To help physicians decrease the number of deaths resulting from traumatic brain injuries, Chandan Reddy, associate professor in the Department of Computer Science and faculty at the Discovery Analytics Center, will use new machine learning techniques for computational models to predict short- and long-term outcomes, categorize traumatic brain injury patients, and provide interventions tailored to a specific patient and his or her injury. This four-year study is funded by a National Science Foundation grant in excess of $1 million. Click here too read more about the grant.
News featuring Chandan Reddy
After graduating with a bachelor’s degree in computer science from Webster University, Khoa Doan entered the workforce. For the next few years, he held positions as a software developer and data engineer in the advertising industry and at NASA and gained experience processing large datasets.
“I came to appreciate both the theoretical and practical contributions,” said Doan. “Working with large datasets is tricky because solutions become much more constrained. The challenge is what interests me and it makes me really happy if I am able to solve a problem.”
Doan decided to pursue a master’s degree in computer science at the University of Maryland. When looking for a Ph.D. program, he was attracted to Virginia Tech and the Discovery Analytics Center “because of a good mix of strong theoretical foundation and practical research objectives. There is a diversity and plethora of research opportunities, especially in things that matter.”
And since being at DAC, he said, “I have learned a lot from the DAC community. I have good friends with both similar and diverse research interests.”
Doan’s main research interest is in scalable machine learning and data mining. His current focus is on deep hashing for similarity search, using neural networks as a basis for efficiently “searching” for similar items in very large databases. For example, he searches for similar documents in news articles, books or papers, and images.
“This problem is very hard because we have to pay attention to both efficiency — how to retrieve the items fast, and sometimes in real-time — and effectiveness,” said Doan. “Items can be similar because of similar words, but also because of similar authors, or similar topics, thus it is very difficult to choose the right concept to describe similarity and convert these informal concepts into mathematical equations.” Doan is also working with his advisor, Chandan Reddy, on research with Criteo, a leading advertising company that has made a significant investment in machine learning.
“Having worked in the advertising industry, solving computational problems in this field is of interest to me, as well, and is a great opportunity,” Doan said.
The Discovery Analytics Center was strongly represented at the IEEE International Conference on Data Mining (ICDM) in New Orleans, Nov. 18-21, with a number of accepted research papers by DAC faculty and students and DAC faculty serving on committees and panels.
Research papers accepted for the conference include:
- “A Probabilistic Geographical Aspect-Opinion Model for Geo-tagged Microblogs”(Ph. D. student Aman Ahuja and Chandan Reddy, associate professor of computer science, collaborating with Carnegie Mellon University and Singapore University). Ahuja presented this paper at the main conference and received a student travel award.
- “Data-Driven Immunization”(Yao Zhang, Virginia Tech alumnus and currently an assistant professor at the University of Memphis; Anil Vullikanti, associate professor of computer science at the Biocomplexity Institute of Virginia Tech; Aditya Prakash, assistant professor of computer science; and the Oak Ridge National Laboratory). Prakash presented this paper — chosen as a Best Paper finalist – at the main conference.
- “Distributed Representations of Subgraphs” (DAC Ph.D. student Bijaya Adhikari; Yao Zhang; Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of DAC; and B. Aditya Prakash). Adhikari presented this paper at a conference workshop and won a student travel award.
- “Learning to Fuse Music Genres with Generative Adversarial Dual Learning” (DAC Ph.D. student Zhiqian Chen and Chang-Tien Lu, associate professor of computer science and associate director of DAC, collaborating with the Georgia Tech Center for Music Technology). Chen presented this paper at the main conference.
- Two former DAC students, Feng Chen, currently assistant professor at the State University of New York at Albany, and Liang Zhao, currently assistant professor at George Mason University, presented the paper, “A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks.” They worked on this research with the Georgia Tech Center for Music Technology. Chen presented this paper at the main conference.
- “Online and Distributed Robust Regressions under Adversarial Data Corruption” (Ph.D. student Xuchao Zhang; Liang Zhao; and Chang-Tien Lu, jointly with George Mason University and the U.S. Army Corps of Engineers). Zhang presented this paper at the main conference.
DAC faculty participation in the ICDM Conference included Chang-Tien Lu serving on the program committee and Naren Ramakrishnan serving as an area chair. Ramakrishnan also co-chaired a panel focusing on ethical and professional issues when dealing with social data with Tanushree (Tanu) Mitra, assistant professor of computer science, as one of the panelists. B. Aditya Prakash was invited to participate as a mentor in the ICDM Ph.D. Forum.
The ICDM has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels.
Congratulations to Chandan Reddy, our new DAC faculty member and associate professor of computer science for receiving an award from the National Science Foundation for his project EAGER: An Integrated Predictive Modeling Framework for Crowdfunding Environments.
EAGER aims to study data analytics tools for improving crowdfunding project success rate. Crowdfunding provides seed capital for start-up companies, creating job opportunities and reviving lost business ventures. In spite of the widespread popularity and innovativeness in the concept of crowdfunding, however, many projects are still not able to succeed. A deeper understanding of the factors affecting investment decisions will not only give better success rate to the future projects but will also provide appropriate guidelines for project creators who will be seeking funding. Click here to read more about Chandan’s project.
DAC welcomes our new faculty member, Chandan Reddy, who was appointed to associate professor in the Department of Computer Science. Chandan is joining us from Wayne State University where he was the director of the Data Mining and Knowledge Discovery (DMKD) Laboratory. His primary research interests are data mining and machine learning with applications to healthcare analytics, social network analysis and bioinformatics. Chandan is joined by two Ph.D. students, Ping Wang and Tian Shi. Click here to learn more about Chandan.