Afshin Dini is a Machine Learning Engineer and doctoral researcher at Tampere University with five years of experience applying deep learning and computer vision to industrial inspection and hyperspectral imaging. His research focuses on transformer-based and self-supervised approaches for visual anomaly detection and segmentation, with papers presented at conferences such as VISAPP and ICPR. At Advian he bridges research and production by building and deploying object detection, segmentation, and defect-detection systems for real-world industrial and environmental applications. Earlier roles in automation and control engineering give him uncommon domain knowledge in PLCs, DCS/ESD systems and turbine control, enabling pragmatic solutions for sensor-rich industrial settings. He combines rigorous academic training (top grades in Data Science and Electrical Engineering) with hands-on commissioning and HMI development experience, making him adept at taking ML models from prototype to field-ready. Afshin is motivated by collaborations that push practical AI boundaries in challenging visual inspection problems.
5 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree, Electrical engineering, 16.23, Bachelor’s Degree, Electrical engineering, 16.23 at K. N. Toosi University of Technology
Master's degree, Data Science, Machine Learning, 4.95, Master's degree, Data Science, Machine Learning, 4.95 at Tampere University
Master’s Degree, Electrical and Electronics Engineering, 17.21, Master’s Degree, Electrical and Electronics Engineering, 17.21 at University of Tehran
Contributions:8 commits, 85 pushes, 1 branch in 2 months
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Afshin Dini - Machine Learning Engineer at Tampere University