

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Community-Aware Group Testing

Caching and Neutrality

Traffic Receipts for Network Transparency

An entropy reduction approach to continual testing

Dynamic Group Testing to Control and Monitor Disease Progression in a Population

Group testing for connected communities

Localizing Traffic Differentiation

Retroactive Packet Sampling for Traffic Receipts

Group testing for overlapping communities

Improving Group Testing Via Gradient Descent

Improving Group Testing via Gradient Descent

Dynamic Group Testing to Control and Monitor Disease Progression in a Population

Flow/Path Performance Consistency