Dr. Fardin Bahreini, Concordia University, Canada
Dr. Fardin Bahreini is an accomplished AI Specialist at Quiri AI and a Research Associate at the Concordia Institute for Information Systems Engineering. He earned his Ph.D. in Building Engineering with a focus on Machine Learning from Concordia University and holds a Master’s in Project Engineering and Management from Amirkabir University of Technology, Iran, along with a Bachelor’s in Civil Systems Engineering from Mazandaran Azad University, Iran. With expertise in AI engineering, data science, and computer vision, Dr. Bahreini specializes in generative AI and cybersecurity, and has developed advanced methods for semantic segmentation of surface defects and semi-automated defect management. His notable projects include leading ML initiatives for surface defect identification and autonomous inspection systems, and his research interests encompass advanced technical analysis, 3D point cloud analysis, NLP, and AI-driven solutions. He is also certified in Generative AI, NLP, and Project Management from DeepLearning.AI and Coursera.
🌍 Professional Profile:
🎓 Education:
Dr. Fardin Bahreini earned his Ph.D. in Building Engineering with a focus on Machine Learning from Concordia University, Montreal. He also holds a Master’s in Project Engineering and Management from Amirkabir University of Technology, Iran, and a Bachelor’s in Civil Systems Engineering from Mazandaran Azad University, Iran.
💼 Professional Experience:
Fardin has extensive experience in AI engineering, data science, and computer vision. He currently serves as an AI Specialist at Quiri AI, focusing on generative AI and cybersecurity. He is also a Research Associate at Concordia Institute for Information Systems Engineering, where he works on 3D point cloud-based detection systems.
🛠 Skills and Expertise:
His expertise spans ML algorithms, including deep neural networks (DNN), K-nearest neighbors (KNN), and support vector machines (SVM). He is adept at deploying ML models, project management, and has advanced knowledge in Python, SQL, and various ML libraries and cloud platforms.
🌟 Achievements:
Fardin has developed a deep learning method for semantic segmentation of surface defects with high accuracy. He also created a semi-automated Python process for defect management and visualization, and developed a comprehensive knowledge model for robot navigation and inspection.
📈 Notable Projects:
His past roles include leading ML projects at Concordia University and Pegasus Research and Technologies, where he developed models for surface defect identification and autonomous inspection systems. As a Project Manager at SBT Company, he utilized ML and NLP techniques for healthcare forecasting.
📚 Certifications:
Fardin holds several certifications from DeepLearning.AI and Coursera, including those in Generative AI, Natural Language Processing (NLP), and Project Management.
🔍 Research Interests:
His research interests include advanced technical analysis, 3D point cloud analysis, NLP, and AI-driven solutions for various applications.
Publication Top Notes:
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Point Cloud–Based Concrete Surface Defect Semantic Segmentation
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Ontological and Machine Learning Approaches for Inspection of Facilities Using BIM
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Point Cloud Semantic Segmentation of Concrete Surface Defects Using Dynamic Graph CNN
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Towards an Ontology for BIM-Based Robotic Navigation and Inspection Tasks
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Point Cloud Semantic Segmentation of Concrete Surface Defects Using Dynamic Graph CNN