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Recent Submissions
“I appreciate birding more”: Exploring and reframing experiences of disabled birders using a strengths-based approach
McGregor, Freya A.; Dayer, Ashley A.; Sinkular, Emily N. (Elsevier, 2026-06)
While being disabled is often considered a deficit that creates only problems, in reality every human has interests, values, skills, knowledge, and external supports that can act as strengths when participating in recreation. Indeed, many disabled birders anecdotally report experiencing positives of birding with a disability, and the inherent modifiability of birding may allow disabled birders to experience different ways of engaging with this activity. We conducted a survey of disabled birders (n=147), then used inductive coding to analyze the responses to an open-ended question (n=106) about the positives of birding they experience. These responses included themes of slowing down, identifying more birds, noticing more details of birds, and adapting their style of birding to meet their access needs. We also used inductive coding to identify the various styles of birding respondents engaged with. This reconsidering of disability using a strengths-based approach creates opportunities for disabled birders and bird-related programming to focus on aspects of birding that may be more in line with disabled birders’ interests, experiences and strengths. It also creates a new avenue for nature organizations to engage with up to a quarter of the population who may feel excluded due to disability. This study contributes to the literature by exploring a rarely studied topic of disabled birders’ experiences using a strengths-based approach to studying disability in outdoor recreation.
Automorphisms of Hermitian Codes and Applications
Lichtenwalner, Monica M. (Virginia Tech, 2026-05-07)
Code automorphisms have long been studied due to their many applications. Decoding, erasure recovery, local decodability, and local correctability can all benefit from codes with rich automorphism groups. Rigid codes, or codes that have a trivial automorphism group, are also interesting, mainly for applications to code-based post-quantum cryptography. It has been shown that, with high probability, a random linear code has a trivial automorphism group, but it can be difficult to explicitly construct a code that is rigid. Automorphisms of one-point Hermitian codes were first studied by Xing in 1995. In this thesis, we determine the automorphisms of multi-point Hermitian codes that are induced by certain curve automorphisms. We also introduce the notion of curve-rigid codes, and we present a family of three-point Hermitian codes that are curve-rigid. Lastly, we demonstrate how we can use the automorphism group of a one-point Hermitian or norm-trace code to correct certain error patterns via permutation decoding, which is a decoding procedure developed by Prange in 1962 and extended by MacWilliams in 1964.
Elastoplastic Buckling of Functionally Graded Beams using Tamura-Tomota-Ozawa and Ramberg-Osgood Material Models
Sundaram Ezhilarasi, Ganesh Aravind (Virginia Tech, 2026-06-16)
Functionally Graded Materials (FGMs) are an advanced class of composite materials characterized by a gradual, continuous variation of material properties spatially. While buckling of slender Functionally Graded Beams (FGBs) can be analyzed using linear eigenvalue analysis and is well documented, the buckling response of FGBs with low and medium slenderness ratios is under-researched. This behavior is highly complex due to coupled shear effects and material yielding in the elastoplastic region. The goal of this work is to investigate the nonlinear elastoplastic buckling of short and medium metal-ceramic FGBs. The beam kinematics are modeled using the First-Order Shear Deformation Theory (Timoshenko beam theory) within a semi-analytical Ritz framework. To simulate the elastoplastic behavior of FGBs, a modified rule-of-mixtures law, based on the Tamura-Tomota-Ozawa model, is coupled with the Ramberg-Osgood phenomenological constitutive equations. The nonlinear stress-strain behavior of the metal component of the FGB is described using Hencky's total plastic strain model. Finally, an arc-length solver is employed to trace the FGB's nonlinear load-displacement path. Parametric studies are conducted by varying power-law coefficients and thickness ratios, and the results are compared with those from a 3D Finite Element Analysis (FEA) using Abaqus/Standard. The analytical model demonstrates excellent agreement with FEA for beams with a medium length-to-thickness ratio, with a maximum error of just about 4% for thickness ratios > 15. However, some discrepancies are observed when comparing very short FGBs with thickness ratios between 5 and 15. These discrepancies stem from the fundamental divergence between the deformation theory of plasticity employed in this formulation and the flow theory used in FEA models, which highlights the 'plastic buckling paradox', and from differences between 3D FEA and 1D First-Order Shear Deformation Theory, reinforcing the critical need for experimental validation.
Filtering and Domain Decomposition Techniques for Intrusive and Non-intrusive Reduced Order Models of Convection-Dominated Problems
Moore, Ian Robert (Virginia Tech, 2026-06-16)
Galerkin reduced order models (ROMs) offer significant computational savings for the simulation of partial differential equations, yet they often exhibit spurious oscillations and loss of physical fidelity in convection-dominated and multiscale regimes. This dissertation develops a framework for correcting the intrinsic spatial under-resolution of Galerkin ROMs through complementary regularization and hybridization strategies. Large eddy simulation-inspired filtering approaches are introduced, including the Ladyzhenskaya ROM and the approximate deconvolution Leray ROM (ADL-ROM), which incorporate spatial filtering to model the effects of truncated scales and enhance stability while maintaining efficiency. Rigorous numerical analysis is provided for both models, establishing stability and convergence results that strengthen their theoretical foundations. To address strongly localized high-gradient features, the OpInf-Schwarz ROM combines operator inference with a Schwarz domain decomposition framework, restricting full order modeling to localized regions while retaining ROM efficiency elsewhere. Finally, I apply spatial filtering concepts directly to OpInf to stabilize non-intrusive ROMs for convection-dominated systems.
Contributions to Machine Learning with Abstention and Surrogate Modeling with Complex Outputs
Zhang, Xinlei (Virginia Tech, 2026-06-16)
Machine learning and statistical modeling have become integral components of modern scientific and engineering analysis, providing powerful tools to model and understand complex phenomena. However, real-world applications frequently present unique challenges, such as data imbalance, fairness constraints, and high-dimensional structured outputs. This dissertation presents novel statistical methodologies to address these challenges, with a focus on classification problems under data imbalance and fairness constraints, efficient emulation of computer experiments with tensor-valued outputs, and simulation-based experimental design and analysis.
Chapter 1 presents the motivation and research objectives. Chapter 2 develops methods for logistic classification with a rejection option, addressing challenges of data imbalance and fairness constraints with respect to sensitive attributes. The proposed approach generalizes existing methods by incorporating convex surrogate loss functions and fairness-aware optimization. Performance is evaluated through comprehensive simulation studies and real-world datasets, including a case study on Methicillin-resistant Staphylococcus aureus (MRSA) prediction using imbalanced electronic health record (EHR) data.
Chapter 3 introduces a so-called Deco-GPT method, a Gaussian process-based emulator for computer experiments with tensor-valued outputs. The method applies tensor unfolding and higher-order singular value decomposition (HOSVD) to improve scalability and predictive accuracy. An extension for handling missing output values is incorporated via matrix completion. Performance is evaluated through simulations and case studies, demonstrating strong performance across diverse settings.
Chapter 4 introduces a simulation framework using CARLA to generate structured datasets, including safety-critical driving events and a route completion dataset with mixed inputs. We consider deep Gaussian process models for analyzing such data with mixed inputs. More specifically, we handle qualitative factors via indicator-based representation and learned embeddings, and compare against standard Gaussian process and latent variable Gaussian process models.
Chapter 5 summarizes the main contributions of this research and discusses potential directions for future work.