Chandan Reddy

Chandan Reddy is an associate professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. from Cornell University and an M.S. from Michigan State University. His primary research interests are data mining and machine learning with applications to healthcare analytics, bioinformatics, and social network analysis.

Reddy’s research is funded by the National Science Foundation, the National Institutes of Health, the Department of Transportation, and the Susan G. Komen for the Cure Foundation. He has published more than 70 peer-reviewed articles in leading conferences and journals including SIGKDD, WSDM, ICDM, SDM, CIKM, TKDE, DMKD, TVCG, and PAMI. He received the Best Application Paper Award at the ACM SIGKDD conference in 2010; Best Poster Award at the IEEE VAST conference in 2014; and was a finalist of the INFORMS Franz Edelman Award Competition in 2011.

Reddy is a senior member of the IEEE and life member of the ACM.

Associate Professor
Research Areas:

Health Informatics
Social Media Analytics

Phone: 571-858-3307
Email

Publications

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Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization

Chandan Reddy
In Proceedings of the ACM ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp ), 08/2015
Citatations: BibTeX

Project Recommendation Using Heterogeneous Traits in Crowdfunding

Chandan Reddy
In Proceedings of the AAAI AAAI Conference on Web and Social Media, (pp ), 05/2015
Citatations: BibTeX

Geographical Latent Variable Models for Microblog Retrieval

Chandan Reddy
In Proceedings of the European Conference on Information Retrieval, (pp ), 03/2015
Citatations: BibTeX

Projects

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Computational Methods for Promoting and Predicting Project Campaigns in Crowdfunding Environments EAGER: An Integrated Predictive Modeling Framework for Crowdfunding Environments

Research Areas:

Forecasting

Dates: August 15, 2016 – July 31, 2018

Sponsors:

National Science Foundation

Rehospitalization Analytics: Modeling and Reducing the Risks of Rehospitalization This project provides a comprehensive and accurate assessment of risk of rehospitalization and has the potential to direct more aggressive treatments towards specific high-risk patients.

Research Areas: