|Present||Associate Professor, Wright State University ‐ Computer Science and Engineering|
|Present||Director, Data-Intensive Analysis and Computing (DIAC) Lab, Wright State University ‐ Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis)|
Joshi Research Center 385
3640 Colonel Glenn Hwy
Dayton, OH 45435-0001
Knowledge Graph Enhanced Community Detection and Characterization
WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining (2019)
Recent studies show that by combining network topology and node attributes, we can better understand community structures in complex networks. ...
Confidential Boosting with Random Linear Classifiers for Outsourced User-Generated Data
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2019)
User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record ...
Who Should Be the Captain This Week? Leveraging Inferred Diversity-Enhanced ...
Proceedings of the 13th International Conference on Web and Social Media, ICWSM 2019 (2019)
Participants in Fantasy Sports make a critical decision: selecting productive players for their fantasy team. The well-established Wisdom of Crowd ...
CRESP: Towards Optimal Resource Provisioning for MapReduce Computing in Public ...
IEEE Transactions on Parallel and Distributed Systems (2014)
Running MapReduce programs in the cloud introduces this unique problem: how to optimize resource provisioning to minimize the monetary cost ...
Optimizing Star-Coordinate Visualization Models for Effective Interactive Cluster Exploration on ...
Intelligent Data Analysis (2014)
Interactive visual cluster analysis is the most intuitive way for finding clustering patterns, validating algorithmic clustering results, understanding data clusters ...
Privacy-Preserving Multiparty Collaborative Mining with Geometric Data Perturbation
IEEE Transactions on Parallel and Distributed Systems (2009)
In multiparty collaborative data mining, participants contribute their own data sets and hope to collaboratively mine a comprehensive model based ...