DAC is home to high-profile research, garnering recognition within and beyond the data analytics community.
Our talented team has been recognized with many competitive research awards and featured in major news and media outlets such as the Wall Street Journal, Newsweek, the Boston Globe and the Chronicle of Higher Education.
Rongrong Tao, DAC Ph.D. student in computer science
The prospect of being located in the heart of Northern Virginia drew Rongrong Tao to Virginia Tech and the Discovery Analytics Center in the National Capital Region. A Ph.D. student who earned a master’s degree in computer science at the University of Michigan in Ann Arbor, Tao conducts research on misinformation detection and analysis.
“This location provides opportunities for collaboration with academic institutes in the metropolitan D.C. area and an advantage for future career advancement,” said Tao. After graduation, she would like to work in an industry where she can apply data mining techniques to real-world problems.
Ellis Kessler graduated from Virginia Tech in the spring of 2017 with a bachelor’s degree in mechanical engineering. The following fall, he was back at the university working toward a Ph.D.
“As an undergraduate, I knew I wanted to do some kind of research with vibrations or structural dynamics. My adviser, Pablo Tarazaga, taught about vibrations and by working with him in the Virginia Tech Smart Infrastructure Laboratory, I became involved with vibrations research on the Goodwin Hall building,” Kessler said.
Baker, also a National Science Foundation research trainee in the UrbComp program, is a math major advised by Mark Embree, professor of mathematics, associate director of the Virginia Tech Smart Infrastructure Laboratory, and DAC faculty.
Yao is a computer science major advised by Bert Huang, assistant professor of computer science and DAC faculty.
Leanna Ireland, a sociology major, is advised by James Hawdon, professor and director of the Center for Peace Studies.
In his research, Kaiqun Fu uses spatial data mining, urban computing, and machine learning to infer crime rates/types from street view images, roadway networks, and criminal records. Applying deep learning methods also uncover hidden safety-related patterns from the physical appearance of street blocks that help address urban safety issues.
Sorour Ekhtiari Amiri, DAC Ph.D. student in computer science
Sorour Ekhtiari Amiri developed an interest in machine learning during her senior year of college. After earning a bachelor’s degree in computer engineering from Beheshti University, she worked on machine learning applications while getting a master’s in computer engineering at the University of Tehran.
Amiri then decided to pursue a Ph.D. in computer science.
DAC faculty member Scotland Leman (left) and DAC Ph.D. student and UrbComp research trainee Matt Slifko discuss spatial relationships in the housing market.
It is no coincidence that Matthew Slifko’s research in predictive modeling in the presence of big and/or messy data deals specifically with the real estate market.
“As I prepared to return to grad school in July 2013, I was selling the house that I had bought at the beginning of the housing bubble in 2008,” said Slifko. “While predictive modeling had always been a favorite topic of mine as a student, real estate was a personal interest before it became an academic one.”
Chidubem Arachie, DAC Ph.D. student in computer science
As a computer science student at the American University of Nigera, Chidubem Arachie had spent a year as an exchange student at American University in Washington, D.C. Back in Nigeria, he graduated, taught high school math and computer science as a corpsman in the National Youth Services Corp in Lagos for a year and worked just shy of two years as a tax accountant at KPMG Nigeria.
“The Washington Post now uses this research as an internal tool for predicting the click-rate of a news article within 24 hours of publication,” said Keneshloo, who worked on this project with his advisor, Naren Ramakrishnan. He has also presented this work at the 2016 SIAM International Conference in a publication co-authored with his Washington Post collaborators.
This grant allows Reddy and his students to develop new computational techniques for some of the challenging problems that arise in the domain of computational advertising. Specifically, Reddy’s lab will be working on building deep learning based methods for the problem of identifying potential customers interested in a particular product based on the past activities in the entire customer pool. Deep learning is an important subfield of artificial intelligence.