Institution: Qatar Computing Research Institute
Email address: mmokbel@hbku.edu.qa
Title: Machine Learning for Big Spatial Data and Applications
Abstract
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 usability, deployment, scalability, and accuracy of long lastingspatial and spatio-temporal data analysis techniques. For the first direction, we will present the Sya system as a full-fledged spatial machine leaning-based probabilistic knowledge base construction system. For the second direction, we will present machine-learning-based techniques for spatial autologistic regression, shortest path queries, and map making.
Biography
Mohamed Mokbel is Chief Scientist at Qatar Computing Research Institute and a Professor at University of Minnesota. His current research interests focus on systems and machine learning techniques for big spatial data and applications. His research work has been recognized by the VLDB 10-years Best Paper Award, four conference Best Paper Awards, and the NSF CAREER Award. Mohamed is the past elected Chair of ACM SIGPATIAL, current Editor-in-Chief for Distributed and Parallel Databases Journal, and on the editorial board of ACM Books, ACM TODS, VLDB Journal, ACM TSAS, and GoeInformatica journals. He has also served as PC Vice Chair of ACM SIGMOD and PC Co-Chair for ACM SIGSPATIAL and IEEE MDM.
