L2S Talk by Leyla Kabuli (EECS, UC Berkeley): Designing imaging systems to maximise information capture

Thu 12 November 2026, 15.00 h at ZESS

Computational imaging systems combine optical hardware with algorithms to push the limits of image capture and interpretation. In these systems, measurements are designed for computational processing rather than human interpretation. As a result, the information available in measurements matters more than their visual interpretability.

In this talk, I will show how information theory provides powerful and practical tools for imaging system design. First, I will introduce a data-driven method for mutual information estimation, which evaluates imaging system performance directly from measurements without reconstruction or ground truth data. Using mutual information, I will establish design principles and optimise phase masks for lensless imaging, revealing that optimal designs are object-dependent and should be tailored to object sparsity. Finally, I will outline future directions for information-driven computational imaging, including black-box optimisation and applications in microscopy.

Leyla Kabuli is a final-year PhD Candidate in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley advised by Professor Laura Waller. She received a BS in EECS and a BA in Music from UC Berkeley in 2021, graduating as the 150th University Medalist. Her research is in computational imaging, at the intersection of information theory, machine learning, and optics. She is supported by the National Science Foundation Graduate Research Fellowship Program and the Berkeley Fellowship.