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This thesis explores advanced manipulation and control of light’s structure, focus-
ing on the degrees of freedom such as phase, polarization, and coherence. The research
primarily addresses the generation, propagation, and application of structured optical
beams, with significant implications for imaging, communication, particle manipula-
tion, microscopy, and quantum state engineering.
A key area of investigation is the use of orbital angular momentum (OAM) in optical
beams. These beams, characterized by a conserved topological charge, have shown
promise in free-space optical communication due to their resilience against amplitude
and phase disturbances. The research highlights the development of partially coherent
beams that maintain deterministic vortices at specific propagation distances, achieved
through fractional Fourier transforms (FracFTs) applied to Schell-model vortex beams
in the source plane.
Another significant focus is on polarization singularities in fields with two harmonic
frequencies, i.e. Lissajous singularities. The study reveals stable Lissajous singulari-
ties within the beam core, offering new opportunities in high-precision metrology and
secure communication. Additionally, Young’s interference experiment with bichro-
matic vector beams is simulated creating Lissajous-type polarization singularities,
enhancing the fundamental understanding of the conditions under which Lissajous
singularities can be created in interference.
This work integrates these findings into a comprehensive framework for structured
coherence beams, advancing theoretical models and experimental techniques. The
resulting beams demonstrate unprecedented control over intensity, phase, coherence,
and polarization, paving the way for innovative applications in optical science and
engineering.
Binary-phase diffraction gratings are optical components that distribute an incident light beam to diffraction-order directions, due to the periodic modulation of the refractive index within the grating volume. Gratings are essential components in fields like acousto-optics, holography, spectroscopy, and are typically fabricated using lithography. High-efficiency first-order gratings are particularly important in spectroscopy, since first-order spectral diffraction spatially separates the incident wavelengths to be measured. Designing a linear grating consists of iterations of numerical simulations for a given phase profile, to determine grating diffraction efficiencies. The inverse design process, specifying the efficiencies desired and obtaining the phase profile, is very challenging and can lead to unstable solutions. In this research effort, a two-step design process is used. The general parameter ranges are determined first based on design goals, and then more rigorous simulations are performed to “map-out” the diffraction efficiency as a function of a limited solution parameter-space search. The phase profile that matches the design goals is then chosen.
The first lithographic step in grating fabrication is to create a mask for the grating’s features on a photoresist and develop the device profile. Etching the features into the substrate with a reactive-ion plasma process results in a permanent optical component. The study addresses certain fabrication challenges in binary grating fabrication, associated with areal scaling from a 25×25 mm2 surface area to a much larger 101×101 mm2 desired component size. The greatest challenge is to achieve the proper etch depth for the device function, which is mitigated by multiple masking and etching steps.
The Littrow-mount configuration is commonly employed to enhance a grating’s first-order efficiency performance, but can cause ghost images due to light recombination, a problem often controlled with antireflective treatments. Part of the research effort presented here uses random antireflective surface structures (rARSS), which are randomly distributed conical nano-features, etched into dielectric surfaces to minimize their Fresnel reflectivity. These structures were fabricated and tested on cylindrical lenses and freeform elements, showing significant transmission enhancement in the visible spectrum, minimal wide-angle scattering losses, and no notable wavefront distortion. rARSS were then applied to proof-of-concept (POC) reactive-ion plasma-etched (RIPLE) gratings for the VIRUS2 spectrograph, which was designed for Littrow-mount configuration. The rARSS-treated gratings successfully suppressed the undesirable reflection from the zeroth-diffraction order, enhanced the transmitted first-order, and reduced Littrow ghost intensities to four orders of magnitude lower than the transmitted spectrum baseline.
In parallel, a grating beam splitting device composed of two alternating crossed-cell tile first-order diffraction gratings, oriented orthogonal to each-other, was fabricated to function as both a two-way and three-way beam splitter at oblique light incidence. The tiling spatially separates the first-diffraction orders of each grating cell group,
while it overlaps the undeflected zeroth-diffraction order, creating three light-splitting pathways in orthogonal directions in three-dimensions. Each grating type was optimized to separate light in the 1st− and 0th−diffraction order, in ratios 96:1 and 2:1 for the two-way and three-way beam splitter respectively. The result was the projection of three equal intensity spots in space for the three-way beam splitter, and two spots off the axial direction for the two-way a beam splitter.
Learning continuous functions parameterized by neural networks has become a novel paradigm for representing complex, high-dimensional data, offering many benefits like shift-invariance and resolution-independent representations. However, these models struggle with data that is discontinuous, noisy, non-linear, and ill-posed, largely due to their inability to capture diverse data characteristics in a unified manner. To overcome these challenges, we introduce Probabilistic Generative Neural Priors, a Bayesian-inspired regularization framework that integrates probabilistic generative models—such as Energy-based Models (EBMs), Score-based Diffusion Models (SBMs), and Variational Autoencoders (VAEs)—with task-specific neural networks like Neural Fields (NFs) and classification models. Our framework leverages generative models as probabilistic priors to provide essential information during inference network training, facilitating faster and more accurate predictions by directly utilizing the prior's outputs. We validate our approach through extensive experiments on a diverse set of applications, including non-linear physics-based partial differential equation (PDE) inverse problems, linear image inverse problems, physics-based topology optimization, and time-series classification. Our results show significant improvements in accuracy metrics, convergence speed, generalization and regularization performance compared to existing methods, across all considered applications.
Busyness, or how busy someone is, has increasingly become a topic of conversation in day-to-day life. Research has previously explored how people use their time and how people perceive their available time, or lack thereof, but there is no clear answer as to why people tell others that they are busy and what it is they are trying to accomplish by doing so. Drawing on impression management research, this paper proposes that people signal to others that they are busy so that the audience has a positive impression of them. The concept of the busyness facade is introduced, which includes behaviors and verbal statements that are intentionally enacted by individuals to signal to others that they have a lot to do or limited available time. Exactly how and why people engage in this busyness facade is explored in two studies using semi-structured interviews and an online, vignette survey. Overall, evidence is found for the existence of busyness facades and a better understanding of how people display busyness is gained, but the studies are unable to identify a clear motive for why busyness facades would be used as an impression management tactic. Additional findings and research directions are discussed.
Freeform optics offer improved optical systems, but their complex shapes challenge traditional measurement methods. Cost-effective solutions are needed, especially for applications where expensive methods are impractical. Non-interferometric methods are a good alternative, but their accuracy can be limited. This dissertation aims to develop an accessible calibration method that improves the accuracy of these methods and enables the measurement of both refractive and reflective elements. The results are presented in three articles. The first article focuses on the calibration method and a new metrology approach that directly measures ray deflections, simplifying the process. The second article analyzes a new technique for converting wavefront data to height information and proposes a calibration process to improve accuracy. The third article tackles the issue of parasitic reflections by using a data-driven approach. This work significantly advances ray trace-based optical metrology and has numerous applications, particularly in the measurement and alignment of freeform optics.
Causal modeling provides us with powerful counterfactual reasoning and interventional mechanisms to generate predictions under various what-if scenarios. Nevertheless, uncovering causal relationships from observational data presents a considerable challenge, as unobserved confounders, limited sample sizes, and variations in distributions can give rise to misleading cause-effect associations. Models relying on these relationships may perform poorly when spurious correlations do not hold in test cases. To mitigate these challenges, researchers augment causal learning with known causal relations. This dissertation first investigates the incorporation of domain knowledge in structure learning by introducing additional constraints that convey qualitative knowledge about causal relationships. The experimental designs are specifically equipped to evaluate the role of domain knowledge. Secondly, a concept-driven approach is implemented to determine the advantages of incorporating concept-level prior knowledge. Given the invariant nature of causal relationships, the study then showcases the broader applicability of incorporating domain knowledge by employing a machine learning method for learning adsorption energies, illustrating the advantages of harnessing domain knowledge to obtain invariant molecular representations in catalyst screening. Finally, a novel approach is introduced to enhance robustness and out-of-distribution generalization by leveraging gradient agreement across different environments to identify reliable features. Collectively, these experimental designs advance causal discovery and robust machine learning by utilizing prior knowledge and relational invariances, paving the way for future research on integrating domain knowledge and invariance principles into the learning process.
Machine learning has risen to the forefront of scientific research due to its unparalleled predictive capabilities. As a result, researchers have become increasingly interested in uncovering the underlying causal structures that govern the relationships between variables in a system. These causal structures, often represented as directed acyclic graphs (DAGs), provide insights into how changes in one variable may directly or indirectly affect other variables, enabling a deeper understanding of the complex interactions within the system. While it is essential to constrain a model by minimizing spurious correlations and conducting "What-If" analyses, learning causal relationships from observational data, known as causal discovery, remains an active and challenging research area. This is due to factors like finite sampling, unobserved confounding factors, and measurement errors. Current approaches, including constraint-based and score-based methods, often struggle with high computational complexity because of the combinatorial nature of estimating DAGs. Inspired by the workshop on the Causality Challenge 'Cause-Effect Pair' at the Neural Information Processing Systems in 2013, this dissertation adopts a novel approach, generating a probability distribution over all possible graphs based on cause-effect pair features proposed in response to the workshop challenge.
The primary goal of this study is to develop new methods that leverage this probabilistic information and assess their performance. Furthermore, this work introduces a novel causal feature selection (CFS) algorithm using this approach and the establishment of a new evaluation criterion for CFS. To further enhance experimental performance, this dissertation proposes the use of a Graph Neural Networks (GNNs)--based probabilistic predictive framework for causal discovery. Conventional causal discovery algorithms face significant challenges in dealing with large-scale observational datasets and capturing global structural information. The GNN-based approach addresses these limitations, enabling the learning of complex causal structures directly from data augmented with statistical and information-theoretic measures. The proposed framework represents a significant leap forward in causal discovery, offering improved accuracy and scalability in both synthetic and real-world datasets, as well as introducing a novel synergy between probabilistic learning and causal graph analysis.
In addition to the methodological advancements, this dissertation includes an application of counterfactual analysis to study affective polarization on social media. By comparing scenarios with and without specific influencer-led conversations on platforms like Twitter, I analyze the impact of these conversations on public sentiment. This application highlights the practical implications of the proposed causal modeling techniques, demonstrating their utility in understanding real-world issues and contributing to the broader field of social media analysis.
Electrical fires caused by arc faults necessitate advanced detection methods for improved safety and reliability in AC and DC power systems. This research introduces innovative artificial intelligence (AI)-based methods for the efficient detection of arc faults, enhancing both safety and reliability in electrical systems. For AC systems, we developed a convolutional neural network (CNN)-based arc fault detection algorithm that autonomously extracts arc fault features without manual thresholding. Using raw current as input, the algorithm achieves an arc fault detection accuracy of 99.47%. Additionally, this research determined an optimal sampling rate of 10 kHz for the input current. The model's efficacy was verified using the Raspberry Pi 3B platform.
While traditional CNN algorithms have high accuracy, they require optimization for real-time arc fault detection on resource-limited hardware. To address this, we proposed a lightweight CNN architecture combined with a model compression technique using a knowledge distillation-based teacher-student algorithm. This model maintains a high detection accuracy of 99.31% and operates with an impressively minimal runtime of 0.20 milliseconds per sample when implemented on the Raspberry Pi 3B platform. This performance demonstrates its suitability for commercial embedded microcontrollers (MCUs) with limited computational capability.
Extending our research to DC systems, we introduced a cost-effective, AI-driven Arc Fault Circuit Interrupter (AFCI) for DC applications. Utilizing an STM32 MCU and a silicon carbide (SiC) MOSFET-based solid-state circuit breaker (SSCB), the proposed method achieves a detection accuracy of 98.15% with a remarkable arc fault interruption time of 25 milliseconds. This AFCI solution stands out for its rapid response and high reliability, promising significant improvements in safety for DC systems.
Together, these contributions signify a leap forward in electrical safety, presenting viable solutions for the timely detection and interruption of arc faults in AC and DC systems. The outcomes of this research are expected to influence future standards and practices in electrical safety management across residential, commercial, and industrial sectors.
Social media has emerged as a common platform for knowledge sharing and exchange in online communities. However, it has also become a hotbed for the diffusion of irregular content. Content moderation is crucial for maintaining a safe and healthy online environment by regulating the distribution of user-generated content (UGC).
Engaging users in content moderation fosters a sense of shared responsibility and empowers them to actively shape the environment of online communities. Leveraging the expertise of moderators leads to a deeper contextual understanding of content, thereby improving the overall consistency and legitimacy of content moderation in compliance with community or platform guidelines. Nevertheless, the collaborative effort of a more inclusive and community-driven moderation process remains unexplored by previous studies. While there is increasing attention to fairness, transparency, and ethics in content moderation, prior research often assesses content moderation perceptions of users, platforms, moderators, and bystanders in isolation. This results in a lack of comprehensive understanding of user perceptions in content moderation decision-making.
To address these limitations, this research proposes UMCollab, a user-moderator collaborative content moderation framework that incorporates the dynamics of user engagement and the domain knowledge of moderators into deep learning models to facilitate content moderation decision-making. Additionally, this research empirically investigates user perceptions of content moderation from the perspectives of content familiarity, content diversity, and user roles.
UMCollab leverages graph learning to model user engagement, which is further enhanced by the credibility and stance of users' online discussions. It also employs attention mechanisms to learn the domain knowledge of moderators based on their decisions regarding UGC per online community rules. Moreover, this study conducts an online user study by asking participants with diverse online engagement backgrounds and roles to complete a series of content moderation decision-making tasks and evaluate their perceptions of content moderation.
The findings of this dissertation research hold significant promise for promoting effectiveness, fairness, transparency, and community ownership in moderating UGC in social media, offering opportunities to improve the safety and success of online communities.
The advancement of medical imaging has significantly enhanced the ability to diagnose, monitor, and treat cancer. This dissertation focuses on the development of deep learning methodologies for the segmentation and registration of medical images, specifically Positron Emission Tomography (PET), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and pathology images, to improve the accuracy and efficiency of cancer diagnosis and treatment planning.
Segmentation, the process of delineating anatomical structures and pathological regions, is a crucial step in medical image analysis. This work introduces novel high-precision deep learning models for the automatic segmentation of tumors and organs at risk (OARs). These models utilize convolutional neural networks (CNNs) and transformer-based architectures to handle the complexities and variations inherent in PET, CT, and MRI. The segmentation models are trained on multi-modal imaging datasets, incorporating advanced techniques such as data augmentation, transfer learning, and ensemble learning to enhance robustness and generalization. Evaluation on various datasets demonstrates that these models achieve superior performance compared to traditional methods, with significant improvements in accuracy and reliability.
Registration, which aligns images from different modalities or time points, is another critical component in the analysis of medical images. This dissertation presents advanced deep learning approaches for the registration of CT, MRI, and pathology images, leveraging deep neural networks (DNNs) and unsupervised learning techniques. The proposed registration methods employ spatial transformer networks (STNs) and other novel architectures to learn complex spatial transformations directly from the data, enabling accurate alignment of multi-modal images. These approaches are designed to be computationally efficient and scalable, facilitating their integration into clinical workflows.
Our final goal is to streamline these deep learning methods to real clinical applications. This dissertation explores the practical applications of the developed models, including their deployment in microservices for common radiotherapy imaging tasks. The models are made accessible via Python scripts for clinical treatment planning software such as RayStation, allowing seamless integration into existing clinical systems. Evaluation using images and treatment planning data for prostate cancer underscores the potential of these models to enhance the quality of treatment planning and streamline the overall process of planning, response assessment, and adaptation. Additionally, this dissertation investigates the potential of federated learning for collaborative model training across multiple institutions without sharing sensitive patient data. This approach could enhance model robustness and generalizability by leveraging diverse datasets from various sources.
In conclusion, this dissertation explores the critical component of medical imaging for cancer diagnosis, monitoring, and treatment with advanced deep learning methods. We hope these innovative techniques developed in this research pave the way for more precise, efficient, and individualized patient care in oncology.
