Harsh Singhal

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Advances in networking technology have enabled network engineers to use sampled data from routers to estimate network flow volumes and track them over time. However, low sampling rates result in large noise in traffic volume estimates. We propose to combine data on individual flows obtained from sampling with highly aggregate data obtained from SNMP(More)
We study the problem of identifiability of distributions of flows on a graph from aggregate measurements collected on its edges. This is a canonical example of a statistical inverse problem motivated by recent developments in computer networks. In this paper (i) we introduce a number of models for multi-modal data that capture their spatio-temporal(More)
An algorithm using pre-defined regions of interest (ROIs) to detect differences between sessions in Blood Oxygen-Level Dependent (BOLD) signal is proposed and results from a reproducibility study are reported here. It is important to know whether tests for change have the desired statistical properties, e.g., low variability between sessions and unbiased(More)
We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design(More)
—We propose models for the joint distribution of two modalities for network flow volumes. While these models are motivated by computer network applications, the underlying structural assumptions are more generally applicable. In the case of computer network flow volumes, this corresponds to joint modeling for packet and byte volumes and enables computer(More)
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