Research
Developing robust uncertainty-aware machine learning methods for medical imaging applications
Research Interests
Machine Learning
Deep learning, ensemble methods, uncertainty quantification
Probabilistic Modeling and Forecasting
Bayesian approaches, statistical modeling
Uncertainty Estimation
Model uncertainty, aleatoric and epistemic uncertainty
Generative Models
Focusing on Diffusion Models and Flow Matching techniques
Medical Imaging
Echocardiography, ultrasound imaging, cardiac analysis
Applied Mathematics
Optimization, signal processing, matrix analysis
Current Research
PhD Research (2021-Present)
University of British Columbia, Robotics and Control Laboratory
My current research focuses on developing robust uncertainty-aware machine learning methods for medical imaging applications, particularly in echocardiography and cardiac function assessment. I work on creating AI systems that can reliably quantify their own confidence, making them suitable for deployment in clinical environments.
Master's Research (2018-2021)
Thesis: "Towards a robust estimation of ejection fraction: a deep uncertainty aware approach"
Developed novel deep learning frameworks incorporating Bayesian uncertainty estimation for more reliable cardiac function assessment from echocardiographic videos. This work laid the foundation for my current PhD research.
Selected Projects
Asymmetric Observer Variability in Echocardiography Quality Assessment
Bayesian Decision Theoretic False Negative Reduction with Skewed Posteriors - addressing the challenge of asymmetric costs in medical diagnosis.
Unsupervised Topologically Deformable Probabilistic Registration of Echocardiograms
Novel approach to cardiac image registration using topological constraints and probabilistic methods for better image alignment.
Medical Image Registration Survey
Comprehensive review of medical image registration methods, from classical mathematical approaches to modern machine learning techniques.
Internship Experience
Summer 2017
Medical Image and Signal Processing Research Center, Isfahan, Iran
Project: "Automatic Diabetic Retinopathy Detection and Fundus Photograph Segmentation using Deep Learning and Dictionary Learning"
Developed automated systems for early detection of diabetic retinopathy from retinal images, combining deep learning and traditional computer vision techniques for improved diagnostic accuracy.