Research

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.

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.

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.

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