Kurt Luther

• Digital History
• Public History
• Military History
• History of Photography

• Postdoctoral Fellow, Carnegie Mellon University
• PhD, Georgia Institute of Technology
• BS, Purdue University

• Director, Crowd Intelligence Lab
• Contributing Editor, Military Images Magazine
• Member, Center for Human-Computer Interaction

• Outstanding Technology Alumni Award, Purdue Polytechnic Institute (2019)
• ACM IUI Best Paper Award (2019)
• Outstanding New Assistant Professor Award, Virginia Tech College of Engineering (2018)
• Grand Prize, Microsoft Cloud AI Research Challenge (2018)
  
• National Science Foundation CAREER Award (2017)

Journal Articles

Vikram Mohanty*, Kareem Abdol-Hamid*, Courtney Ebersohl*, and Kurt Luther. 2019. Second Opinion: Supporting last-mile person identification with crowdsourcing and face recognition. In Proceedings of the 7^th AAAI Conference on Human Computation and Crowdsourcing (HCOMP ’19), 86–96. (22/87 = 25.3% acceptance rate)

Sukrit Venkatagiri*, Jacob Thebault-Spieker*, Rachel Kohler*, John Purviance*, Rifat Sabbir Mansur*, and Kurt Luther. 2019. GroundTruth: Augmenting expert image geolocation with crowdsourcing and shared representations. Proceedings of the ACM on Human-Computer Interaction 3, CSCW: 107:1–107:30. (205/658 = 31.2% acceptance rate)

Tianyi Li*, Chandler J. Manns*, Chris North, and Kurt Luther. 2019. Dropping the baton? Understanding errors and bottlenecks in a crowdsourced sensemaking pipeline. Proceedings of the ACM on Human-Computer Interaction 3, CSCW: 136:1–136:26. (205/658 = 31.2% acceptance rate)

V. Mohanty, D. Thames, S. Mehta, and K. Luther. Photo Sleuth: Combining Human Expertise and Face Recognition to Identify Historical Portraits. ACM Conference on Intelligent User Interfaces (IUI 2019), Los Angeles, CA, USA, 2019. (25% acceptance rate)

N.-C. Wang, D. Hicks, and K. Luther. Exploring Trade-Offs Between Learning and Productivity in Crowdsourced History. Proceedings of the ACM on Human-Computer Interaction, 2 (CSCW), 2018. (26% acceptance rate)

T. Li, K. Luther, and C. North. CrowdIA: Solving Mysteries with Crowdsourced Sensemaking. Proceedings of the ACM on Human-Computer Interaction, 2 (CSCW), 2018. (26% acceptance rate)

• “Transforming Investigative Science and Practice with Expert-Led Crowdsourcing,” National Science Foundation, $554,628. PI.
•  “The American Soldier Collaborative Digital Archive,” National Endowment for the Humanities, $50,000. Co-PI.
• “GraphCrowd: Using Crowdsourced Design to Visualize Effects of Environmental Chemicals on Signaling Networks,” National Institutes of Health, $626,159. PI.
• “Mapping the Fourth of July in the American Civil War Era: A Crowdsourced Digital Archive,” National Historical Publications and Records Commission, $74,224. Co-PI.
• “Supporting Crowdsourced Sensemaking in Big Data with Dynamic Context Slices,” National Science Foundation, $500,000. PI.