1. 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

2. 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

3. 1a. Non-Traditional Clustering/HD/ED Algorithms
Mining Spatial &
Spatio-Temporal
Datasets
Clustering Algorithms
With plug-in Fitness Functions
Agglomerative
IHD Approaches
MOSAIC STAXAC
Agglomerative
Clustering Algorithms
AVALANCHE
Spatio-Temporal
Event Detection ST
Clustering
Prototype-based
Clustering
Parallel Computing
CLEVER
Finding High Correlation Hotspots Ozone/PM2.5
UH-DAIS

4. 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

5. 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

6. 1d: Spatio-temporal Event Detection
Example: Event Detection System Architecture

7. 2. Predicting and Understanding Flooding
ResearchTasks:
Predict Water Levels
Flood Vulnerability Mapping
Development of Flood Plans
Understand Causes of Flooding
People: Christariny Hutapea, Yongli Zhang and Yue Cao
UH-DAIS

8. A DAG-Based Chaining Approach for WLP
Raw Data
R(t),R(t-1),…(Rainfall)
W(t), W(t-1),…(Water-level)
V(t),V(t-1) (Stream Velocity)
DAG of Measuring Points
Prediction Scenario
Mapping Tool
currently under
development at UH
Model Execution
Framework
Data Sets
(one for each Measuring Point)
Single Target
Prediction
System
uses f2 f1 f3 f4
DAG
Models
(one for each Measuring Point)
off the shelf
UH-DAIS

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

10. 4. Taxonomy Generation Goals: Organizing datasets hierarchically
People: Paul Amalaman and Chong Wang
Taxonomy Generation
Algorithm
Datasets
Goals:
Organizing datasets hierarchically
Learning “Interesting” Subclasses
UH-DAIS

11. 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 resilience that employs artificial intelligence planning techniques to come up with plans that absorb or recover from failure of critical components.
We also work on “new” recovery-based resilience metrics.
UH-DAIS

12. Helping Scientists to Make Sense Out of their Data
Figure 1: Co-location regions involving deep and
shallow ice on Mars
Figure 2: Interestingness hotspots where both income and CTR are high.
Figure 3: Maryland Crime Hotspots
UH-DAIS

13. Some UH-DAIS Graduates 1
Christopher T. Ryu, Professor,
Department of Computer Science, California State University, Fullerton
Dr. Wei Ding, Associate Professor, Department of Computer Science, University of Massachusetts, Boston
Sharon M. Tuttle, Professor,
Department of Computer Science,
Humboldt State University, Arcata, California
Sujing Wang, Assistant Professor,
Department of Computer Science,
Lamar University, Beaumont, Texas
Christoph F. Eick

14. Some UH-DAIS Graduates 2
Puja Anchlia, MS Ebay, San Jose
Chun-sheng Chen, Ebay, Seattle
Chong Wang, MS Apple
Justin Thomas Johns Hopkins University Applied Physics Laboratory
Mei-kang Wu MS Microsoft, Bellevue, Washington
Jing Wang MS AOL, California
Rachsuda Jiamthapthaksin PhD Faculty, Assumption University, Bangkok, Thailand
Christoph F. Eick

15. 2016/2017 Students in the UH-DAIS Lab
PhD Students: Yongli Zhang, Fatih Akdag, Romita Banerjee, and Chong Wang
Master Students: Riny Hutapea, Yue Cao, and Karthik Bibireddy
Undergraduate Students: Alex—Lester Moreira Cruz and Hasnain Bilgrami
Visiting and Externally Advised Students: Karima Elgarroussi
Contributing Alumni: Paul Amalaman and Sujing Wang
UH-DAIS