Raghda Al Taei

Raghda Al Taei is a Data Scientist and Computer Engineer with a strong academic foundation in artificial intelligence, data science, and big data. She holds a Master’s degree in Computer Engineering (Artificial Intelligence) from Amirkabir University of Technology in Tehran, Iran, where her thesis focused on outlier detection methods for big data. She further specialized in Data Science through Johns Hopkins University. Currently, Raghda works as a Research Assistant - Data Scientist at the University of Alberta, where she processes and analyzes big data generated by 3D printers and uses dashboards to display data findings. Previously, she served as a Database Developer in South Korea and as an AI Researcher specializing in computer vision at Ferdowsi University in Iran. Raghda is proficient in programming languages such as SQL, R, Python, and MATLAB.

Research Summary

Data Science, Artificial Intelligence, Machine Learning, Statistical Modeling for Complex Datasets, Natural Language Processing (NLP)

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At the University of Alberta, Raghda manages and analyzes data generated by 3D printers using relational and non-relational databases. She builds dashboards to visualize trends, enabling the application of machine learning methods to derive conclusions and insights from processed and cleaned data. Her past projects include developing a Shiny app for next-word prediction using NLP, implementing a YOLO-based object detection system for real-time image processing, and designing novel ensemble-based outlier detection methods for big data—some of which have been published in Springer journals. Other notable works include an improved KNN algorithm for imbalanced datasets and an AdaBoost-SVM classification model, both of which address data complexity and deliver impactful solutions.