What Else is Important in AI we Did not Cover? Ontologies and the Semantic Web Logical Reasoning and Theorem Proving Distributed Artificial Intelligence Multi-Agent Systems Robotics Philosophical Foundation of AI Natural Language Understanding Knowledge in Learning UH-DAIS

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

Looking for 1-2 Students for Master Thesis Students should begin working on their thesis Jan. 16 or May 31, 2017: Areas of Interest include: Spatio-Temporal Clustering, Interestingness Hotspot Discovery, and Change Analysis in Spatial Datasets Design and Implementation of a Water Level Prediction and Flood Warning System for Harris County Disaster Computing—Using AI Planning for Absorbing and Recovering from Critical Component Failures, starting May 31, 2016. Educational Data Mining: Early Warning System for Failing Students/Student Self-Assessment System Event Detection in Spatio-temporal Datasets already gone! If you are interested, send me an e-mail by December 31, 2016, and I will be selecting students by January 15, 2017. Send me an e-mail, even if you want to start June 1, 2017! UH-DAIS

Interestingness Hotspot Discovery Framework Objectives Identify hotspot seeds Grow hotspot seeds by adding neighboring objects Remove redundant hotspots using a graph-based approach Find scope of hotspots Find interesting contiguous regions in spatial data sets based on the domain expert’s notion of interestingness which is captured in an interestingness function Allow plugin interestingness functions to be used with point based, polygonal or gridded datasets Remove redundant overlapping hotspots and find the scope of each hotspot. Develop algorithms to create neighborhood graph for point based datasets. By Fatih Akdag and Christoph F. Eick Air pollution dataset low-variation hotspots Earthquake dataset correlation hotspots Point-based and Polygonal datasets Gridded dataset

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

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

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