Kurt Luther

Kurt Luther, Associate Professor

_{Department of History and Department of Computer Science

VTRC-Arlington, 900 N. Glebe Road

Arlington, VA 22203

kluther@vt.edu}

Kurt Luther is an associate professor of history and computer science at Virginia Tech.

Principal Areas of Research Focus

Digital History
Public History
Military History
History of Photography

Education

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

Professional Roles

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

Awards and Honors

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 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)

Fellowships, Grants, and Sponsored Research

“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.