Muhammad Aqeel

I am pursuing my PhD in Computer Science at University of Verona, Italy, under the supervision of Francesco Setti, where I specialized in developing advanced anomaly detection algorithms for industrial manufacturing environments. My doctoral research focused on creating robust deep learning architectures that can automatically identify defects, irregularities, and quality issues in real-time production settings, addressing critical challenges in industrial automation and quality control. This work involved designing novel convolutional neural networks and unsupervised learning approaches that achieve high accuracy while maintaining computational efficiency suitable for deployment in resource-constrained industrial systems. I received my Master's degree in Computer Science from Beijing University of Posts and Telecommunications (2019-2021), under the supervision of Xiaohong Liu, where my research centered on Natural Language Processing with a particular focus on sentiment analysis, exploring innovative methods for understanding and classifying emotional content in text data. I graduated with my Bachelor's degree in Computer Science from PMAS Arid Agriculture University Rawalpindi, Pakistan (2013-2017), where I built a strong foundation in programming and algorithmic thinking. My interdisciplinary background spanning from industrial computer vision to natural language understanding enables me to approach complex machine learning challenges from multiple perspectives, with a current focus on advancing industrial AI through scalable and interpretable anomaly detection systems that can transform manufacturing quality assurance and enable predictive maintenance strategies.