Cameron Nowzari

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This paper studies a deployment problem for a group of robots where individual agents operate with outdated information about each other’s locations. Our objective is to understand to what extent outdated information is still useful and at which point it becomes essential to obtain new, up-to-date information.We propose a self-triggered coordination(More)
This article reviews and presents various solved and open problems in the development, analysis, and control of epidemic models. Proper modeling and analysis of spreading processes has been a longstanding area of research among many different fields including mathematical biology, physics, computer science, engineering, economics, and the social sciences.(More)
This paper studies an SI1SI2S spreading model of two competing behaviors over a bilayer network. In particular, we address the problem of determining resource allocation strategies that ensure the extinction of one behavior while not necessarily ensuring the extinction of the other, and pose a marketing problem in which such a model can be of use. Our(More)
This paper studies a distributed event-triggered communication and control strategy that solves the multi-agent average consensus problem. The proposed strategy does not rely on the continuous or periodic availability of information to an agent about the state of its neighbors, but instead prescribes isolated event times where both communication and(More)
In this paper we propose a generalized version of the Susceptible-Exposed-Infected-Vigilant (SEIV) disease spreading model over arbitrary directed graphs. In the standard SEIV model there is only one infectious state. Our model instead allows for the exposed state to also be infectious to healthy individuals. This model captures the fact that infected(More)
This paper considers a multi-agent consensus problem over strongly connected and balanced directed graphs. Unlike many works that consider continuous or periodic communication and control strategies, we are interested in developing an event-triggered algorithm to reduce the overall load of the network in terms of limited communication and control updates.(More)
We propose a mathematical framework, based on conic geometric programming, to control a susceptible-infected-susceptible viral spreading process taking place in a directed contact network with unknown contact rates. We assume that we have access to time series data describing the evolution of the spreading process observed by a collection of sensor nodes(More)