Azade Farshad

Azade Farshad

Assistant Professor, Aalto University ยท Principal Investigator, ELLIS Institute Finland

My group develops generative models and structured scene representations to understand, simulate, and augment the visual world โ€” from natural scenes to the operating room.

About

I am an Assistant Professor at the Department of Computer Science, Aalto University, and a Principal Investigator at the ELLIS Institute Finland. Before moving to Finland in 2025, I was a postdoctoral research associate at the Technical University of Munich (TUM), where I also completed my PhD summa cum laude in 2023 with a thesis on learning neural representations with limited data and supervision โ€” recognized with the BVM 2025 Best Dissertation Award.

My research develops deep generative models โ€” with a focus on diffusion models and structured representations such as scene graphs โ€” to understand, generate, and manipulate images and videos. A central goal of my group is to bring these methods into the operating room: simulating surgery, modeling surgical workflow, and synthesizing realistic medical data to build the next generation of clinical AI tools. I continue to co-advise doctoral researchers at TUM with Prof. Nassir Navab.

Beyond research, I serve the community as an Area Chair for MICCAI, an Executive Committee member of the British Machine Vision Association (BMVA), a Board Member of Women in MICCAI, and a Lead Member of Women in AI and Robotics. I have organized workshops at CVPR, ICCV, and ECCV, and I am committed to mentoring junior researchers and supporting women in AI. See my full CV โ†’

Research

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Generative Models for Medical Imaging

We build diffusion-based methods for synthesizing and manipulating medical images and videos โ€” counterfactual generation, physics-informed multimodal MRI synthesis, and anatomy-aware inpainting for clinically meaningful data augmentation.

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Surgical Data Science & Simulation

We simulate surgery with generative video models, surgical scene graphs, and workflow understanding โ€” towards training tools and intraoperative assistance for the operating room of the future.

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Scene Graphs & Semantic Scene Understanding

We study structured representations of visual scenes: scene-graph-guided image synthesis and manipulation, unconditional scene graph generation, and disentangled semantic editing.

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Learning with Limited Data

We make medical AI work where labels and data sharing are scarce โ€” meta-learning, few-shot and self-supervised segmentation, and federated learning for non-IID clinical data.

News

  • 2026 ๐ŸŽ“ We are recruiting! Open PhD and postdoc positions in generative models and medical AI.
  • 2026 ๐Ÿ“ฃ I am co-organizing the MELEX workshop (More Exploration, Less Exploitation) at ECCV 2026.
  • 2026 ๐Ÿ“„ Conformable Convolution, on topologically constrained learning of complex anatomical structures, was accepted at AAAI 2026.
  • Sep 2025 ๐Ÿ† MoViS won the Best Paper Award at the CREATE Workshop, MICCAI 2025.
  • 2025 ๐Ÿ“„ Two papers accepted at MICCAI 2025: HieraSurg, our hierarchy-aware diffusion model for surgical video generation, and DeepAF, a one-shot spatiospectral auto-focus model for digital pathology.
  • 2025 ๐Ÿ‡ซ๐Ÿ‡ฎ I joined Aalto University as an Assistant Professor and the ELLIS Institute Finland as a Principal Investigator.
  • Mar 2025 ๐Ÿ† My doctoral thesis received the BVM 2025 Best Dissertation Award.
  • 2025 ๐Ÿ“„ Latent Drifting, on counterfactual medical image synthesis with diffusion models, was accepted at CVPR 2025.
  • Oct 2024 ๐Ÿ† Two Best Paper Awards at MICCAI 2024 โ€” GRAIL Workshop (SANGRIA) and EARTH Workshop (VISAGE).

Selected Publications full list โ†’

* denotes equal contribution.