Applied and Computational Math Seminar: Machine learning for smart cities

Speaker: Mahdi Hashemi, George Mason University
Title: Machine learning for smart cities

Abstract: Cities are growing physically and digitally, faster than ever. The ever-growing population of cities, along with their intrinsic inaccessibility and inequity has created difficulties with traffic, mobility, safety, health, pollution, and misinformation among many others. The physical and digital growth of cities outpaces the effort to address the aforementioned issues.

The growing popularity of online social networks (OSN) and World Wide Web (WWW) has remarkably expedited the information dissemination among individuals and groups. Digital data is the lifeblood of modern cities. Today, it's being captured in large quantities at unprecedented rates via ubiquitous devices and sensors. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms that benefit from the availability of such data. That has turned the discussion from how the massive amounts of data are collected to how knowledge can be extracted from them.

Smart cities become smart not only because they automate routine functions serving the citizens, buildings, and traffic systems but also because they enable monitoring, understanding, analyzing and planning the city to improve the efficiency, equity, and quality of life for its citizens in real time. With physical and digital problems on one hand and big data on the other, smart cities strive to juxtapose them to find inexpensive solutions. How the digital data should be processed to help solve problems in cities remains one of the major areas of research and development in recent years and the focus of this talk.

Time: Friday, January 31, 2020, 1:30-2:30pm

Place: Exploratory Hall, Room 4106