Bio
Dr. Egawati Panjei serves as an Assistant Professor in the School of Computer Science at the Gallogly College of Engineering, The University of Oklahoma. Before joining OU, she held an assistant professorship at Southern Methodist University (SMU) in Dallas, Texas, where she taught courses including Introduction to Machine Learning, Principles of Modern Computing, Operating Systems, and Introduction to Computing. Dr. Panjei earned her Ph.D. in Computer Science under the guidance of Dr. Le Gruenwald at OU, where she also completed her master’s degree in Computer Science. She holds a bachelor’s degree in Information Technology from Institut Teknologi Sepuluh Nopember (ITS) in Indonesia.
Dr. Panjei’s research focuses on advancing the fields of Machine Learning, Data Mining, and Data Stream Processing, with a special emphasis on developing robust and explainable prediction models, including those for anomaly detection. Her work strives to create algorithms that highlight critical features influencing predictions, fostering greater transparency and trust in model outcomes. She explores real-time applications in areas such as healthcare and network security, while also integrating physics-informed machine learning approaches to enhance model accuracy and reliability. Through her commitment to explainability and practical impact, Dr. Panjei aims to drive innovation in predictive modeling for complex, real-world challenges.
Research Focus
- Outlier/Anomaly Detection and Explanation
- Real Time Data Stream Processing and Analysis
- Machine Learning
- Data Mining
Teaching Experiences
- Database Management Systems
- Operating Systems
- Introduction to Machine Learning
- Principles of Modern Computing
- Introduction to Computing
- Data Structure
- Principles of Programming Languages
Professional Experiences and Awards
- Assistant Professor at SMU, Dallas, TX, 2024 - 2025
- Gallogly College of Engineering Dissertation Excellence Award, The University of Oklahoma, 2024.
- Graduate Teaching & Research Assistant, The University of Oklahoma, 2019 - 2024
- PhD Recruitment Excellence Fellowship by School of Computer Science, The University of Oklahoma, 2019
- Software Engineer at NextThought, Norman, OK and Jakarta, Indonesia, 2014 - 2019
- Fulbright Scholarship, 2012-2014
- Lecturer at Universitas Ciputra, Surabaya, Indonesia, 2007 - 2012
Selected Publications
- Panjei, E. and Gruenwald, L.: Discovering Outlying Attributes of Outliers in Data Streams. Data & Knowledge Engineering. 154, (2024). https://doi.org/10.1016/j.datak.2024.102349
- Panjei, E., Gruenwald, L.: EXOS: Explaining Outliers in Data Streams. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., and Khalil, I. (eds.) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science. pp. 25–41. Springer Nature Switzerland, Cham (2023)
- Borah, A., Diochnos, D.I., Gruenwald, L., Jafarigol, E., Panjei, E., Trafalis, T.B.: Research Issues in Adversarially Robust Stream-Based Federated Learning. In: International Conference on Optimization and Learning (OLA). pp. 80–82 (2022)
- Panjei, E., Gruenwald, L., Leal, E., Nguyen, C., Silvia, S.: A Survey on Outlier Explanations. The VLDB Journal. 31, 977–1008 (2022). https://doi.org/10.1007/s00778-021-00721-1
- Borah, A., Gruenwald, L., Leal, E., Panjei, E.: A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. In: 2021 IEEE International Conference on Big Data. pp. 2737–2742 (2021)
- Panjei, E., Gruenwald, L., Leal, E., Nguyen, C.: Micro-clusters-based Outlier Explanations for Data Streams. In: The 1st Workshop on Anomaly and Novelty Detection, Explanation and Accommodation (ANDEA), co-located with 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). pp. 1–8 (2021)
