Raju is a Chancellor’s Faculty Excellence Program Cluster Associate Professor in Geospatial Analytics in the Department of Computer Science, North Carolina State University (NCSU). He works at the intersection of spatial and temporal big data management, analytics, and high performance computing with applications in the national security, geospatial intelligence, natural resources, climate change, location-based services, and human terrain mapping. As the Associate Director of the Center for Geospatial Analytics (CGA), Raju plays a leadership role in the center’s strategic vision for spatial computing research.

Before joining NCSU, Raju was the Lead Data Scientist for the Computational Sciences and Engineering Division (CSED) at the Oak Ridge National Laboratory (ORNL). Raju has more than 25 years of research and development experience in large-scale spatiotemporal data management and geographic knowledge discovery. He worked at many leading research laboratories: ORNL (2006-2014), IBM-Research (IRL, IIT-Delhi Campus, 2004-2006), University of Minnesota (Remote Sensing Laboratory, St. Paul, USA. 1999-2004), AT&T Labs (R&D HQ, Middletown, NJ. USA. 2008), and the Center for Development of Advanced Computing (CDAC, Pune University Campus, India. 2005-2008). He has published more than 80 peer-reviewed articles in conferences and journals, and edited two books on “Knowledge Discovery from Sensor Data.” He served on program committees of leading international conference including ACM KDD, ACM GIS, ECML/PKDD, SDM, CIKM, IEEE BigData, and co-chaired several workshops including ICDM/SSTDM, ICDM/KDCloud, ACM SIGSPATIAL BigSpatial, Supercomputing/BDAC, KDD/LDMTA, KDD/Sensor-KDD, and SDM/ACS. He holds MS and PhD degrees in computer science from the University of Minnesota.

Research Areas

  • Artificial Intelligence and Intelligent Agents
  • Cloud Computing
  • Data Sciences and Analytics
  • Information and Knowledge Management
  • Parallel and Distributed Systems
  • Scientific and High Performance Computing


Ph.D., 2008, University of Minnesota, Computer Science.

M.S., 2003, University of Minnesota, Computer Science.


Ranga Raju Vatsavai, “Gaussian Multiple Instance Learning Algorithm for Mapping the Slums of the World,” ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013. 

Christopher T. Symons, Ranga Raju Vatsavai, Goo Jun, Itamar Arel: Bias Selection Using Task-Targeted Random Subspaces for Robust Application of Graph-Based Semi-supervised Learning. ICMLA (1) 2012: 415-420.

Ranga Raju Vatsavai, “Rapid Damage eXplorer (RDX): A probabilistic framework for learning changes from bitemporal images,” IEEE International Conference on Data Mining (ICDM Demo Paper), Belgium, December 2012.

Ranga Raju Vatsavai, Jordan Graesser: Probabilistic Change Detection Framework for Analyzing Settlement Dynamics Using Very High-resolution Satellite Imagery. ICCS 2012: Procedia Computer Science, Vol. 9: 907-916

Graesser, J.; Cheriyadat, A.; Vatsavai, R.R.; Chandola, V.; Long, J.; Bright, E., "Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol.5, no.4, pp.1164-1176, Aug. 2012.

Varun Chandola, Ranga Raju Vatsavai: A scalable gaussian process analysis algorithm for biomass monitoring. Statistical Analysis and Data Mining 4(4): 430-445 (2011).

Ranga Raju Vatsavai, Shashi Shekhar, Budhendra L. Bhaduri: A Learning Scheme for Recognizing Sub-classes from Model Trained on Aggregate Classes. SSPR/SPR 2008: 967-976. 

Ranga Raju Vatsavai, Shashi Shekhar, Thomas E. Burk. “An efficient spatial semi-supervised learning algorithm.” International Journal of Parallel, Emergent and Distributed Systems, 22(6): 427-437 (2007). 

Ranga Raju Vatsavai, Shashi Shekhar, Thomas E. Burk, Stephen Lime: UMN-MapServer: A High-Performance, Interoperable, and Open Source Web Mapping and Geo-spatial Analysis System. GIScience 2006: 400-417.

Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsavai, Weili Wu, Sanjay Chawla: Spatial contextual classification and prediction models for mining geospatial data. IEEE Transactions on Multimedia 4(2): 174-188 (2002)