The core idea of computational imaging is to supplement limited observable data\r\nwith human-imposed assumptions, or priors. One could formulate a prior as a statistical model or physics model of the object being imaged. However, incorporating such assumptions in the imaging process poses computational challenges, including efficiently expressing sophisticated priors, appropriately balancing priors with observations, and gently enforcing physics constraints. This thesis addresses such challenges with principled methods for bringing informative assumptions into computational imaging. We emphasize applications in scientific imaging, and we focus on two categories of priors as well as the intersection between them: data-driven statistics and physics knowledge.
\r\n\r\nOn the data-driven side, this thesis presents work on score-based priors, including a posterior-estimation method and results of re-imagining the famous M87* black hole from real data with score-based priors. On the physics-based side, we have been able to tackle extremely under-determined imaging problems by enforcing physics constraints, including performing single-viewpoint dynamic tomography of emission near a black hole and characterizing interior material properties from video. As a means towards integrating data-driven and physics-based assumptions, we have developed a method to enforce physics constraints on generative models. In this thesis, we explore each aforementioned project with an emphasis on technical novelty and experimental validation on simulated and real data. By opening new routes for bringing in both data-driven and physics-based assumptions, the methods presented in this thesis enable visualizing scientific phenomena beyond the reach of conventional sensors.
", "doi": "10.7907/cmed-tj81", "publication_date": "2026", "thesis_type": "phd", "thesis_year": "2026" }, { "id": "thesis:17671", "collection": "thesis", "collection_id": "17671", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:09062025-050711919", "primary_object_url": { "basename": "Thesis.pdf", "content": "final", "filesize": 113472034, "license": "other", "mime_type": "application/pdf", "url": "/17671/2/Thesis.pdf", "version": "v6.0.0" }, "type": "thesis", "title": "Learning to Sample in Computational Imaging: Measurement Acquisition and Posterior Estimation", "author": [ { "family_name": "Wu", "given_name": "Zihui", "orcid": "0000-0002-7622-3548", "clpid": "Wu-Zihui" } ], "thesis_advisor": [ { "family_name": "Bouman", "given_name": "Katherine L.", "orcid": "0000-0003-0077-4367", "clpid": "Bouman-K-L" } ], "thesis_committee": [ { "family_name": "Yue", "given_name": "Yisong", "orcid": "0000-0001-9127-1989", "clpid": "Yue-Yisong" }, { "family_name": "Bouman", "given_name": "Katherine L.", "orcid": "0000-0003-0077-4367", "clpid": "Bouman-K-L" }, { "family_name": "Perona", "given_name": "Pietro", "orcid": "0000-0002-7583-5809", "clpid": "Perona-P" }, { "family_name": "Song", "given_name": "Yang", "orcid": "0000-0003-3193-1679", "clpid": "Song-Yang" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Many problems in science and engineering require visualizing objects that are not directly observable\u2014such as black holes that are millions of light-years away from the Earth or internal anatomical structures hidden within the human body. Computational imaging is a powerful paradigm that combines sensor design with advanced computational algorithms to make the invisible visible. The typical computational imaging pipeline involves first collecting indirect measurements of the target object and then solving a reconstruction problem. This thesis focuses on two core challenges about sampling along this pipeline: (1) optimizing the sampling process for measurement acquisition, and (2) sampling the posterior distribution of possible reconstructions given noisy measurements.
\r\n\r\nThe first part of the thesis investigates how to design adaptive and task-specific acquisition strategies for computational imaging systems, with a focus on compressed sensing magnetic resonance imaging (CS-MRI). We propose a sequential sampling method that learns to select measurements in multiple stages, and an approach that tailors sampling patterns for specific downstream tasks such as region-of-interest reconstruction, segmentation, and classification. These methods enable a better selection of measurements taken during acquisition, leading to improved performance compared to conventional baselines. We have also implemented our learned sequences on a real MRI scanner and verified their improvement in practice.
\r\n\r\nThe second part of the thesis develops a principled framework for posterior sampling using diffusion models (DMs)\u2014a state-of-the-art class of generative models. By revealing a key connection between DMs and the Split Gibbs Sampling, we introduce a posterior sampling method that rigorously incorporates pre-trained DMs as image priors for solving inverse problems, which exhibits strong performance on a variety of applications. We then show that this framework can be naturally extended into a series of instantiations for solving more general inverse problems, addressing topics like text conditioning, video inverse problems, non-differentiable forward models, and discrete-space sampling. We also present a comprehensive benchmark for systematically evaluating state-of-the-art DM-based posterior estimation methods.
\r\n\r\nBy leveraging machine learning to address challenges in both data acquisition and posterior estimation, this thesis provides new possibilities for building more intelligent and reliable imaging systems across science and engineering.
", "doi": "10.7907/j42z-s192", "publication_date": "2026", "thesis_type": "phd", "thesis_year": "2026" }, { "id": "thesis:18608", "collection": "thesis", "collection_id": "18608", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05222026-052420973", "primary_object_url": { "basename": "AFG_Thesis_Final.pdf", "content": "final", "filesize": 20530014, "license": "other", "mime_type": "application/pdf", "url": "/18608/1/AFG_Thesis_Final.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Uncovering Hidden Structure in Data and the Physical World for Seismic Tomography and Beyond", "author": [ { "family_name": "Gao", "given_name": "Angela Fang", "orcid": "0000-0001-8574-8728", "clpid": "Gao-Angela-Fang" } ], "thesis_advisor": [ { "family_name": "Bouman", "given_name": "Katherine L.", "orcid": "0000-0003-0077-4367", "clpid": "Bouman-K-L" } ], "thesis_committee": [ { "family_name": "Yue", "given_name": "Yisong", "orcid": "0000-0001-9127-1989", "clpid": "Yue-Yisong" }, { "family_name": "Ross", "given_name": "Zachary E.", "orcid": "0000-0002-6343-8400", "clpid": "Ross-Z-E" }, { "family_name": "Tropp", "given_name": "Joel A.", "orcid": "0000-0003-1024-1791", "clpid": "Tropp-J-A" }, { "family_name": "Bouman", "given_name": "Katherine L.", "orcid": "0000-0003-0077-4367", "clpid": "Bouman-K-L" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Reliable and calibrated imaging strategies are essential for tackling challenging real-world imaging problems. Developing these strategies involves interdisciplinary approaches that translate inverse problem tooling into avenues for real scientific insight. However, raw scientific measurements present many challenges that modern imaging techniques do not adequately address. For instance, many state-of-the-art imaging methods struggle with seismic imaging problems due to several factors including non-linear forward models, uncalibrated noise, sensitivity to initial conditions, and limited angular observations. This thesis aims to overcome these challenges by presenting methods that image under uncertainty, leverage unconventional sources of regularization, and reshape the way we solve imaging inverse problems, ultimately enabling new scientific discoveries.", "doi": "10.7907/vcsd-dn14", "publication_date": "2026", "thesis_type": "phd", "thesis_year": "2026" }, { "id": "thesis:16159", "collection": "thesis", "collection_id": "16159", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:08172023-032920075", "primary_object_url": { "basename": "Nitika_Yadlapalli_2024___Thesis.pdf", "content": "final", "filesize": 17656069, "license": "other", "mime_type": "application/pdf", "url": "/16159/4/Nitika_Yadlapalli_2024___Thesis.pdf", "version": "v7.0.0" }, "type": "thesis", "title": "Interferometric Millimeter Observations of the High Energy Universe", "author": [ { "family_name": "Yadlapalli Yurk", "given_name": "Nitika", "orcid": "0000-0003-3255-4617", "clpid": "Yadlapalli-Yurk-Nitika" } ], "thesis_advisor": [ { "family_name": "Ravi", "given_name": "Vikram", "orcid": "0000-0002-7252-5485", "clpid": "Ravi-Vikram" }, { "family_name": "Bouman", "given_name": "Katherine L.", "orcid": "0000-0003-0077-4367", "clpid": "Bouman-K-L" } ], "thesis_committee": [ { "family_name": "Zmuidzinas", "given_name": "Jonas", "orcid": "0000-0002-3330-5439", "clpid": "Zmuidzinas-J" }, { "family_name": "Hallinan", "given_name": "Gregg W.", "orcid": "0000-0002-7083-4049", "clpid": "Hallinan-G-W" }, { "family_name": "Phinney", "given_name": "E. Sterl", "orcid": "0000-0002-9656-4032", "clpid": "Phinney-E-S" }, { "family_name": "Ravi", "given_name": "Vikram", "orcid": "0000-0002-7252-5485", "clpid": "Ravi-Vikram" }, { "family_name": "Bouman", "given_name": "Katherine L.", "orcid": "0000-0003-0077-4367", "clpid": "Bouman-K-L" } ], "local_group": [ { "literal": "Owens Valley Radio Observatory (OVRO)" }, { "literal": "Astronomy Department" }, { "literal": "div_pma" } ], "abstract": "This thesis explores what can be accomplished in the ways of time-domain astrophysics with a variety of scales of millimeter interferometry. I touch upon techniques in instrumentation, theory, observation, and computation, showcasing the breadth and richness of the field.
\r\n\r\nThe transient millimeter sky is largely comprised of synchrotron sources whose physical properties are just beginning to be revealed. We are entering an age where new wide-field surveys will exponentially increase the number of known transients, including the first wide-field millimeter survey capable of significant transient detections. As we approach this era, resources dedicated to monitoring and follow-up become increasingly more important.
\r\n\r\nA significant part of my work involves design and commissioning for a new single baseline millimeter interferometer at the Owens Valley Radio Observatory called SPRITE. Uniquely positioned as a dedicated transient follow-up telescope, SPRITE has the ability to observe nearby transients with a relatively high cadence. In this thesis, I also highlight two specific classes of sources for which millimeter observations may be particularly interesting. I present predictions for millimeter emission from supernovae interacting with dense circumstellar media and discuss their rates of detection in upcoming surveys. I additionally present lower frequency spatially-resolved radio observations of an X-ray binary in an active state.
\r\n\r\nOn the other extreme, this thesis also explores the use of very long baseline interferometry to investigate how high resolution images of supermassive black holes vary over the timescale of a year. In 2017, the Event Horizon Telescope Collaboration (EHTC) observed the supermassive black hole in nearby galaxy M87, producing the first resolved image of the shadow of a black hole and potentially revealing intra-day variability of the observed synchrotron emission around the shadow. I present work on the imaging and preliminary analysis of the 2018 epoch of EHT observations of the black hole in M87, and discuss the EHTC\u2019s conclusions of intra-day and year-long variations in the images.
", "doi": "10.7907/g8fs-5q44", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" } ]