Rice University Research Repository
The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.

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Recent Submissions
Parent University Evaluation
(Rice University Kinder Institute for Urban Research, 2025) Molina, Mauricio; Pham, Annie; Bonner, Hannah; Stroub, Kori
Parental involvement in children's education significantly improves attendance, behavior, grades, and social skills, all of which are crucial for long-term success. To increase parental engagement and advocacy from parents, the Houston Independent School District (HISD) established the Parent University program in the 2018-19 school year. While the program has continuously evolved since its inception, the overall goal has remained the same: to connect parents with district-provided resources and information through a series of courses on topics that include parents’ dreams and aspirations for their children, the inner workings of HISD, challenges to equity and quality education, parental advocacy and volunteerism, and the pathway to college. Given this wide range of content and the resource-intensive nature of the intervention, the Kinder Institute for Urban Research’s Houston Education Research Consortium (HERC) has partnered with HISD to better understand which elements of Parent University have been the most effective in promoting parental engagement and advocacy. Information from this study will be used by program administrators to streamline and improve the content provided to future Parent University cohorts.
Collaborative Settler Colonialism: Japanese Migration to Brazil in the Age of Empires
(University of California Press, 2025) Lu, Sidney Xu; Transnational Asian Studies
Seeing without revealing: Privacy-Aware Computational Cameras and Decentralized Learning Frameworks
(2025-02-17) Tasneem, Zaid; Veeraraghavan, Ashok
Integrating cameras and vision algorithms into our daily lives has led to the development of a wide range of new applications but also has raised significant privacy concerns. This thesis reimagines these applications in a privacy-aware fashion, enabling optimal privacy-utility trade-offs. The solutions it explores leverage the design degrees of freedom offered by three domains: optics, electronics, and digital computing.
In the initial segment, the thesis delves into the utilization of optical computing to obstruct facial identity while facilitating downstream applications such as depth estimation, human pose estimation, person detection, and activity recognition. This optical computing is enabled by either a single-layer diffractive optical element or a metasurface whose parameters are optimized in an end-to-end learning pipeline using adversarial optimization. The results are computational cameras that achieve optimal privacy-utility trade-offs and are validated using proof-of-concept hardware.
The subsequent part of the thesis examines the application of an analog electronic chip designed to execute a shallow Convolutional Neural Network (CNN) for per-pixel analysis. This electronic NN is trained to output only pixels with non-private information, avoiding imaging faces. This method underscores the potential of electronic analog computing in enhancing privacy in vision systems.
Finally, the thesis presents a decentralized digital solution that facilitates the collaborative creation of global crowd-sourced Neural Radiance Fields (NeRFs). This involves the introduction of a novel federation scheme and a secure multi-party computation protocol, ensuring high-quality 3D reconstruction for immersive viewing without compromising the users' privacy.
Towards Efficient Knowledge Graph Generation From Textbooks: A Dual Framework Approach
(2025-02-05) Hatchett, Johaun; Baraniuk, Richard G
The recent proliferation of LLMs necessitates a strategy for addressing these models' deleterious shortcomings: hallucination, and lack of explainability. Knowledge graphs (KGs) have gained attention as a potential solution to these problems, as they can serve as a traceable, factual database for LLMs; however, constructing high-quality KGs efficiently remains a challenge.
To address these challenges, this thesis proposes Words2Wisdom, a logic-informed, LLM-based framework for generating quality KGs from textbooks. Words2Wisdom creates expressive KGs by leveraging the structure of propositional logic, and ensures accurate fact representation, demonstrating knowledge validity (precision) greater than 95% when using the GPT-4o model in a few-shot environment. Our results suggest targeted fine-tuning and model specialization can further enhance KG quality.
Furthermore, this thesis examines whether LLMs are able to assess the quality of KGs. We introduce Libra, a framework establishing a novel KG evaluation protocol for validating KGs against textbook sources. Preliminary results show high observed agreement between Libra and human experts, suggesting that KG construction and evaluation and can indeed be effectively automated, paving the way for future research on the role of LLMs in hallucination mitigation.
Electronic Transport on Aligned Carbon Nanotube Assemblies
(2025-02-14) Yu, Shengjie; Kono, Junichiro
Individual carbon nanotubes (CNTs) offer high electrical conductivity, tensile strength, and flexibility, but these properties are diminished in randomly oriented structures. Aligned CNT fibers retain good conductivity (up to 10.9 MS/m) but are still inferior to individual CNTs. We have investigated electronic transport phenomena in these fibers through temperature- and magnetic field-dependent measurements, finding that the conductivity decreases with decreasing temperature at low temperatures due to quantum conductance corrections. Using a combination of 3D and 1D weak localization (WL) models, we explained the observed magnetoresistance and discuss their dimensionality in detail.
Low-temperature studies on individual CNT bundles showed significant quantum corrections, with WL and universal conductance fluctuations (UCF) providing consistent phase coherence length estimates (tens of nanometers). However, UCF amplitude and magnetic field asymmetry suggest a coherence length scale similar to the few-micron distance between the voltage probes.
This study enhances the understanding of electronic transport mechanisms in aligned CNT fibers, essential for improving conductivity for various applications.