Lecturer at Shahid Bahonar University of Kerman,Iran.
Dr. Shahrzad Falahat is a visionary researcher specializing in computer vision and remote sensing with a proven track record in academic and industrial AI. Holding a Ph.D. in Computer Vision, she has led interdisciplinary AI projects for over five years, collaborating across sectors such as electrical, medical, and railway industries. Her innovations include software for fault detection in power lines, cutting electricity outages by 70%, and automatic cartography tools that significantly improve mapping efficiency. Dr. Falahat’s technical proficiency spans Python, PyTorch, TensorFlow, and embedded AI systems, making her a versatile leader in AI development. Her outstanding contributions, impactful publications, and real-world implementations make her an exceptional candidate for the Best Researcher Award.
🌍 Professional Profile:
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🏆 Suitability for the Best Researcher Award
Dr. Shahrzad Falahat exemplifies excellence in applied AI research, making her a highly suitable candidate for the Best Researcher Award. With a Ph.D. in Computer Vision and over five years of impactful industrial experience, she has led innovative projects that address critical real-world challenges. Her development of AI-powered fault detection software for power transmission lines reduced outages by 70%, while her automated cartography system cut map production time by 80%. She combines deep technical expertise in Python, PyTorch, TensorFlow, and embedded AI with strong project management and cross-sector collaboration. Her work integrates research and practice, resulting in scalable, intelligent solutions with tangible societal benefits, positioning her as a leader in the field of AI and computer vision.
🎓 Education
Dr. Shahrzad Falahat earned her Ph.D. in Computer Vision, focusing on advanced deep learning techniques and remote sensing applications. Her academic journey equipped her with a robust foundation in machine learning, optimization, and AI-driven image processing. Throughout her doctoral studies, she published influential research on automated systems in industrial and environmental monitoring. Her educational background is enriched by expertise in embedded systems, GPU computing, and multi-platform AI development. With a blend of theoretical insight and practical execution, Dr. Falahat continues to bridge academia and industry, pushing the frontiers of computer vision and applied AI technologies.
🏢 Work Experience
Dr. Shahrzad Falahat currently serves as a Researcher at Shahid Bahonar University of Kerman, where she leads AI projects across industries including energy, agriculture, and transportation. She has spearheaded projects that translated complex research into deployable AI solutions. Previously, she was a Medical Imaging Data Scientist at Azin Eye Surgery Center, developing real-time diagnostic systems for eye diseases. Her contributions extend to edge AI deployments using NVIDIA Jetson Nano and STM32 AI, and leading product management from conception to deployment. She excels in dataset design, stakeholder collaboration, and technical documentation. Dr. Falahat’s blend of academic depth and real-world implementation underscores her excellence in delivering innovative, scalable AI solutions.
🏅 Awards and Honors
Dr. Shahrzad Falahat’s contributions to AI-driven innovation have earned her recognition in both research and industrial domains. She has been honored for her work on reducing electricity outages through intelligent fault detection systems and for her impactful software tools that enhance mapping and diagnosis. Her projects have received institutional support, including collaborations with the Islamic Republic of Iran Railways and Azin Eye Surgery Center. She has been an invited presenter at several national workshops and conferences and is respected for her role in bridging AI research with industrial applications. Her consistent excellence in technical leadership, publication, and applied innovation positions her as a distinguished candidate for research excellence awards.
🔬 Research Focus
Dr. Falahat’s research centers on the intersection of computer vision, deep learning, and remote sensing. She develops intelligent systems for infrastructure monitoring, medical diagnostics, and geospatial mapping. Her key focus lies in real-time AI deployment, optimization of deep learning models, and embedded system integration. Notable projects include automatic fault detection in power lines and real-time eye disease detection using medical imaging, both demonstrating high accuracy and operational efficiency. She is also actively involved in railway infrastructure monitoring. Her work leverages edge computing, cloud AI platforms, and domain-specific datasets to deliver practical, scalable solutions. Dr. Falahat’s applied research addresses real-world challenges, making significant contributions to both technological advancement and societal needs.
📊 Publication Top Notes:
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Maize tassel detection and counting using a Yolov5-based model
Cited by: 16
Author(s): S Falahat, A Karami
Year: 2023
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Influence of thickness on the structural, optical and magnetic properties of bismuth ferrite thin films
Cited by: 15
Author(s): H Maleki, S Falahatnezhad, M Taraz
Year: 2018
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Synthesis and study of structural, optical and magnetic properties of BiFeO3–ZnFe2O4 nanocomposites
Cited by: 9
Author(s): S Falahatnezhad, H Maleki
Year: 2018
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Deep fusion of hyperspectral and LiDAR images using attention-based CNN
Cited by: 7
Author(s): S Falahatnejad, A Karami
Year: 2022
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PTSRGAN: Power transmission lines single image super-resolution using a generative adversarial network
Cited by: 5
Author(s): S Falahatnejad, A Karami, H Nezamabadi-pour
Year: 2024
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Influence of synthesis method on the structural, optical and magnetic properties of BiFeO3–ZnFe2O4 nanocomposites
Cited by: 5
Author(s): S Falahatnezhad, H Maleki, AM Badizi, M Noorzadeh
Year: 2019
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A comparative study on predicting the characteristics of plasma activated water: artificial neural network (ANN) & support vector regression (SVR)
Cited by: 2
Author(s): S Karimian, S Falahat, ZE Bakhsh, MJG Rad, A Barkhordari
Year: 2024
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A Spectral-Spatial Augmented Active Learning Method for Hyperspectral Image Classification
Cited by: 2
Author(s): S Falahatnejad, A Karami
Year: 2023
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PTSRDet: End-to-End Super-Resolution and object-detection approach for small defect detection of power transmission lines
Cited by: 0
Author(s): S Falahatnejad, A Karami, H Nezamabadi-pour
Year: 2025
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Building Footprint Segmentation Using the Modified YOLOv8 Model
Cited by: 0
Author(s): S Falahatnejad, A Karami, R Sharifirad, M Shirani, M Mehrabinejad, …
Year: 2024