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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Dear faculty members and fellow students,
You are cordially invited to attend my thesis defense.
Title: Statistical Learning and Decision Making for Spatio-Temporal Data
Date: April 8th, 2022
Time: 12:00 pm – 1:30 pm
Location: https://bluejeans.com/5007129655 (BlueJeans meeting link) / Main 126
Student Name Shixiang (Woody) Zhu
Machine Learning PhD Student
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Committee
1 Yao Xie (Advisor)
2 Dr. He Wang (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
3 Dr. Pascal Van Hentenryck (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
4 Dr. George Nemhauser (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
5 Dr. Feng Qiu (Argonne National Laboratory)
Abstract
Spatio-temporal data modeling and sequential decision analytics are a growing area of research with an enormous amount of modern spatio-temporal data being consistently collected from the real world. These applications include power grids, public safety systems, healthcare systems, financial markets, social media, IoT networks, and even our personal mobile devices. Understanding the intricate spatio-temporal dynamics behind these data requires the next generation of mathematical and statistical algorithms based on quantitative models of human and physical dynamics. In this thesis, we first present the recent developments in this area with both methodological advances and various real-world applications. Then we develop new theoretical and algorithmic techniques for capturing the dynamics of real-world spatio-temporal data by combining cutting-edge machine learning and classical statistical models. We also formulate the sequential decision making process as an optimization problem in a data driven manner, which could suggest better decisions by taking advantage of the historical knowledge. Lastly, we study a wide array of real-world spatio-temporal datasets using our proposed methods. The results demonstrate the value of spatio-temporal analytics in understanding computational, physical, and social systems.
