Join us in welcoming Srijan Sengupta, an assistant professor in the Statistics Department at NC State, as he shares his strategies for scalable inference from network data.
https://ncsu.zoom.us/j/92584004020?pwd=S3ovcmJQU2h5MElEYTdleW1oVVp5dz09
Meeting ID: 925 8400 4020
Passcode: 503038
Two generalizable strategies for scalable inference from network data
Massive network datasets are becoming increasingly common in domains like epidemiology, neuroscience, and digital health. Existing statistical inference methods are computationally infeasible for such massive networks. This talk will introduce two generalizable strategies for scalable inference from network data: aggregative subsampling (AggSub) and predictive subsampling (PredSub). AggSub is a divide-and-conquer algorithm where the original network is split into multiple subnetworks with a common overlap. Statistical inference is carried out for each subnetwork, and the results from individual subnetworks are aggregated by leveraging the overlap. The core idea of predictive subsampling is to avoid large-scale matrix computations by
breaking up the task into a smaller matrix computation plus a large number of vector computations that can be carried out in parallel. Under the proposed method, the inferential task of interest is carried out on a small subgraph to estimate the relevant model parameters. The remaining nodes are added one by one using only vector computations. These two strategies apply to various inference tasks, such as community detection, parameter estimation, model selection, and hypothesis testing.
Srijan Sengupta is an Associate Professor of Statistics at North Carolina State University, where he has been working since Aug 2020. He received my Ph.D. in Statistics from the University of Illinois at Urbana-Champaign in Jul 2016. From Aug 2016 to Aug 2020, he was a tenure-track Assistant Professor of Statistics at Virginia Tech. Dr. Sengupta is interested in developing formal inferential algorithms for network data and applying such algorithms to epidemiology, social sciences, and environmental health. He is also developing a statistical science of patient safety, focusing on adverse medical events due to human errors, medical devices, drug reactions, and radiation therapy.
