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.

🔔 News

🧠 [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

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)

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

Teaching

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)

Publications

Full Publication List

Students

Full Student List

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