5 months research project on Tubular Structures Enhancements
I am a Ph.D. Candidate in AI and computer vision specializing in medical imaging with a background in software development, my background involves areas like deep learning, loss function regularization, few-shot learning methods, and model analysis.
Current research focuses include developing novel methods for bifurcation detection, designing topology-preserving loss functions tailored for analyzing tubular structures in medical images.
March 2024: I got accepted to CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School, Co-hosted by CIFAR and the Vector Institute in Toronto, Ontario, Canada.
Feb 2024: My work on few-shot airway-tree modeling got accepted to 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece.
July 2023: I Received BME Conference Fellowship from Engineering for Health Interdisciplinary Center (E4H) of IP-Paris.
July 2023: I received Best Poster Award from Engineering for Health Annual Forum.
Modeling, Segmentation, and Detection in 3D and few-shot learning setups with a focus on federated learning.
Leveraging AI in radiology and medical image analysis, my focus centers on tubular structures and associated diseases.
Teaching programming, machine and deep learning, computer vision, medical imaging basics, and etc.
Providing consultations bridging the gap from AI research to practical applications across industries.
5 months research project on Tubular Structures Enhancements
5 months research internship on strategic machine learning model maintenance in CEDAR (Inria Saclay) team.
Member of the mobile development team for developing, deploying, and maintaining new Android Applications.
I worked on the complete software development pipeline from defining requirements to testing and deployment.