Teaching

Role: Instructor
Institution: University of Rochester
Semester: Spring 2024

I designed and taught this graduate-level course, with support from my advisor, Prof. Mujdat Cetin. This course focuses on both mathematical and machine learning-based methods for computational imaging.

Course Topics Included:

• Fundamentals of observation models
• Analytical and algebraic reconstruction methods
• Ill-posed inverse problems, variational modeling
• Tikhonov regularization
• Sparsity-promoting regularization, compressed sensing
• Regularization parameter selection problem
• Dictionary learning
• Plug-and-Play Priors, Regularization by Denoising (RED)
• Deep unrolling
• Deep generative model-based Bayesian inversion methods
• Bayesian neural network-based image reconstruction methods

Evaluation

Students were evaluated through 8 homework assignments and a comprehensive final project.

Materials

• Syllabus (PDF)
• Recordings and course materials are available upon request.
• Course website hosted via Blackboard (access restricted)