1.1 Demonstrate a knowledge of the multidimensional signal theory and understand its role in the design, analysis and development of various imaging modalities

1.2 Gain essential knowledge of wave propagation concepts, antenna systems, and ambiguity function from a signal processing perspective

1.3 Develop a thorough understanding of the inverse problem framework and understand the challenges involved in an image formation problem

1.4 Integrate and use the analytical tools from linear algebra, estimation, and optimization theory to formulate real-world image reconstruction problems

2.1 Develop a knowledge of the computational imaging framework and understand its role in the development of modern imaging technologies

2.2 Integrate and use the numerical tools from linear algebra, estimation, and optimization theory to develop image reconstruction algorithms

2.3 Solve the image reconstruction problems faced in different imaging settings (such as in synthetic aperture radar, optical, biomedical and astronomical imaging, spectral imaging, etc.)

3.1 Become familiar with the working principle of different types of computational imaging systems involving optics, radar, diffraction, or tomography

3.2 Demonstrate a knowledge of common image reconstruction algorithms used in different imaging modalities (such as for problems of image deconvolution, phase retrieval, compressed sensing, and tomography)

3.3 Gain technical and scientific competence in the interdisciplinary field of imaging (which lies at the intersection of applied mathematics, physics, signal processing, estimation theory, optimization, computer algorithms, as well as application domains in remote sensing)

3.4 Take effective roles in the development of emerging imaging modalities