You are kindly invited to attend Shixiang (Woody) Zhu's Ph.D. proposal presentation. Please see the details below.
Title: Statistical Learning and Decision Making for Spatio-temporal Data
Date: April 8th, 2021
Time: 11 AM EST
Student Name: Shixiang (Woody) Zhu, Machine Learning Ph.D. Student
Home School: H. Milton Stewart School of Industrial & Systems Engineering
Georgia Institute of Technology
Dr. Yao Xie (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
1 Dr. Pascal Van Hentenryck (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
2 Dr. George Nemhauser (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
3 Dr. Feng Qiu (Argonne National Laboratory)
4 Dr. He Wang (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)
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 proposal, 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.