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MG_3393Q_small.jpg Email: amal.saadallah At sign cs.tu-dortmund.de
Phone: 0231/755-6490
Fax: 0231/755-5105
Room-No.: OH12 R4.023

About

I work as a research assistant in the artificial intelligence group of the TU Dortmund, within B3 project (Data Mining on Sensor Data of Automated Processes)-Collaborative Research Center SFB 876 (Providing Information by Resource-Constrained Data Analysis). My main research work is about ensemble methods, time series analysis and the combination of machine learning methods and process simulation systems.

Talks and Presentations

  • Generative Adversarial Networks and Active Learning
  • Stability prediction in milling processes using a simulation-based Machine Learning approach
  • Data Mining Using Sensors Data of Automated Processes- Tunnel Project

Projects

Research Topics

Publications

Cao/etal/2021a Cao, Ba-Tung and Saadallah, Amal and Egorov, Alexey and Freitag, Steffen and Meschke, Günther and Morik, Katharina. Online Geological Anomaly Detection Using Machine Learning in Mechanized Tunneling. In In: Barla M., Di Donna A., Sterpi D. (eds) (editors), Challenges and Innovations in Geomechanics, Vol. vol 125., pages 323--330, Springer, 2021. Arrow Symbol
Saadallah/etal/2021a Saadallah, Amal and Tavakol, Maryam and Katharina, Morik. An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting. In The 37th IEEE International Conference on Data Engineering (ICDE), 2021.
Saadallah/Morik/2021a Saadallah, Amal and Katharina, Morik. Meta-Adversarial Training of Neural Networks for Binary Classification. In IJCNN International Joint Conference on Neural Networks, 2021.
Finkeldey/etal/2020a Finkeldey, Felix and Saadallah, Amal and Wiederkehr, Petra and Morik, Katharina. Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. In Engineering Applications of Artificial Intelligence, Vol. Volume 94, 2020. Arrow Symbol
Saadallah/Morik/2020g Saadallah, Amal and Katharina, Morik. Active Sampling for Learning Interpretable Surrogate Machine Learning Models. In IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2020.
Bunse/etal/2019a Bunse, Mirko and Saadallah, Amal and Morik, Katharina. Towards Active Simulation Data Mining. In Kottke, Daniel and Lemaire, Vincent and Calma, Adrian and Krempl, Georg and Holzinger, Andreas (editors), Proc. of the 3rd Int. Tutorial and Workshop on Interactive Adaptive Learning at ECML-PKDD 2019, Vol. 2444, pages 104--107, CEUR Workshop Proceedings, 2019. Arrow Symbol
Meschke/etal/2019a Meschke, Günther and Cao, Ba Trung and Egorov, Alexey and Saadallah, Amal and Freitag, Steffen and Morik, Katharina. Big data and simulation -- A new approach for real-time TBM steering.. In Peila, D.; Viggiani, G.; Celestino, T. (eds.), Tunnels and Underground Cities. Engineering and Innovation Meet Archaeology, Architecture and Art, Proceedings of the WTC 2019 ITA-AITES World Tunnel Congress (WTC 2019), Naples, Italy, Taylor & Francis, London, No. DOI: 10.1201/9780429424441-284, pages 2681 – 2690, 2019.
Saadallah/etal/2019a Saadallah, Amal and Egorov, Alexey and Cao, Ba-Trung and Freitag, Steffen and Morik, Katharina and Meschke, Günther. Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling. In CIRP Manufacturing Systems Conference 2019, 2019. Arrow Symbol
Saadallah/Moreira/2019a Saadallah, A., Moreira-Matias, L., Sousa, R., Khiari, J., Jenelius, E., Gama, J.. BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract). In 35th IEEE International Conference on Data Engineering (ICDE 2019), 2019.
Saadallah/Piatkowski/2019a Amal Saadallah, Nico Piatkowski, Felix Finkeldey, Petra Wiederkehr and Katharina Morik. Learning Ensembles in the Presence of Imbalanced Classes. In ICPRAM: 8th international conference on pattern recognition applications and methods - icpram 2019, 2019. Arrow Symbol
Saadallah/Priebe/2019c Saadallah, Amal and Priebe, Florian and Katharina, Morik. A Drift-based Dynamic Ensemble Members Selection using Clustering for Time Series Forecasting. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML PKDD 2019, Würzburg, Germany, 2019.
Grau/Moreira/2018a Josep Maria Salanova Grau, Luis Moreira-Matias, Amal Saadallah, Panagiotis Tzenos, Georgia Aifadopoulou, Emmanouil Chaniotakis, Miquel Estrada. Informed Versus Noninformed Taxi Drivers: Agent-Based Simulation Framework for Assessing Their Performance. pages 16, Transportation Research Board 97th Annual Meeting, 2018. Arrow Symbol
Saadallah/2018a Luis Moreira Matias, Amal Saadallah, Jihed Khiari. Method to control vehicle fleets to deliver on-demand transportation services.. In United States patent application US 15/281,142, 2018. Arrow Symbol
Saadallah/etal/2018a Saadallah, Amal and Finkeldey, Felix and Morik, Katharina and Wiederkehr, Petra. Stability prediction in milling processes using a simulation-based machine learning approach. In 51st CIRP conference on Manufacturing Systems, Elsevier, 2018.
Saadallah/Moreira/2018a Saadallah, A., Moreira-Matias, L., Sousa, R., Khiari, J., Jenelius, E., Gama, J.. BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. IEEE Transactions on Knowledge and Data Engineering, 2018.

Software

Supervised Theses