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Hye-Chung Kum, PhD

Hye-Chung Kum
Hye-Chung Kum, PhD

Professor

Curriculum Vitae

CV (PDF)

Contact

Health Policy & Management
212 Adriance Lab Rd.
1266 TAMU
College Station, TX 77843-1266
kum@tamu.edu
Phone: 979.436.9439
Google Scholar Profile
Scholars@TAMU Profile
Population Informatics Lab

Education and Training

  • University of North Carolina at Chapel Hill (UNC-CH), PhD, 2004
  • UNC-CH, School of Social Work, MSW (Master of Social Work), 1998
  • UNC-CH, Department of Computer Science, MS, 1997
  • Yonsei University, Seoul, Korea, Department of Computer Science, BS, 1995

Research Interests

  • Using Abundance of Existing Digital Data (e.g. government administrative data, electronic health records) to Support Accurate Evidence Based Decisions for Policy, Management, Legislation, Evaluation, and Research
  • Data Science of Using Massive Secondary Datasets
  • Building Efficient and Effective Human Computer Hybrid Systems to Clean, Integrate, and Extract Valuable Information from Raw Chaotic Data

Teaching Interests

  • Data Science for Health Services Researchers
  • Health Information Technology
  • Retrospective Database Analysis for Health Policy and Management

Representative Publications

Stewart, C.J., Kum, H.-C., Barth, R.P., Duncan, D.F. Former foster youth: Employment outcomes up to age 30. Children and Youth Services Review, 2014. 36(0): p. 220-229.

Kum, H.-C., Krishnamurthy, A., Machanavajjhala, A., and Ahalt, S. Population Informatics: Tapping the Social Genome to Advance Society: A Vision for Putting Big Data to Work for Population Informatics, Editors. 2013. p. 56-63.

Kum, H.-C., Krishnamurthy, A., Machanavajjhala, A., Reiter, M., and Ahalt, S. Privacy preserving interactive record linkage (PPIRL). J Am Med Inform Assoc, 2013.

Kum, H.-C., and Ahalt, S. Privacy-by-Design: Understanding Data Access Models for Secondary Data. AMIA Summits Transl Sci Proc, 2013. 2013: p. 126-30.

Kum, H.-C., et al. Secure Decoupled Linkage (SDLink) system for building a social genome. in Big Data, 2013 IEEE International Conference on. 2013.

Rajasekar A., Kum, H.-C., Crosas M., et al. The DataBridge, Science Journal. ASE. 2(1) 2013. Nominated for best paper award at ASE/IEEE International conf. on Big Data 2013

Kum, H.-C., Ahalt, S., and Pathak, D. Privacy-Preserving Data Integration Using Decoupled Data, in Security and Privacy in Social Networks, Y. Altshuler, et al., Editors. 2013, Springer New York. p. 225-253.

Kum, H.-C., Ahalt, S., and Carsey, T.M. Dealing with Data: Governments Records. Science, 2011. 332(6035): p. 1263-1263.

Kum, H.-C., and Ahalt, S. Decoupled Data for Privacy Preserving Record Linkage with Error Management. in Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). 2011.

Barth, R.P., Duncan, D.F., Hodorowicz, M., Kum, H.-C. Felonious arrests of former foster care and TANF-involved youth. Journal of the Society for Social Work and Research, 2010. 1(2).

Kum, H.-C., D.F. Duncan, and C.J. Stewart, Supporting self-evaluation in local government via Knowledge Discovery and Data mining. Government Information Quarterly, 2009. 26(2): p. 295-304.

Chang, J.H. and Kum, H.-C. Frequency-based load shedding over a data stream of tuples. Information Sciences, 2009. 179(21): p. 3733-3744.

Kum, H.-C., D.F. Duncan, and K.A. Flair, A dynamic website for county level child welfare outcome measures, in Proceedings of the 2008 international conference on Digital government research. 2008, Digital Government Society of North America: Montreal, Canada. p. 383-384.

Duncan, D.F., Kum, H.-C., et al., Informing Child Welfare Policy and Practice Using Knowledge Discovery and Data Mining Technology via a Dynamic Web Site. Child Maltreatment, 2008. 13(4): p. 383-391.

Kum, H.-C., Chang, J.H., and Wang, W. Benchmarking the effectiveness of sequential pattern mining methods. Data & Knowledge Engineering, 2007. 60(1): p. 30-50.

Kum, H.-C., Chang, J.H., and Wang, W. Intelligent sequential mining via alignment: Optimization techniques for very large DB, in Advances in Knowledge Discovery and Data Mining, Proceedings, Z.H. Zhou, H. Li, and Q. Yang, Editors. 2007. p. 587-597.

Kum, H.-C., Chang, J.H., and Wang, W. Sequential pattern mining in multi-databases via multiple alignment. Data Mining and Knowledge Discovery, 2006. 12(2-3): p. 151-180.

Kum, H.-C., Paulsen, S., and Wang, W. Comparative study of sequential pattern mining models, in Foundations of Data Mining & Knowledge Discovery, T.Y. Lin, et al., 2005. pp43-70.

Kum, H.-C., Duncan, D., and Wang, W. Understanding social welfare service patterns using sequential analysis, in Proceedings of the 2004 annual national conference on Digital government research. 2004, Digital Government Society of North America: Seattle, WA. p. 1-2.

Duncan, D., Kum, H.-C., et al., Successfully adopting IT for social welfare program management, in Proceedings of the 2004 annual national conference on Digital government research. 2004, Digital Government Society of North America: Seattle, WA. p. 1-9.

Kum, H.-C., et al., Social welfare program administration and evaluation and policy analysis using knowledge discovery and data mining (KDD) on administrative data, in Proceedings of the 2003 annual national conference on Digital government research. 2003, Digital Government Society of North America: Boston, MA. p. 1-6.

Kum, H.-C., et al., ApproxMAP: Approximate mining of consensus sequential patterns. Proceedings of the Third Siam International Conference on Data Mining, ed. D. Barbara and C. Kamath. 2003. 311-315.