This image showsMehrdad Mohannazadeh Bakhtiari

Mehrdad Mohannazadeh Bakhtiari

Postdoctoral Researcher
SC SimTech
Research Group "Statistical Model-Data Integration"
[Image: Mehrdad Mohannazadeh]

Contact

+49 711 685 60054
 Personal Webex room

Universitätsstraße 32
70569 Stuttgart
Room: 227A

  1. 2025

    1. Handwerker, J., Barthlott, C., Bauckholt, M., Belleflamme, A., Böhmländer, A., Borg, E., Dick, G., Dietrich, P., Fichtelmann, B., Geppert, G., & others, . (2025). From initiation of convective storms to their impact—the Swabian MOSES 2023 campaign in southwestern Germany. Frontiers in Earth Science, 13, 1555755.

  2. 2024

    1. Najafi, H., Modiri, E., Mohannazadeh, M., Devi Nallasamy, N. D., Rakovec, O., Shrestha, P. K., Thober, S., Vorogushyn, S., Samaniego, L., & Weiss, T. (2024). The future of high-resolution impact-based flood early warning systems. AGU Fall Meeting Abstracts, 2024, H32D–04.

  3. 2023

    1. Mohannazadeh Bakhtiari, M., & Villmann, T. (2023). An Interpretable Two-Layered Neural Network Structure--Based on Component-Wise Reasoning. International Conference on Artificial Intelligence and Soft Computing, 145–156.
    2. Bakhtiari, M. M., Staps, D., & Villmann, T. (2023). Learning Vector Quantization in Context of Information Bottleneck Theory. Esann.
    3. Mohannazadeh Bakhtiari, M., Villmann, A., & Villmann, T. (2023). The geometry of decision borders between affine space prototypes for nearest prototype classifiers. International Conference on Artificial Intelligence and Soft Computing, 134–144.

  4. 2022

    1. Bakhtiari, M. M., & Villmann, T. (2022). Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory. International Workshop on Self-Organizing Maps, 41–52.
    2. Bakhtiari, M. M., & Villmann, T. (2022). Classification by components including Chow’s reject option. International Conference on Neural Information Processing, 586–596.

  5. 2021

    1. Kaden, M., Schubert, R., Bakhtiari, M. M., Schwarz, L., & Villmann, T. (2021). The LVQ-based Counter Propagation Network-an Interpretable Information Bottleneck Approach. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

  6. 2020

    1. Musavishavazi, S., Mohannazadeh Bakhtiari, M., & Villmann, T. (2020). A mathematical model for optimum error-reject trade-off for learning of secure classification models in the presence of label noise during training. International Conference on Artificial Intelligence and Soft Computing, 547–554.

  7. 2019

    1. Villmann, T., Kaden, M., Mohannazadeh Bakhtiari, M., & Villmann, A. (2019). Appropriate data density models in probabilistic machine learning approaches for data analysis. International Conference on Artificial Intelligence and Soft Computing, 443–454.

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