Sherri Harms

Professor, Cyber Systems

Office: DSCH 351   |    Phone: (308) 865-8123   |    Email:

Sherri Harms



  • Ph.D., Computer Engineering and Computer Science , U. Missouri
  • M.S., Computer Science, Iowa State U.
  • B.S., Computer Science, Mathematics, 7-12 Math Education (Certified in Iowa 1987-1997),  Buena Vista U.

Research Interests 

  • Computer Science Education
  • Student Innovation
  • Data mining
  • Spatio-temporal data mining
  • Predictive modeling for climatic and agricultural decision support systems
  • Intelligent web applications

Current Courses 

  • CYBR 150 Object-Oriented Programming
  • CYBR 223 IT Infrastructure
  • CYBR 388 GS Capstone: Social Networking
  • CYBR 425/825 Database Systems 
  • CYBR 434/834P IT Teaching Methods 
  • CYBR 441 Artificial Intelligence 

Dr. Harms has several years of industry experience in software development and database management. She has numerous peer reviewed journal and conference publications, with research funded by NSF, USDA, Google, and state agencies.  Over her career, Dr. Harms has been involved with over $2,000,000 in research grants and contracts.  Dr. Harms has been instrumental in starting undergraduate research in the Cyber Systems Department at UNK, and she has mentored hundreds of student publications and presentations. She is actively committed to service learning, with student projects that have aided hundreds of local, regional, and national organizations. She is passionate about research and service in computer science education, recruiting and retaining women in CS, and in database and data mining. Her two recent grants are a Google CS4HS grant and a Nebraska Rural Futures Institute (RFI) grant for implementing social media plans for small businesses & non-profits through service learning. She is a Math/CS councilor for the Council on Undergraduate Research (CUR); and the CUR Math/CS Division received the 2019 Division of the Year award. Dr. Harms received the 1997 Governor’s Award for Teaching Excellence for Lincoln University in Missouri.

Professional Memberships

  • Editorial and Review Board, Intern. J. of Data Mining, Modeling and Management, 2008 - present
  • Math/CS Councilor, Council on Undergraduate Research, 2017 - present
  • UNK Representative, NCWIT Academic Alliance, 2015 – present
  • Reviewer, ACM Symposium on Applied Computing Committee, 2003 - present
  • Member, Association for Computing Machinery, 1999-2007; 2017 – present
  • Member, Epsilon Pi Tau, 2019-present
  • Member, National Education Association, UNK Chapter, 2003 - present
  • Member, Women in Cyber Security, 2019- present

Selected Publications

  • Harms, S. K. (2017). Tackling CS education in K-12: Implementing a Google CS4HS Grant Program. La Crosse, WI: 2017 Midwest Instructional Computing Symposium Proceedings.
  • Harms, S. K., Hastings, J. D., A Cross-Curricular Approach to Fostering Innovation such as Virtual Reality Development through Student-Led Projects. IEEE/ASEE Frontiers in Education, 9.
  • Harms, S. K.  Enabling Student Innovation through Virtual Reality Development (pp. 10). Cedar Rapids, IA: 2016 Midwest Instructional Computing Symposium Proceedings.
  • Harms, S. Tadesse,T.,  Wardlow, B. (2014) Improving Drought Risk Modelling: Using Multiple Periods of Satellite Data with Ensembles of Data Mining Algorithms, International Journal of Society Systems Science (IJSSS), Vol. 6, No. 2.(June 2014). (refereed, international)
  • Harms, S., Hastings, J., Jussel, M. (2013).  Assessing Writing in Computer Science  Bachelor of Science Degree Programs, Midwest Instructional Computing Symposium, April 20, 2013 (refereed, regional). 
  • Harms, S. Database Systems Course: Service Learning Project, Midwest Instructional Computing Symposium, April 14, 2012.
  • Harms, S. Tadesse,T.,  Wardlow, B. (2009). Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool, J. Gama et al. (Eds.): Discovery Science 2009, Lecture Notes in Artificial Intelligence 5808, pp. 107–120. (27% acceptance rate, international).
  • Harms, S., Temporal Event Sequence Rule Mining, Data Warehousing and Mining Encyclopedia, 2nd Edition. J. Wang, ed., Idea Group Inc., August 2008, pp. 1098-1102. (35% acceptance rate, international)  
  • Harms, S., Temporal Association Rule Mining in Event Sequences, Data Warehousing and Mining Encyclopedia, J. Wang, ed., Idea Group Inc., 2006, pp. 1098-1102. (60% acceptance rate, international)
  • Tadesse, T., Wilhite, D. A., Hayes, M. J., Harms, S. K., Goddard, S. Discovering Associations between Climatic and Oceanic Parameters to Monitor Drought in Nebraska Using Data-Mining Techniques, Journal of Climate 18 (10), May 2005, pp. 1541-1550. (refereed, international)
  • Tadesse, T., Wilhite, D., Harms, S., Hayes, M., Goddard, S., Drought monitoring using data mining techniquesJournal of Natural Hazards, 33(1), September 2004, pp. 137-159. (refereed, international)
  • Harms, S. K., Deogun, J.,  Sequential Association Rule Mining with Time LagsJournal of Intelligent Information Systems (JIIS).  22 (1), January 2004, pp. 7-22. (4% acceptance rate, international)
  • Li, D., Deogun, J., Harms, S.K., Interpolation Techniques for Geo-spatial Association Rule Mining,  Lecture Notes in Artificial Intelligence 2639, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, proceedings of the 9th. International Conference, RSFSDGrC 2003, G. Wang, Q. Liu, Y. Ya, A. Skowron eds., Springer Verlag, October 2003, pp. 573-580. (50% acceptance rate, international)
  • Harms, S. K., Deogun, J., Goddard, S., Building Knowledge Discovery into a Geo-spatial Decision Support System, Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, FL, March 2003, pp. 445-449. (26% acceptance rate, international)
  • Goddard, S., Harms, S., Reichenbach, S., Tadesse, T., Waltman, W.,  Geospatial Decision Support for Drought Risk Management, Communications of the ACM. 46 (1), January 2003, pp. 35-38. (invited, international)
  • Harms, S. K., Deogun, J., Tadesse, T.,  Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences,  Lecture Notes in Artificial Intelligence 2366: Foundations of Intelligent Systems, Proceedings of the 13th  International Symposium on Methodologies for Intelligent Systems, Lyon, France, June 27-29 2002, pp.432-441. (39% acceptance rate, international)
  • Harms, S. K., Saquer, J., Deogun, J., Tadesse, T., Discovering Representative Episodal Association Rules from Event Sequences Using Frequent Closed Episode Sets and Event ConstraintsProceedings of the ICDM '01: The 2001 IEEE International Conference on Data Mining, Silicon Valley, CA, November 29 - December 2, 2001, pp. 603-606. (31% acceptance rate, international)

Selected Funded Grants

  • Google CS4HS Grant, Computer Science Principles on the Prairie, $31,892.34. 2016-2017
  • Nebraska Rural Futures Institute Grant, Facilitating the Implementation of Social Media Plans for Small Businesses & Local Non-Profits through Service Learning," $20,000.00. 2016-2018
  • 2011 University of Nebraska Online Worldwide Program Planning Grant: Information Technology BS Online degree completion program, UNK CSIS and UNO School of Interdisciplinary Informatics, S. Harms, lead PI, May 2011.
  • U. of Nebraska Kelly Fund Grant: Technology Transparency in Computer Science (CS), Computer Information Systems(CIS) and Visual Communication and Design (VCD) Curricula, S. Harms and M. Hartman (PIs), Spring 2010.
  • USDA Risk Management Association (RMA) internal grant RMA: Developing Drought Monitoring and Prediction Tools for the Continental U.S. using Data Mining Techniques,  to UNL, subcontract to UNK, Don Wilhite, lead UNL PI; S. Harms, lead UNK PI, $6.1 million, 3 years, awarded Fall 2005. 
  • UNK Collaborative Grant: Information Technology Survey Of Nebraska Rural Communities, A.R. Taylor, lead PI; Spring 2006.
  • National Science Foundation (NSF) Research Experiences for Undergraduates (REU): Knowledge Discovery Based on Geographical Regions, Supplement to the Digital Government grant listed below.  Summer 2004, S. Goddard and Sherri Harms, PIs.
  • National Science Foundation (NSF) Research Experiences for Undergraduates (REU): Knowledge Discovery: From Prototype to Decision Support, Supplement to the Digital Government grant listed below. Summer 2003, S. Goddard and S. Harms, PIs. 
  • USDA Risk Management Association (RMA) internal grant RMA: Risk Assessment and Exposure Analysis on the Agricultural Landscape,   funded Spring 2003 to researchers from multiple agencies (UNL, UNK, UNO, USGS EROS Data Center, USDA NRCS), Steve Goddard, lead PI; $1,000,000, 2 years, Fall 2002. 
  • National Science Foundation (NSF) Digital Government Initiative (DGI): A Geospatial Decision Support System for Drought Risk Management$1,000,000 3 year grant awarded Spring 2001 to University of Nebraska-Lincoln, S. E. Reichenbach, lead PI.