Research
My current research interests are focused on methods used in the interdisciplinary field of Data Science including:
- Artificial Intelligence/Machine Learning/Data Mining: association rule mining, Data stream mining (focus on clustering), sequence mining, recommender systems, reinforcement learning (MDP/POMDP), data visualization.
- Combinatorial Optimization: Traveling Salesman Problem, seriation, optimal ordering and scheduling problems, density and graph-based clustering.
- Application Areas: bioinformatics, healthcare analytics, quantitative marketing, earth sciences, manufacturing, and engineering problems.
- Research Software Development: see Software Section.
Publications, Talks and Former Students
- Publications
- Talks
- The research lab for Intelligent Data Analysis Lab@SMUis now part of the SMU Artificial Intelligence Laboratory.
- List of graduated student researchers with topics and theses.
Research Projects
- Data Science Supplemental: Evaluation of Liquefaction Potential of Saturated Granular Soils Under Partial Drainage Conditions (Supplement to CMMI-1728612 by Usama El Shamy, NSF, 2021-2022).
- SAFE-NET: An Integrated Connected Vehicle and Computing Platform for Public Safety Applications funded by NIST (60NANB17D180, 2017-2020). Read the press release.
- QuasiAlign: Position Sensitive P-Mer Frequency Clustering with Application to Classification and Differentiation funded by NIH (R21HG005912, 2011-2014).
- TRACDS: Temporal Relationships Among Clusters in Data Streams funded by NSF (IIS-0948893, 2009-2013). Our research on hurricane intensity prediction was featured in the article “Discovery: New Forecasting Algorithm Helps Predict Hurricane Intensity and Wind Speed” (Dec. 5, 2011) by the National Science Foundation and in “Weatherwatch: Can the intensity of a hurricane be predicted?” (Oct. 12, 2011) by The Guardian.
Patents
Former research topics
- Digital information management: Digital and virtual libraries
- Software engineering: Reuse and design patterns
Acknowledgement of Support
