Department of History and Department of Computer Science
VTRC-Arlington, 900 N. Glebe Road
Arlington, VA 22203
571-858-3335 | email@example.com
Kurt Luther is an associate professor of history and computer science at Virginia Tech.
- 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)
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 7th 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.
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