Dr. Sujing Wang


General Information

  • Position: Assistant Professor
  • E-mail: sujing.wang@lamar.edu
  • Phone: 409-880-7798
  • Room: 67

Education

  • Ph.D. Computer Science, University of Houston, Houston, TX
  • M.S. Computer Science, Universtiy of Houston, Houston, TX

Research Interests

  • Data Mining and Knowledge Discovery
  • Spatial Temporal Clustering
  • Geographic Information Systems (GIS)
  • Geographic Computation
  • Big Data
  • Computer Modeling and Simulation

Selected Publications

S. Wang*, C. F. Eick, “A Geospatial Clustering and Analysis Framework for Mining Ozone Pollution Data ”, in Proceedings of Geocomputing 2015, Dallas, TX, USA, May 20-23, 2015.

S. Wang*, T. Cai, C. F. Eick, “New Clustering and Analyzing Technique for Mining Multi-source Enriched Geo-spatial Data”, in Proceedings of the ACM SIGMOD/PODS Workshop on Managing and Mining Enriched Geo-Spatial Data, Snowbird, Utah, USA,  June 22-27, 2014.

S. Wang*, C. F. Eick, “A Polygon-based Clustering and Analysis Framework for Mining Spatial Datasets,” GeoInformatica, 18(3), pp 569-594, 2014, DOI: 10.1007/s10707-013-0190-2.

S. Wang*, T. Cai, C. F. Eick, “New Spatiotemporal Clustering Algorithms and their Applications to Ozone Pollution,” in Proceedings of the 13th IEEE International Conference on Data Mining Workshop (ICDMW) on Spatial  and Spatiotemporal Data Mining (SSTDM-13), Dallas, TX, December 7-11, 2013.

Z. Cao, S. Wang*, C. F. Eick, “Analyzing the Composition of Cities Using Spatial Clustering,” in Proceedings of the 19th ACM SIGKDD conference on Knowledge Discovery and Data Mining Workshop (KDD) on Urban Computing (UrbComp 2013), Chicago, IL, August 11-14, 2013.

S. Wang*, C. F. Eick, “A Spatial Temporal Analysis Framework for Mining Geospatial Datasets,” presented at the First International Conference on Space, Time, and CyberGIS (CyberGIS’12), University of Illinois at Urbana-Champaign, Champaign, IL, August 6-9, 2012.

S. Wang*, C. Chen, V. Rinsurongkawong, F. Akdag, C. F. Eick, “Polygon-based Methodology for Mining Related Spatial Datasets,” in Proceedings of the 18th ACM SIGSPATIAL Conference on Advances in Geographic Information Systems Workshop (ACM GIS) on Data Mining for Geoinformatics (DMGI), San Jose, CA, November 6-9, 2010.