VIRTUAL Distinguished Lecture with Mohamed Mokbel: Machine Learning for Big Spatial Data and Applications

Date/Time
Date(s) - 11 Oct 2021
1:10 PM - 3:00 PM

Location

Headshot photo of Mohamed Mokbel standing outside with trees and flowers in the backgroundSpeaker: Mohamed Mokbel, Professor with the Department of Computer Science & Engineering at the University of Minnesota

Title: Machine Learning for Big Spatial Data and Applications

Abstract: Machine learning techniques have been widely used to support a wide variety of applications, where big spatial data is a major component. Example of such applications include agriculture, meteorology, remote sensing, transportation, and location-based services. Unfortunately, most machine learning techniques were designed in a generic way to support all types of data, without special support for spatial data. Such lack of native spatial support results in sub par performance. This talk will focus on our recent efforts in adopting machine learning techniques for big spatial data and applications. This includes going for two orthogonal, but related, directions. First, injecting the spatial awareness inside machine learning techniques and applications, which will result in a higher accuracy for such applications. Second, taking advantage of the recent advances in machine learning techniques to boost the deployment, scalability, and accuracy of long lasting spatial and spatio-temporal data analysis techniques.

Bio: Mohamed Mokbel (PhD, Purdue University, MS, BS, Alexandria University) is a Professor at the University of Minnesota. Prior roles while on leave/sabbatical from UMN include Chief Scientist of Qatar Computing Research Institute, Founding Technical Director of GIS Technology Innovation Center in Saudi Arabia, and multiple times Visiting Researcher at Microsoft Research, USA. His research interests include database systems, spatial data, and GIS. His research work has been recognized by the NSF CAREER Award, VLDB 10-years Best Paper Award, and four conference Best Paper Awards. Mohamed is the past elected Chair of ACM SIGSPATIAL, current Editor-in-Chief for Springer Distributed and Parallel Databases (DAPD) Journal, and on the editorial board of ACM Books, ACM TODS, VLDB Journal, ACM TSAS, and GeoInformatica journals. He has served as PC Co-Chair for ACM SIGMOD, ACM SIGSPATIAL, and IEEE MDM. Mohamed is an IEEE Fellow and ACM Distinguished Scientist. For more information, please visit: www.cs.umn.edu/~mokbel

Seminar Host: Goce Trajcevski

Webinar Link: https://iastate.zoom.us/j/98548165885?pwd=ZUNPdDZGNHZoT1k5OTNzMHZjSWVCZz09

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Contact Information

Interested in giving a seminar? Contact Professor Jiming Song.

Announcements and publicity by Kristin Clague, and scheduling by the department secretary.