Raju Vatsavai
Associate Professor
Center for Geospatial Analytics
Engineering Building II (EB2) 2254
Raju is a Chancellor’s Faculty Excellence Program Cluster Associate Professor in Geospatial Analytics in the Department of Computer Science. As the Center for Geospatial Analytic’s Associate Director of Spatial Computing & Technology, Raju plays a leadership role in our strategic vision for spatial computing research. He works at the intersection of big data management, data analytics, and high performance computing with applications in national security, geospatial intelligence, natural resources, climate change, location-based services, and human terrain mapping. Raju was previously the lead data scientist for the computational sciences and engineering division of the Oak Ridge National Laboratory (ORNL). He holds MS and PhD degrees in computer science from the University of Minnesota and is coming to NC State with more than 20 years of research and development experience in large-scale spatiotemporal data management and geographic knowledge discovery. A leader in the field, Raju is passionate about understanding the world through (high-) resolution, dimensional, and temporal pixels by developing innovative and computationally efficient algorithms.
Publications
- Anomalous cluster detection in spatiotemporal meteorological fields (2019)
- Deformable Part Models for Complex Object Detection in Remote Sensing Imagery (2018)
- FUTURES-DPE: Towards Dynamic Provisioning and Execution of Geosimulations in HPC environments (2018)
- Machine Learning Approaches for Slum Detection Using Very High Resolution Satellite Images (2018)
- Real-Time Energy Audit of Built Environments: Simultaneous Localization and Thermal Mapping (2018)
- Hierarchical change detection framework for biomass monitoring (2017)
- High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data (2017)
- Parallel processing over spatial-temporal datasets from geo, bio, climate and social science communities: A research roadmap (2017)
- Semantics-enabled framework for spatial image information mining of linked earth observation data (2017)
- Semi-supervised deep generative models for change detection in very high resolution imagery (2017)