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Katerina Katsarou

Lupe [1]
© Katerina Katsarou

Katerina Katsarou received her Diploma from the Department of Electrical and Computer Engineering-Polytechnic School of University of Patras and her Master's Degree in Computer Science from University of Ioannina.

During her graduate studies she attended courses in the field of  Data Mining, Statistical Algorithms for Medical Applications and Advanced Topics in Relational Databases. She participated in research projects in the Department of Computer Technology and Informatics of University of Patras and was a lab assistant in the Department of Computer Science and Engineering of University of Ioannina.

In January 2018, she joined  the Service-centric Networking group of Prof. Dr. Axel Küpper at Telekom Innovation Laboratories as a research scientist in the field of data science and machine learning. 

Research Interests & Awards

Lupe [2]
© ISNCC 2020


    • Sentiment Analysis
    • Context-aware Recommender Systems
    • Deep Learning
    • Stock market and Cryptocurrency prices forecasting
    • Community detection in online social networks
    • R, Python, SQL

    Publications

    Order by: Author [3]  Year [4]  Journal [5]

    Katsarou, K. and Ounoughi, C. and Mouakher, A. and Nicolle, C. (2020). STCMS: A Smart Thermal Comfort Monitor For Senior People [6]. 28th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2020). IEEE (accepted for publication).

    Hagemann, T. and Katsarou, K. (2020). Reconstruction-based anomaly detection for the cloud: A comparison on the Yahoo!Webscope S5 dataset [7]. 4th International Conference on Cloud and Big Data Computing(ICCBDC 2020). ACM, 68-75.

    Fischer, S. and Katsarou, K. and Holschke, O. (2020). DEEPFLOW: Toawards Network-Wide Ingress Traffic Prediction Using Machine Learning At Large Scale [8]. 7th International Symposium on Networks, Computers and Communications(ISNCC 2020). IEEE.

    Hagemann, T. and Katsarou, K. (2020). A Systematic Review on Anomaly Detection for Cloud Computing Environments [9]. 3rd Artificial Intelligence and Cloud Computing Conference (AICCC 2020). ACM, 83-96.

    Hatzinikolaou D., Katsarou K. (2019). An Account of Principal Components Analysis and Some Cautions on Using the Correct Formulas and the Correct Procedures in SPSS [10]. International Journal of Statistics & Applications. Scientific & Academic Publishing Company, 160-169.

    Katsarou, K. and Shekhawat, D. S. (2019). CRD-SentEnse: Cross-domain Sentiment Analysis using an Ensemble Model [11]. Proceedings of The 11th International ACM Conference on Management of Digital EcoSystems(MEDES 2019). ACM (accepted for publication).

    Supervised Theses

    • Hurair Hashimi, S.M. (2020). A hybrid approach to stock market predictive analysis based on microservices architecture. Master Thesis, Technische Universität Berlin [12]
    • Jeney, R. (2020). Multi-Domain Sentiment Classification using an LSTM-based Framework with Attention Mechanism. Master Thesis, Technische Universität Berlin [13]
    • Winata, I. (2020). A Machine Learning-based Mechanism for Feature Extraction for CDSA. Master Thesis, Technische Universität Berlin [14]
    • Sunder, S. (2020). Sentiment Polarization in Online Social Networks: The Flow of Hatespeech. Master Thesis, Technische Universität Berlin [15]
    • Yu, G. (2019). Predicting the next App based on Smartphone Data. Master Thesis, Technische Universität Berlin [16]
    • Mai, J. (2019). A hybrid approach for emotion-based sentiment analysis for Twitter data. Bachelor Thesis, Technische Universität Berlin [17]
    • Dhakal, U. (2019). Cross-domain sentiment analytics using a Deep learning approach. Bachelor Thesis, Technische Universität Berlin [18]
    • Shekhawat, D. (2019). Sentiment Analysis for Product Reviews Using Machine Learning. Master Thesis, Technische Universität Berlin [19]

    [20]

    Katerina Katsarou
    Service-centric Networking
    TEL 19
    Ernst-Reuter-Platz 7
    10587 Berlin
    Phone: +49 30 8353 58149
    Fax: +49 30 8353 58409
    Contact [21]

    [22]
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