MA Muhammad Aqeel

Muhammad Aqeel

Postdoctoral Research Fellow

I am a Postdoctoral Research Fellow at the University of Verona, Italy, developing anomaly detection methods for industrial manufacturing systems. I completed my PhD in Computer Science from University of Verona under the supervision of Francesco Setti, specializing in unsupervised learning approaches for anomaly detection, with over 12 publications at leading computer vision conferences including ICCV and ECCV. I earned my Master's degree from Beijing University of Posts and Telecommunications under Xiaohong Liu, focusing on Natural Language Processing and sentiment analysis, and my Bachelor's degree from PMAS Arid Agriculture University, Pakistan. My research bridges theoretical AI and practical industrial applications, with a focus on developing efficient, scalable unsupervised anomaly detection systems for real-world manufacturing environments.

2022 - 2025
PhD in Computer Science
2019 - 2021
Master in Computer Science
2013 - 2017
Bachelor in Computer Science

Professional Experience

2025 - Present
Postdoctoral Research Fellow
University of Verona, Italy
Jun - Nov 2025
Visiting Researcher
University of Ljubljana, Slovenia
Dec 2024 - May 2025
Public Service Administrator
Comune di Ferrara, Italy
2023 - 2025
Teaching Assistant
University of Verona, Italy
2021 - 2022
Research Assistant
Huazhong University of Science and Technology, China
2018 - 2019
Software Quality Assurance
UIIT, Arid Agriculture University, Pakistan
2017 - 2018
Software Developer
eConceptions (Pvt) Ltd., Pakistan

Funding & Grants

PNRR PhD Scholarship (2022–2025)
Italian Ministry of University and Research via University of Verona
Full tuition + €16,500/year stipend (3 years)
Research Mobility Grant (Jun–Dec 2025)
University of Ljubljana, Slovenia
€6,000 for 6-month visiting researcher position
Chinese Government Scholarship (2019–2021)
China Scholarship Council via Beijing University of Posts and Telecommunications
Full tuition waiver + monthly living allowance (2 years)

Research Publications

[1]

Semantic Parsing for Aspect Based Sentiment Analysis

Muhammad Aqeel, Francesco Setti | IEEE Access 2025 | PDF
[2]

Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti | ICCV 2025 | PDFCode
[3]

Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti | ECCV 2024 (Oral) | PDF
[4]

A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection

Muhammad Aqeel, Danijel Skocaj, Marco Cristani, Francesco Setti | ICCVW 2025 (Oral) | PDF
[5]

Robust Anomaly Detection in Industrial Environments via Meta-Learning

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti | ICCVW 2025 (Oral) | PDF
[6]

Diffusion-Based Data Augmentation for Medical Image Segmentation

Maham Nazir, Muhammad Aqeel, Francesco Setti | ICCVW 2025 | PDF
[7]

RoadFusion: Latent Diffusion Model for Pavement Defect Detection

Muhammad Aqeel, Kidus Dagnaw Bellete, Francesco Setti | ICIAP 2025 (Oral) | PDF
[8]

ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis

Muhammad Aqeel, Federico Leonardi, Francesco Setti | ICIAP 2025 | PDF
[9]

Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation

Muhammad Aqeel, Maham Nazir, Zanxi Ruan, Francesco Setti | ICIAP 2025 | PDF
[10]

Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti | VISAPP 2025 (Oral) | PDF
[11]

Self-supervised Learning for Robust Surface Defect Detection

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti | DeLTA 2024 (Oral) | PDF
[12]

Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection

Luigi Capogrosso, Federico Girella, Francesco Taioli, Michele Dalla Chiara, Muhammad Aqeel, Franco Fummi, Francesco Setti, Marco Cristani | VISAPP 2024 | PDFCode