Inverse problems and reconstruction
Forward models, measurement design, and reconstruction methods for recovering images from noisy, incomplete, or irregularly sampled data.
Physics-based models; Sampling and optimization; Compressed sensing; Uncertainty quantification.
Remote sensing, radar, and sonar
Synthetic-aperture acquisition and reconstruction for remote observation, including sensor design and environmental measurements.
Synthetic-aperture radar and sonar; Radiometry and GNSS reflectometry; Climate and environmental monitoring.
Optical and wave imaging
Physical models of wave propagation and computational methods that connect optical measurements to reconstructed images.
Fourier ptychography; Holography and coded apertures; Time-of-flight imaging; Physics-based super-resolution.
Medical and astronomical imaging
Image formation and physical inverse problems in tomography, medical sensing, and astronomical observation.
MRI and ultrasound; X-ray and Doppler tomography; Radio astronomy and k-space reconstruction.
Microscopy and cryo-EM/ET
Computational microscopy and structural imaging through physical models of measurement and reconstruction from limited or noisy observations.
Synthetic-aperture microscopy; Cryo-electron microscopy; Cryo-electron tomography.
AI for imaging
Learning methods that use imaging physics or measurement structure to support acquisition, reconstruction, and analysis. This includes VLMs for reasoning about imaging data and agentic acquisition and reconstruction workflows.
Plug-and-play reconstruction; Diffusion priors; Physics-informed methods; Vision-language models (VLMs); Agentic imaging workflows.