Â鶹ÊÓƵ

General Information

• Position: Associate Professor
• E-mail: sujing.wang@lamar.edu
• Phone: (409) 880-7798
• Room: Maes 67

Education

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

Research Interests

• Big Data
• Data Science
• Data Mining and Knowledge Discovery
• Geographic Information Systems (GIS)
• Computer Modeling and Simulation

Selected Publications

A. R. Pokhrel, S. Wang*, “Design of Fast and Scalable Clustering Algorithm on Spark”, in Proceedings of ACM 2020 the 4^th International Conference on Cloud and Big Data Computing (ICCBDC 2020), August 26-28, 2020, Liverpool, United Kingdom.

S. Wang*, S. Andrei, O. Urbina, and D. Sisk, “Introducing STEM to 7^th Grade Girls using SeaPerch and Scratch”, in Proceedings of 2020 the 50^th IEEE Frontiers in Education (FIE) International Conference, Oct. 21-24, 2020, Uppsala, Sweden.

R. Banerjee, K. Elgarroussi, S. Wang, A. Talari, Y. Zhang, and C. F. Eick*, “K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time”, International Journal of Semantic Computing, 13(01):111-133, 2019.

S. Wang*, S. Andrei, O. Urbina, and D. Sisk, “A Programming Academy for 6^th Grade Females to Increase Knowledge and Interests in Computer Science”, in Proceedings of 2019 the 49^th IEEE Frontiers in Education (FIE) International Conference, October 16-19, 2019, Cincinnati, OH, USA.

S. Wang* and C. F. Eick, “A Data Mining Framework for Environmental and Geo-Spatial Data Analysis”, International Journal of Data Science and Analytics (2018) 5:83.

 K. Elgarroussi, S. Wang*, R. Banerjee, and C. F. Eick*, “Aconcagua: A Novel Spatio-temporal Emotion Change Analysis Framework”, in Proceedings of 2018 the 26^th ACM International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018) Workshop on AI for Geographic Knowledge Discovery , Seattle, Washington, USA, November 6-9, 2018.

A. M. Aryal, S. Wang*, “Spark-SNN: Density-based Clustering on Spark”, in Proceedings of 2018 IEEE 3^rd International Conference on Big Data Analysis (ICBDA 2018), Mar. 9-12, 2018.

R. Banerjee, K. Elgarroussi, S. Wang*, Y. Zhang and C. F. Eick*, “Tweet Emotion Mapping: Understanding US Emotions in Time and Space”, in Proceedings of 2018 IEEE Frist International Conference on Artificial Intelligence and Knowledge Engineering (AIKE 2018), Laguna Hills, CA, USA, Sept. 26-28, 2018, pp. 93-100, doi: 10.1109/AIKE.2018.00021.

K. Kang, S. Wang*, “Analyze and Predicate for College Student Online Program Dropout”, in Proceedings of ACM 2^nd International Conference of Compute and Data Analysis (ICCDA 2018), Dekalb, IL, USA, Mar. 23-25, 2018.

A. M. Aryal and S. Wang*, “Discovery of Patterns in Spatial-temporal Data Using Clustering Techniques”, in Proceedings of 2017 IEEE 2^nd International Conference on Image, Vision and Computing Workshops on Database and Data Mining (ICDDM 2017), June 2-4, 2017.

Y. Zhang, S. Wang*, A. M. Aryal, and C. F. Eick*, “Serial versus Parallel: a Comparison of Spatio-temporal Clustering Approaches”, in Proceedings of 2017 International Symposium on Methodologies for Intelligent Systems (Foundations of Intelligent Systems), June 26-29, 2017.

S. Wang* and C. F. Eick, “MR-SNN: Design of Parallel Shared Nearest Neighbor Clustering on Hadoop”, in Proceedings of 2017 IEEE 2^nd International Conference on Big Data Analysis (ICBDA 2017), March 10-12, 2017.

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*, 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 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.