Machine Learning Researcher

Education

• PhD Student Information Science University of Washington, Current
• MSc Operations Research with Data Science (Distinction), University of Edinburgh ‘18
• BS Systems Engineering with Cyber Track (Honors), United States Military Academy ‘17

Projects

• Multi-Agent Systems for Frame Detection (Under Review)
• LLM Chain Ensembles for Scalable and Accurate Data Annotation (Accepted to IEEE International Conference on Big Data, prepub available at https://arxiv.org/abs/2410.13006)
• RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Linguistic Classifiers (Accepted to International Conference on COmputational Linguistics, prepub available at https://arxiv.org/abs/2408.08217)
• LLM Confidence Evaluation Measures in Zero-Shot CSS Classification (Under review, prepub -> https://arxiv.org/abs/2410.13047)

Talks and Lectures

• Calling BS: Data Reasoning In A Digital World - Guest Lecturer at University of Washington, Fall 2024
• Military Operations Research Symposium - Data Platform Smackdown, Spring 2023

Publications

1. D. Farr, N. Manzonelli, I. Cruickshank, K. Starbird, and J. West, “LLM Chain Ensembles for Scalable and Accurate Data Annotation,” Proceedings of the 2024 IEEE International Conference on Big Data, 2024. Available: https://arxiv.org/abs/2410.13006.
2. D. Farr, N. Manzonelli, I. Cruickshank, and J. West, “RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science,” Proceedings of the 31st International Conference on Computational Linguistics (COLING), 2025. Available: https://arxiv.org/abs/2408.08217.
3. D. T. Farr, I. Cruickshank, and N. D. Bastian, “Towards a Systems Thinking Model of Decision Dominance,” Phalanx, Fall 2023.
4. D. Farr, “Meta-analysis by Estimation of Total Relevant Information Content in Biological Data (METRIC),” Thesis, School of Mathematics, University of Edinburgh, August 2018.
5. D. Farr, “An Analysis of Facility Entrance and Exit Data to Detect Anomalies Using the Classification and Regression Tree Algorithm Technique,” Advanced Individual Study Project, United States Military Academy at West Point, December 2016 (FOUO).
6. B. Li, S. M. Clohisey, B. S. Chia, B. Wang, A. Cui, T. Eisenhaure, L. D. Schweitzer, P. Hoover, N. J. Parkinson, A. Nachshon, N. Smith, T. Regan, D. Farr, et al., “Genome-wide CRISPR screen identifies host dependency factors for influenza A virus infection,” Nature Communications, January 2020. Available: https://www.nature.com/articles/s41467-019-13965-x.
7. M. Cushing, H. Kwak, D. Grabher, and D. Farr, “Open-Source Intelligence within Dense Urban Environments,” Proceedings of the Annual General Donald R. Keith Memorial Conference, United States Military Academy at West Point, May 4, 2017. (Best Paper Decision Analysis Track and Honorable Mention, TRADOC Army Warfighting Challenge Competition).
8. J. V. Farr and D. T. Farr, “The Role of Engineer Managers in the Age of Data-Driven Decision Making,” in Handbook of Engineering Management: The Digital Economy, L. Lunevich, Ed., CRC Press, May 30, 2023.
9. J. V. Farr and D. T. Farr, “Decision Analysis Driven by Complexity and Big Data,” in Handbook of Engineering Management: The Digital Economy, L. Lunevich, Ed., CRC Press, May 30, 2023.