Welcome

I am an Assistant Professor in AI at the Department of Computer Science, University of Copenhagen. My research focuses on deep generative models, robust representation learning, and medical image analysis. I work at the intersection of computer vision, health data science, and responsible AI. This website provides more information about my research, teaching, publications, and academic activities.

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🔔 News

• ⚛️ [Paper] Accepted at BMVC 2026 (November 23-26)
  Title: QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models
• 🔄 [Paper] Accepted at BMVC 2026 (November 23-26)
  Title: ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
• 🎯 [Paper – Oral] Accepted at MICCAI Workshop on Efficient Medical AI (October 1)
  Title: How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification
• 🏆 [Challenge] Organizing the FOMO26 Challenge at MICCAI 2026 (October 1)
  Title: Foundation Model Challenge for Brain MRI
• 📈 [Paper – Poster] Accepted at ECCV 2026 (September 10-12)
  Title: A Mechanism-Driven Theory of Phase Transitions in Active Learning
• 🧠 [Paper – Poster] Accepted at ECCV 2026 Workshop on Artificial Intelligence for Medical 3D Vision (September 9)
  Title: Do 3D Medical Foundation Models See Through MRI Artifacts? A Controlled Study of Representation Robustness
• 🎓 [Summer School] Leading the AI in Life Science Summer School 2026 (August 10–21)
  Focus: An intensive summer school exploring the future of health data and artificial intelligence
• 🧠 [Paper – Poster] Accepted at SPIE Medical Imaging (February 16)
  Title: MRI Embeddings Complement Clinical Predictors for Cognitive Decline Modeling in Alzheimer's Disease Cohorts
• 🧠 [Paper – Poster] Accepted at SPIE Medical Imaging (February 16)
  Title: Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease
• 🔍 [Paper – Poster] Accepted at ICCV 2025 (October 21–23)
  Title: To Label or Not to Label: PALM – A Predictive Model for Evaluating Sample Efficiency in Active Learning Models
• 🏆 [Challenge] Organizing the FOMO25 Challenge at MICCAI 2025 (September 27)
  Title: The First Foundation Model Challenge for Brain MRI
• 🦷 [Paper – Poster] Accepted at MICCAI Workshop on Oral and Dental Image Analysis (September 27)
  Title: Tooth-Diffusion: Guided 3D CBCT Synthesis with Fine-Grained Tooth Conditioning
• 🫀 [Paper – Oral] Accepted at MICCAI 2025 (September 24–26)
  Title: Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels
• 🧠 [Paper – Oral] Accepted at MICCAI Workshop on Efficient Medical AI (September 23)
  Title: RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
• 🎨 [Paper – Oral] Accepted at FAIEMA 2025 (September 18-19)
  Title: A Wavelet Diffusion Framework for Accelerated Generative Modeling with Lightweight Denoisers
• 🎤 [Talk] Keynote speaker at Computational Neuroscience – NAD Workshop (August 14)
  Title: AI-Assisted Disease Predictions

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Education

• Ph.D. in Medical Imaging, University College London, 2020
  Disease Progression Modeling and Deep Learning (Thesis)
• M.Sc. in Electronics Engineering, Sabanci University, 2015
  Computer Vision and Pattern Analysis (Thesis)

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Research

My research focuses on artificial intelligence with interests in deep machine learning and computer vision methods that are robust, generalizable, and applicable to real-world data.

• Deep Generative Models for Images 🎨🖼️⚖️🔐
  Generative adversarial networks, variational autoencoders, and denoising diffusion models
  Bias mitigation, fairness, explainability, and quality assessment

• Robust Representation Learning 🤖🔍🔄📊
  Unsupervised, self-supervised, and active learning
  Transfer learning, domain adaptation, and data augmentation
  Large vision models, efficient fine-tuning, and OOD generalization

• Medical Image Analysis 🧠🧬📈🧫
  Disease progression modeling and prediction
  Alzheimer’s disease, aging, stroke, tumors, lesions, and disorders

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Teaching

• Advanced Topics in Deep Learning (Fall 2026)
• Introduction to Data Science (Spring 2026)
• Advanced Topics in Deep Learning (Fall 2025)
• Introduction to Data Science (Spring 2025)
• Deep Learning (Fall 2024)
• Advanced Topics in Deep Learning (Fall 2024)
• Advanced Deep Learning (Spring 2024)
• Introduction to Data Science (Spring 2024)

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Publications

Full Publication List

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Students

Full Student List

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Contact

I am open to supervising Bachelor’s, Master’s, and Ph.D. students interested in computer vision, deep learning, and medical AI. If you are motivated and have a strong background in computer vision, machine learning, applied mathematics, or a related field, please feel free to get in touch.

✉️ Email: ghazi(at)di.ku.dk
🏛️ Institution: Pioneer Centre for AI, Department of Computer Science, University of Copenhagen
🗺️ Address: Øster Voldgade 3, 1350 Copenhagen, Denmark

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