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Qualifying Exam: Learning Models for Inference and Estimation in Large Scale Spatio-temporal Data


The Internet of Things (IoT) paradigm is seeing a number of inexpensive devices connected to the internet. These devices are generating large amounts of data and are resulting in new classes of applications. With the ubiquity of cellular data channels, mobile devices are also generating large amounts of spatio-temporal data. We propose to enhance three areas of interest in spatio-temporal data analysis - imputation, causal inference and causal estimation from indirect measurements. The proposed approach is to investigate various machine learning models to extract information from spatio-temporal data. We propose to evaluate these models using fine-grained pollution measurement data from metropolitan areas.

Srinivas Devarakonda
CoRE A (301)
Event Date: 
11/20/2018 - 2:30pm
Prof. Badri Nath (Chair), Prof. Desheng Zhang, Prof. Yongfeng Zhang, Prof. Kostas Bekris
Event Type: 
Qualifying Exam