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• Data Mining
• Bioinformatics
• Neural Networks
• +5 more

Dr. Ray R. Hashemi is a Professor in the Department of Computer Science within Georgia Southern University's College of Engineering and Computing. His academic career spans over 14 years of continuous research output from 2003-2017, with significant contributions as co-editor for four International Conferences on Information Technology and Knowledge Engineering (2005, 2010, 2014, 2017). His research focuses on innovative applications of data mining across diverse domains: Bioinformatics: DNA sequence analysis, organ toxicity prediction, and liver cancer predictive systems Medical Informatics: Bone mineral density analysis using DEXA data and dendrograms Financial Systems: Extraction of essential constituents from S&P500 index Environmental Science: Climate prediction using algae sedimentation patterns Computer Vision: Video mining for theatrical analysis and Android-based OCR for non-flat documents Methodologically, Dr. Hashemi specializes in neighborhood systems analysis, association rule mining, and grid-based approaches for sparse data. His work consistently bridges theoretical data mining concepts with practical applications, developing tools for signature-based prediction, record layout discovery, and intent analysis through web behavior. Recent publications (2015-2017) show increased focus on domain-specific applications in finance and toxicology while maintaining core data mining expertise. His collaborative work includes partnerships with international researchers across multiple continents, demonstrated through conference editorial roles and co-authored publications. Dr. Hashemi's research demonstrates sustained scholarly activity with practical implementations in medical diagnostics, financial analysis, and environmental prediction systems.

Tinghao FengView profile

Assistant Professor

• Time Series Analysis
• Data Visualization
• Machine Learning
• +3 more

Tinghao Feng serves as an Assistant Professor at Appalachian State University, specializing in time-oriented data analysis and visualization systems. His research develops comprehensive tools for exploring temporal patterns across environmental science, healthcare, and finance domains through innovative data mining algorithms and user-friendly software applications. His primary research interests focus on Time Series Analysis and Data Visualization, with significant contributions to Machine Learning applications. He investigates methods for visualizing complex temporal data, developing pattern recognition algorithms, and creating interactive analysis frameworks applicable to environmental monitoring, clinical informatics, and financial forecasting systems. Recent publications reveal a strong emphasis on visual analytics for temporal data, spanning environmental applications (honey bee monitoring, landslide prediction), healthcare informatics (patient messaging systems, pandemic modeling), and remote sensing. Key developments include the TimePool system for univariate time series querying and EVis for environmentally driven event analysis, demonstrating cross-disciplinary impact. Dr. Feng maintains active demonstration systems including TimePool for temporal query visualization and Polynomial Similarity Dependency Trees for linguistic structure analysis, reflecting his commitment to translating research into practical analytical tools for complex data exploration.