Education and work experiences


EDUCATION

PhD in Machine Learning and Intelligent Systems
September 2021–September 2024
Wroclaw University of Science and Technology, Wroclaw, Poland
Awarded fellowship: Marie Skłodowska-Curie Fellowship - H2020 project European Union
Research Area: AI-driven multimodal data fusion and analysis for real-time complex system monitoring
  • Lightweight Deep convolutional neural network (DCNN) architecture development (U-Net, CNN fusion models)
  • Deep learning-driven multidimensional data fusion framework for complex system condition monitoring
  • Feature engineering in DCNNs via generative adversarial networks (GANs) for enhanced representation learning
  • Advanced unsupervised dimensionality reduction techniques for scalable big data visualization
MSc Control Systems Engineering
September 2019–February 2021
Science and Research branch, Azad University, Tehran, Iran
Research Area: AI-driven autonomous control systems for adaptive predictive process monitoring
  • Machine Learning models application for optimizing complex industrial systems
  • Bio-Inspired optimization methods (e.g., reinforcement learning, Bee algorithm) for industrial applications
BSc Aerospace Engineering
September 2014–June 2019
Science and Research branch, Azad University, Tehran, Iran
  • AI-enhanced intelligent sensors for autonomous real-time decision-making in Industrial automation
  • Rapid model-based control prototyping with edge computing and digital twins
 
WORK EXPERIENCE

Postdoctoral Fellow
September 2024 - December 2024
Wroclaw University of Science and Technology, Wroclaw, Poland
Focus: AI-driven risk assessment models with multimodal data fusion: integrating text, video, and audio
  • Cross-Domain heterogeneous sensor fusion for explainable risk assessment in edge-AI systems
  • Dynamic weighted voting for multisource data fusion using an ensemble of deep learning models approach
Data Scientist
September 2021 - September 2024
AMC VIBRO SP. Z O.O., Krakow, Poland
Focus: Unified Multimodal Data Pipelines: Cloud-Based Processing for Industrial Systems Monitoring
  • Deep learning-based models development for anomaly detection in multimodal industrial data
  • Benchmarking and competitive analysis of machine learning models for performing data analysis tasks
Visiting Scientist
February 2024 - May 2024
Fraunhofer Institute for Structural Durability and System Reliability, Darmstadt, Germany
Focus: Deep learning-driven condition monitoring frameworks
  • Multimodal data fusion approaches in condition monitoring models
  • Probabilistic uncertainty-aware decision fusion of neural network
 
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