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 data 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

• 🎤 [Talk] Keynote speaker at Computational Neuroscience – NAD Workshop (August 14)
  Title: AI-Assisted Disease Predictions

• 🎨 [Paper – Oral] Accepted at FAIEMA 2025 (September 18-19)
  Title: A Wavelet Diffusion Framework for Accelerated Generative Modeling with Lightweight Denoisers

• 🧠 [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 MICCAI 2025 (September 24–26)
  Title: Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels

• 🦷 [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

• 🏆 [Challenge] Organizing the FOMO25 Challenge at MICCAI 2025 (September 27)
  Title: The First Foundation Model Challenge for Brain MRI

• 🔍 [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

---

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, privacy, and quality assessment

• Robust Representation Learning 🤖🔍🔄📊
  Unsupervised, self-supervised, and active learning
  Transfer learning, domain adaptation, and data augmentation
  Multimodal, heterogeneous, and time-series data

• Brain Health Data Analysis 🧠🧬📈🧫
  Disease progression modeling and prediction
  Alzheimer’s disease, stroke, tumors, and psychiatric 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 related areas, 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

---