Machine Learning Meets Big Spatial Data: The Landscape
In collaboration with the UMD Center for Machine Learning
Date and Time of the talk: March 30 2021, 9:30 AM EDT
Information of the Speaker
Ibrahim Sabek, MIT
Dr. Sabek is a Postdoctoral Associate at MIT. Before that, he got his PhD and M.Sc. in computer science from the University of Minnesota, Twin Cities. His research interests broadly include machine learning for systems, scalable data processing, and querying, probabilistic databases, scalable knowledge base construction, and big spatial data management and analysis. Dr. Sabek has been named an NSF Computing Innovation Fellow (CIFellow) in 2020, and awarded the University of Minnesota Doctoral Dissertation Fellowship in 2019 for his dissertation focused on scalable machine learning for big spatial data and applications. His research work has won the first place in the ACM SIGSPATIAL Student Research Competition (SRC) 2019, and has been nominated for the Best Paper Award of ACM SIGSPATIAL 2018.
For more information, please visit: http://people.csail.mit.edu/ibrahimsabek/.
The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to existing machine learning solutions or building new solutions from scratch. In this talk, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in this intersection via a thorough landscape. We also discuss the existing end-to-end systems, and highlight their main properties and limitations.