[ MED / 3D ]
Medical image analysis
Brain-tumor segmentation across high- and low-quality MRI domains, combining strong 3D architectures with transfer learning and uncertainty-aware evaluation.
PhD Student and Researcher · Computer Vision
Ali Azmoudeh
I develop computer vision systems that remain reliable when data is limited, synthetic, or shifted—across facial analysis, medical imaging, and open-world visual understanding.
Multi-domain vision / 02Analysis active
FACEvisual analysis
BRAINmedical imaging
VISION AIdeep learning
01 / PROFILE
Engineering vision
for the real world.
I am a PhD student in Computer Engineering at Istanbul Technical University and a Graduate Research Assistant at SiMiT Lab, working under the supervision of Prof. Dr. Hazım Kemal Ekenel.
My research sits at the intersection of computer vision, deep learning, and trustworthy AI. I study how vision models behave beyond clean benchmarks: under domain shift, limited annotations, low-quality medical scans, privacy constraints, and imbalanced data.
Current directions include reliable brain-tumor MRI segmentation, conformal risk control, synthetic data for facial expression recognition, biometrics, and class-agnostic visual counting.
- Based
- Istanbul, Türkiye
- Affiliation
- ITU · SiMiT Lab
- Role
- PhD Researcher · RA/TA
- Advisor
- Prof. Dr. Hazım Kemal Ekenel
- Focus
- Computer Vision · Trustworthy Medical AI
02 / RESEARCH
Three connected
research directions.
From pixels to decisions, my work asks the same question: how can a model generalize responsibly when the real world does not match its training set?
[ FACE / SYN ]
Facial analysis & synthetic data
Privacy-aware facial expression recognition using pseudo-labeling, diffusion synthesis, GAN-based editing, and rigorous cross-dataset evaluation.
[ OPEN / COUNT ]
Generalizable visual understanding
Class-agnostic counting, biometrics, domain adaptation, and models that can reason about categories or conditions not seen during training.
03 / OUTPUT
Selected publications.
Peer-reviewed conference work and open research across medical vision, facial expression recognition, and visual counting.
P / 03
On Applicability of Synthetic Datasets for Facial Expression Recognition
IEEE FG 2026 · Kyoto, Japan · First author
Examines pseudo-labeling, diffusion-based synthesis, and GAN-based expression editing as privacy-preserving ways to address imbalance and limited facial-expression data.
P / 02
GLIMS-MedNeXt: An Ensemble Framework for Brain MRI Segmentation in Sub-Saharan Africa
MICCAI 2025 BraTS-Lighthouse · LNCS 16376 · Springer, 2026
Combines GLIMS and MedNeXt with transfer learning and ensemble fusion to improve brain-tumor segmentation under low-quality imaging conditions.
P / 01
A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches
arXiv · Computer Vision and Pattern Recognition · 2025
Introduces a taxonomy spanning reference-based, reference-less, and open-world text-guided counting, and reviews 29 approaches on established benchmarks.
04 / PATH
Research & education.
2025—
PhD, Computer Engineering
Istanbul Technical University · Computer vision and trustworthy medical AI
2024—
Turkcell Graduate Research & Teaching Assistant
Istanbul Technical University · Research and undergraduate teaching
2022—
Graduate Research Assistant
SiMiT Lab, ITU · Computer vision, biometrics, and medical imaging
2022–25
MSc, Computer Engineering
Istanbul Technical University
2016–21
BSc, Computer Engineering
University of Tabriz
Research collaboration
Let’s build vision systems that generalize.
For research discussions, collaborations, or questions about my published work, reach me through my ITU email.
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