Mr. Barzan Saeedpour | Optimization Awards | Excellence in Innovation Award
Mr. Barzan Saeedpour | Optimization Awards | University of Kurdistan | Iran
Mr. Barzan Saeedpour is a highly skilled and innovative Artificial Intelligence Engineer with extensive experience in developing and deploying intelligent systems that integrate machine learning, computer vision, and sensing technologies for real-world applications. He holds a Master’s degree in Artificial Intelligence and a Bachelor’s degree in Electrical Engineering from the University of Kurdistan, where his academic foundation was strengthened by advanced coursework in neural networks, pattern recognition, computer vision, and complex network dynamics, reflecting a strong background in computational intelligence and applied engineering. His professional career demonstrates progressive expertise, beginning as a Senior AI Engineer at Tabadolat Electronic Gharb in the Kurdistan Science and Technology Park, where he led projects on traffic sign detection, automated parking systems, road safety monitoring, and medical image-based disease detection, before advancing to Lead AI Engineer at Birkar System, where he directed innovative projects such as Iranian car license plate detection using computer vision, cloud-based GIS microservices, LLM fine-tuning for customer service, and real-time IP camera monitoring. Through these roles, Mr. Barzan Saeedpour has demonstrated remarkable ability to lead multidisciplinary teams, apply cutting-edge research to industrial solutions, and design scalable architectures for AI-driven sensing platforms. Mr. Barzan Saeedpour has contributed 2 scholarly publications, which have collectively received 4 citations, resulting in an h-index of 1. His most recent publication is titled “Mediating between filter and wrapper via probabilistic models: A hybrid feature selection framework for multi-label data” published in Engineering Applications of Artificial Intelligence
Selected Publications
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Saeedpour, B., Akhlaghian, F., Ramezani, M., & Hosseini, E. (2025). Mediating between filter and wrapper via probabilistic models: A hybrid feature selection framework for multi-label data. Engineering Applications of Artificial Intelligence.
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Hosseini, E., Saeedpour, B., Banaei, M., & Ebrahimy, R. (2025). Optimized deep neural network architectures for energy consumption and PV production forecasting. Energy Strategy Reviews.
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Manbari, Z., Salavati, C., Akhlaghian, F., Delbina, H., Saeedpour, B., & Mohammad, M. A. (2021). Parallel local feature selection and high-dimensional data classification. Proceedings of the International Conference on Computer and Knowledge Engineering (ICCKE). Citations: 2
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Saeedpour, B., Akhlaghian, F., & Hosseini, E. (Preprint/Conference Work). Applications of probabilistic models for multi-label feature selection in AI-driven sensing systems.