Talk on Machine Learning for Smart Cities: Research and Teaching Methods by Mahdi Hashemi
Event Start Date:
Monday, March 28, 2022
- 10:30 am
Event End Date:
Monday, March 28, 2022 - 11:30 am
Location: Hornbake 2119 and Virtual
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Monday, March 28, 2022 10:30 am
Monday, March 28, 2022 11:30 am
America/New York
Talk on Machine Learning for Smart Cities: Research and Teaching Methods by Mahdi Hashemi
As part of our spring talk series, please join us on Monday, March 28, for a talk from Mahdi Hashemi. The talk will be held in Hornbake 2119 and on Zoom.
Abstract:
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 a challenge and the focus of this talk. In addition to my research and teaching efforts in machine learning and its applications in different areas, I will cover my teaching method, vision, and philosophy in this talk.
Bio:
Mahdi Hashemi received the Ph.D. degree in Computing and Information from the University of Pittsburgh. He is currently an Assistant Professor with the Department of Information Sciences and Technology, George Mason University. He is the founding director of the Machine Learning and Urban Computing Group, where he also specializes in intelligent transportation, spatial-temporal data mining and prediction, and online misinformation mining and detection. He has published 30 journal articles and 12 conference papers. He served on the program committee of SEKE international conference and is a Reviewer Board Member of Information and a Topics Board Member of Remote Sensing, since 2020. He is an active reviewer of IEEE Transactions on Intelligent Transportation Systems and Transactions in GIS, among others. He has received multiple awards from Mason to mentor multidisciplinary teams of undergraduate and graduate students since 2020. He was nominated for the 2021 and 2022 George Mason University Teaching Excellence Award. He has developed and teaches machine learning and deep learning related courses, at both undergraduate and graduate levels.
Hornbake 2119 and Virtual
As part of our spring talk series, please join us on Monday, March 28, for a talk from Mahdi Hashemi. The talk will be held in Hornbake 2119 and on Zoom.
Abstract:
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 a challenge and the focus of this talk. In addition to my research and teaching efforts in machine learning and its applications in different areas, I will cover my teaching method, vision, and philosophy in this talk.
Bio:
Mahdi Hashemi received the Ph.D. degree in Computing and Information from the University of Pittsburgh. He is currently an Assistant Professor with the Department of Information Sciences and Technology, George Mason University. He is the founding director of the Machine Learning and Urban Computing Group, where he also specializes in intelligent transportation, spatial-temporal data mining and prediction, and online misinformation mining and detection. He has published 30 journal articles and 12 conference papers. He served on the program committee of SEKE international conference and is a Reviewer Board Member of Information and a Topics Board Member of Remote Sensing, since 2020. He is an active reviewer of IEEE Transactions on Intelligent Transportation Systems and Transactions in GIS, among others. He has received multiple awards from Mason to mentor multidisciplinary teams of undergraduate and graduate students since 2020. He was nominated for the 2021 and 2022 George Mason University Teaching Excellence Award. He has developed and teaches machine learning and deep learning related courses, at both undergraduate and graduate levels.
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