[Eecs_msee] EECS Research Seminar Talk-Geometric and Geospatial Algorithms under Data Conundrums in Geographic Information Systems

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Title: Geometric and Geospatial Algorithms under Data Conundrums in Geographic Information Systems

Abstract: The rapid growth of the need for data in Geographic Information Systems (GIS) to enhance our understanding of the environment has prompted researchers and practitioners across various disciplines to develop efficient algorithmic methods for analyzing the mobility and movements of the entities on maps by discerning geometric features from uncertain, highly granular or even limited data. The data challenge has arisen in domains such as intelligent transportation systems, urban planning, mobility, military surveillance systems, and others over the past few years.
In this talk, I will summarize some of our recent innovations involving realistic data in Computational Geometry and Geospatial Systems. Several methods conducive to understanding the underlying properties of features on geometric data, such as maps and GPS data, will be presented in detail. Additionally, we will explore the practical application of these methods and their relevance to transportation research, e-commerce, and vehicle routing problems. I will discuss the potential avenues these innovations open for marketing, commercialization, and further collaboration.

Biographical Sketch:  Dr. Mirzanezhad is a Postdoctoral Researcher at the University of Michigan -- Ann Arbor. He received his Ph.D. in computer science at Tulane University, New Orleans, US. His area of research entails Computational Geometry, Geospatial Algorithms, Data analytics, and Geographic Information Systems (GIS). His overarching goal is to develop efficient and equitable methods to provide feasible insights for policy enforcement to improve the infrastructure of transportation systems for all groups of people. Dr. Mirzanezhad received the best research paper award at the International Workshop on Interactive and Spatial Computing (IWISC). He has conducted research projects primarily supported by Ford Motors Company, RAND Corporation, and the National Science Foundation.

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