Preston Award for Best STEM Master’s Thesis

Congratulations to Crowd Lab alumna Rachel Kohler for winning the William Preston Society Award for Outstanding Master’s Thesis at Virginia Tech. The Preston Award recognizes the best original research with the potential to benefit all people. Rachel won in the STEM category, which includes any field of science, technology, engineering, or mathematics at VT.

According to a VT News article, “The William Preston Society is comprised of former members of the Virginia Tech Board of Visitors and the current president and past presidents of Virginia Tech.”

Rachel graduated from VT in 2017 with bachelor’s and master’s degrees in computer science. Her thesis committee was Dr. Luther (chair), Dr. Chris North, and Dr. Mike Horning.

Received ICTAS Junior Faculty Award

Dr. Luther received an Institute for Critical Technology and Applied Science (ICTAS) Junior Faculty Award. The award includes a two-year, $80,000 grant to support his lab’s research on using crowdsourcing and computer vision to identify people in historical and modern photographs. The co-PI on the grant is Prof. Paul Quigley of Virginia Tech’s History department.

Dr. Luther previously received a $10,000 seed grant from ICTAS to support his research on crowdsourcing and context slices, in collaboration with Dr. Chris North in the Computer Science department.

More details are available in the VT News press release.

Notable Paper Award at HCOMP 2017

Adam Kalai, Jeff Nichols, and Steven Dow presenting Notable Paper Award to Kurt Luther at HCOMP 2017

Our paper on crowdsourced image geolocation and diagramming won the Notable Paper Award at HCOMP 2017. Congrats to Crowd Lab alums Rachel Kohler and John Purviance, co-authors of the paper, for this recognition. In the photo above, Dr. Luther receives the award certificate on behalf of his co-authors from Adam Kalai and Steven Dow (HCOMP 2017 co-chairs) and Jeff Nichols (Awards committee).

You can read the award-winning paper, Supporting Image Geolocation with Diagramming and Crowdsourcing, in the online proceedings.