This work is about the phenomon of peer filtering, a natural effect where the sharing dynamics of users in a social platform increases the porportion of true content that survives in the network relative to the amount that is initially incoming. Download paper here Presented at: 2024 INFORMS Conference, 2024 International School and Conference on Network Science(NetSci), 2024 Conference on Network Science and Economics
This work demonstrates the existence of random graphs in which the value of price discrimination is non-neglible but a small amount of information of the network is sufficient for near optimal pricing Presented at: 2023 INFORMS Conference, 2024 Revenue Management Conference
This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library. Paper can be found here
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This work considers the following problem: We have a data consisting of an unidrected network with labelled nodes. We want to be able to realize a unlabeled possibly permuted version of the network publically with revealing details about the network(known as the published graph). The best way to describe the privacy goals is to consider an attacker who has the published graph, the list of ids in the network, and another network they learned outside what we gave them. We call this second graph the crawled graph. The goal of the attack then is to use these two graphs to uncover the ids of nodes in the network. Our work focuses on possible defenses against such attacks while preserving other useful information about the network ex. the degree distribution of the true hidden network and the published graph should be similar.