Data Analysis and Intelligent Systems Lab Its research is focusing on: Spatial Data Mining Clustering and Anomaly Detection Classification and Prediction GIS Current Projects Clustering Algorithms with Plug-in Fitness Functions and Other Non-Traditional Clustering Approaches Analyzing and Doing Useful Things with Bio-aerosol Data Interestingness Scoping Algorithms for the Analysis of Spatial and Spatio-temporal Datasets Using Mixture Models for Anomaly Detection and Change Analysis Taxonomy Generation—Learning Class Hierarchies from Training Data Educational Data Mining (lead by Nouhad Rizk) UH-DAIS

Research Areas and Projects Data Analysis and Intelligent Systems Lab (UH-DAIS) Its research is focusing on: Data Mining Data Science and Making Sense of Data GIS (Geographical Information Systems) AI (Artificial Intelligence) 2. Current and Planned Projects Clustering Algorithms with Plug-in Fitness Functions, other Non-Traditional Clustering and Hotspot Discovery (HD) Approaches Predicting and Understanding Flooding Educational Data Mining (jointly with Nouhad Rizk) Taxonomy Generation—Learning Class Hierarchies from Training Data Fast Execution Frameworks for Agglomerative Algorithms just starting Critical Infrastructure Resilience More AI-related research projects: particularly Planning, and maybe Internet of Things and Games looking for new students! UH-DAIS

1b. Interestingness Hotspot Discovery Objective: Find interesting contiguous regions in spatial data sets based on the domain expert’s notion of interestingness which is captured in an interestingness function Methodology: Identify hotspot seeds Grow seeds by adding neighboring objects Remove redundant hotspots using a graph-based approach Find Scope of hotspots (polygonal boundary detection) Data sets: Gridded, polygonal, point-based data sets People: Fatih Akdag

1.c Spatio-Temporal Clustering Remark: Future Research will also investigate Spatio-Temporal Event Detection Analyzing NYC Cab Pickup Data People: Yongli Zhang and Karima Elgarroussi UH-DAIS

2. Predicting, Mapping, Understanding Flooding ResearchTasks: Predict Water Levels Flood Vulnerability Mapping Development of Flood Plans Understand Causes of Flooding People: Christariny Hutapea, Chong Wang, Yongli Zhang and Yue Cao http://www.harriscountyfws.org/ UH-DAIS

3. Educational Data Mining (EDM) People: Nouhad Rizk, Karthik Bibireddy, Rohith Jidagam and Alex Lam UH-DMML

6. Dr. Eick’s Resilience Work In 2015, I was the PI of a large funded DHS project in which we designed an early warning system for bio-threats using “cheap” biosensors. I am part of a large research group at UH whose research centers on “Making the Houston Harbor Smart and Resilient”. Disaster analytics and understanding what makes communities resilient to absorb and recover from disasters. Moreover, my research group is starting new research in critical infrastructure and community resilience that employs artificial intelligence planning techniques to come up with plans that absorb or recover from failure of critical components. UH-DAIS