Kyle Luoma

Research Scientist

Electrical Engineering & Computer Science

Army Cyber Institute

kyle.luoma [at] westpoint.edu

LTC Kyle Luoma is an Operations Research and Systems Analyst (ORSA) officer and servers as a researcher in the Army Cyber Institute. Kyle commissioned out of the Cal State University - Fullerton Army ROTC program as an Aviation officer in 2005 when he graduated from Biola University with B.S. in Business Management. He received an M.S. degree in Manpower Systems Analysis from the Naval Postgraduate School in 2017, a second B.S. in Computer Science in 2018, and is near completion of a Ph.D. In Computer Science at the U.C. San Diego Jacobs School of Computer Science and Engineering.

LTC Luoma has served in three different branches or functional areas, beginning with Aviation where he served in various positions and units as a UH-60-qualified aviator. In 2013 he transferred to the Force Management (FA50) functional area where he helped advance the Army's aviation restructuring initiative, coordinate aviation munitions resourcing, define equipping and organizational requirements in support of the DoD's arctic strategy, and helped develop the initial structure of Army Future's Command. Beginning in 2018, Kyle worked as an ORSA (FA49) in the Army G-1, assisting with requirements development and data integration challenges associated with the IPPS-A program. LTC Luoma has been working at the Army Cyber Institute since 2024, where he serves as a computer science researcher in the Data and Decision Sciences team. His research focus is on applications of machine learning and AI in database interaction and data engineering. 

Ph.d. Candidate (ABD) in Computer Science - U.C. San Diego

 

M.S. in Manpower Systems Analysis - Naval Postgraduate School

 

B.S. in Computer Science - Cal State University - Monterey Bay

 

B.S. in Business Management - Biola University

Research Interests

  • Research interests: Database systems, natural language interfaces with data systems, data engineering, applications of language models in data integration, data systems for machine learning.

 

  • Publication: Kyle Luoma and Arun Kumar. 2025. SNAILS: Schema Naming Assessments for Improved LLM-Based SQL Inference. Proc. ACM Manag. Data 3, 1, Article 77 (February 2025), 26 pages.