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 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.
Yuliang Zou, a Ph.D. student at the Discovery Analytics Center, was in Munich, Germany, earlier this week to participate in the 2018 European Conference on Computer Vision ECCV. The conference, held every other year, is one of the most influential academic conferences for this area of research.
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.”
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.
Davon Woodard has spent the past few months in the National Capital Region as a fellow for Data Science for the Public Good (DSPG). The program, launched and directed by the Social and Decision Analytics Laboratory (SDAL) at the Biocomplexity Institute of Virginia Tech, engages young scholars in conducting research at the intersection of statistics, computation, and the social sciences to determine how information generated within the community can be leveraged to improve quality of life.
Since graduating in 2016 with a Ph.D. in computer science, Huijuan Shao has transitioned from academia to industry. For nine months, she was a research associate at George Washington University where she developed regular expression models with Java to extract clinical variables from cancer pathology reports and tuned queries performance in PostgreSQL when searching from 8TB national electronic health records. In January 2018, her career took another path. She and her family moved west, to Santa Clara, California, where she joined Hitachi America, Ltd., as a research scientist, focusing on industrial AI.
The Discovery Analytics Center and the Urban Computing Certificate Program (funded through a National Science Foundation traineeship grant and administered through DAC) will be well represented at the 24th Annual Association for Computing Machinery Special Interest Knowledge Discovery and Data Mining (KDD 2018) conference in London, August 19-23.
The overall theme of this year’s conference is data mining for social good.