Egawati Panjei, Ph.D.

Egawati Panjei, Ph.D.

Clinical Assistant Professor,  Computer Science 

Office Location: Caruth Hall, Suite 445

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Education

  • Ph.D., Computer Science 
    The University of Oklahoma, Norman, OK, USA
  • MS, Computer Science 
    The University of Oklahoma, Norman, OK, USA
  • BSc, Computer Science
    Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia     

Biography

Dr. Egawati Panjei earned her Ph.D. in Computer Science from The University of Oklahoma. She specializes in Machine Learning, Data Mining, and Data Stream Management, with a notable focus on anomaly explanation. Her research has been published in prestigious venues such as the VLDB Journal and the International Conference on Big Data Analytics and Knowledge Discovery.

With a rich teaching background as an Information Technology lecturer in Indonesia, Dr. Panjei excels at simplifying complex concepts and fostering engaging learning environments. Her experience as a Software Engineer further enriches her practical insights into software development and project management. Additionally, she was a recipient of the Fulbright Scholarship for her master’s degree in Computer Science.

Honors and Awards

  • Fulbright Scholarship (2012 - 2014).
  • Gallogy College of Engineering, The University of Oklahoma PhD Recruitment Excellence Fellowship (2019).

Research

  • Outlier Detection and Explanation
  • Real Time Data Stream Processing and Analysis
  • Machine Learning
  • Data Mining 
     

Recent Publications

  • • 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)