Visionary Interdisciplinary Engineer
Unifying Advanced AI and Mechanical Engineering to build the next generation of autonomous systems.
Visionary Interdisciplinary Engineer and Strategic Innovator with a high-value dual-competency in Mechanical Engineering and Advanced AI. I specialize in architecting "Digital Twin" ecosystems and "Agentic AI" workflows—bridging the gap between physical systems and autonomous digital intelligence.
A proactive theorist and idea generator, I offer a unique competitive advantage: the ability to translate complex mechanical realities into predictive, data-driven Industry 5.0 solutions. Distinguished by exceptional adaptability and strict punctuality, I am dedicated to driving institutional growth through precise documentation and future-proof technologies.
SolidWorks, Ansys Fluent, ESI PAM-RTM, Mold Flow Analysis, DFM
TensorFlow, Keras, PyTorch, GradientTape, Custom Layers, Neural Architecture Search
OpenCV, ResNet-50, U-Net, Mask-RCNN, YOLO, Image Segmentation, Tracking
RNNs, LSTMs, GRUs, Transformers, Tokenization, Prompt Engineering
Python, Git/GitHub, MATLAB, SQL, API Integration, MS Project, Office
Strategic Planning, Technical Documentation, Ideation, Agile, Punctuality
Ferdowsi University of Mashhad
July 2025 - Dec 2025
Ferdowsi University of Mashhad
Sep 2023 - July 2025
Energy Conversion (GPA: 17.23/20)
Ferdowsi University
2019 - 2023
Birjand University
2014 - 2019
Designed custom object detection models (ResNet-50, R-CNN) and semantic segmentation architectures (U-Net, Mask-RCNN) for unstructured industrial environments. Implemented Saliency Maps for model interpretability.
Developed sophisticated Sequence-to-Sequence models (LSTMs, GRUs) for natural language tasks. Built a generative text model trained on large-scale corpora for creative sequence prediction.
Engineered custom training loops using TensorFlow GradientTape. Implemented Distributed Training strategies across multi-GPU/TPU cores. Designed Siamese Networks with contrastive loss.
Master of Science Thesis, Ferdowsi University of Mashhad (Sep 2023)
This thesis introduces a novel approach using machine learning and artificial neural networks (ANNs) to determine thermodynamic properties and simulate the vehicle tank filling process. ANN models replace traditional methods, offering faster simulations with minimal computational power. Validated against AGA8 and GERG-2008 equations of state.
Validated expertise in building and deploying scalable AI models across Computer Vision, NLP, and Time Series domains.
Mastered advanced techniques including object detection, segmentation (U-Net, Mask-RCNN), and model interpretability.
Proficiency in custom training loops (GradientTape) and distributed training strategies for HPC.
Acquired deep architectural control capabilities, building custom layers for specialized problem-solving.
Specialized in sequence modeling and text processing using RNNs, GRUs, and LSTMs for sentiment analysis.
Established a strong foundation in the end-to-end machine learning lifecycle and model optimization.
Native
Advanced
Open to new opportunities in AI, Digital Twins, and Smart Manufacturing.
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