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What Else is Important in AI we Did not Cover? 1. 2. 3. 4. 5. 6. 7. 8. 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 Department of Computer Science UH-DAIS Data Analysis and Intelligent Systems Lab Its research is focusing on: 1. Spatial Data Mining 2. Clustering and Anomaly Detection 3. Classification and Prediction 4. GIS Current Projects 1. 2. 3. 4. 5. 6. Clustering Algorithms with Plug-in Fitness Functions and Other NonTraditional 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) Department of Computer Science 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: 1. Spatio-Temporal Clustering, Interestingness Hotspot Discovery, and Change Analysis in Spatial Datasets 2. Design and Implementation of a Water Level Prediction and Flood Warning System for Harris County 3. Disaster Computing—Using AI Planning for Absorbing and Recovering from Critical Component Failures, starting May 31, 2016. 4. Educational Data Mining: Early Warning System for Failing Students/Student Self-Assessment System 5. 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 email, even if you want to start June 1, 2017! Department of Computer Science UH-DAIS Interestingness Hotspot Discovery Framework Identify hotspot seeds Air pollution dataset low-variation hotspots Grow hotspot seeds by adding neighboring objects Earthquake dataset correlation hotspots Remove redundant hotspots using a graph-based approach Find scope of hotspot s Objectives Objectives Find interesting contiguous regions in spatial data sets based on the domain expert’s notion of interestingness which is captured in an interestingness functionfunctions Allow plugin interestingness to be used with point based, polygonal or gridded datasets Develop algorithms to create Gridded dataset neighborhood graph for point based datasets. Remove redundant overlapping hotspots and find the scope of each hotspot. Point-based and Polygonal datasets By Fatih Akdag and Christoph F. Eick Spatio-Temporal Clustering Remark: Future Research will also investigate Spatio-Temporal Event Detection Analyzing NYC Cab Pickup Data People: Yongli Zhang and Karima Elgarroussi Department of Computer Science UH-DAIS 1d: Spatio-temporal Event Detection Example: Event Detection System Architecture Department of Computer Science Educational Data Mining (EDM) People: Nouhad Rizk, Karthik Bibireddy, Rohith Jidagam and Alex Lam Department of Computer Science UH-DMML