VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
A Step-to-the-Doctorate Institute: Profile of a Bridge Program
Waller, Tremayne O.; Wright, Mandy J.; Talukdar, Shahidur Rashid; Jan, Faika T.; Anshebo, Surafel; Chan, Travis; Endlaw, Shriya; Santos, Niko (CEED, College of Engineering, Virginia Tech, 2026-09)
The transition from an undergraduate degree to a graduate program represents a critical juncture in the academic pathway, particularly for students pursuing advanced degrees in science, technology, engineering, and mathematics (STEM). This transition is often characterized by systemic barriers for underrepresented groups, who face challenges such as limited access to resources, a lack of mentorship, and financial constraints. To bridge this gap, A Step to the Doctorate Institute (S2D) was founded in 2020 by Dr. Tremayne Waller and Dr. Karis Cotton.
A Step-to-the-Doctorate (S2D)—an undergraduate-to-graduate bridge program—is a transdisciplinary initiative hosted by the Center for Engineering Excellence and Discovery (CEED) within the College of Engineering at Virginia Tech. S2D is an engineering-focused program designed to encourage, motivate, support, guide, and accelerate the progression of talented STEAM undergraduate students into master’s and doctoral programs. This report provides a detailed, evidence-based performance review of S2D from 2020 through 2026, examining cohort growth patterns, undergraduate pipeline diversity, and the graduate school transition successes of its alumni.
Key Programmatic Achievements:
S2D has achieved exceptional outcomes since its inception, establishing itself as a vital conduit for engineering graduate education. The program’s accomplishments are summarized in five core areas:
▪ Enrollment and Growth (2020–2026): The program’s annual enrollment scaled dramatically from 11 participants in 2020 to a peak of 61 participants in 2025, with a total of 223 scholars.
▪ Expanding the Scope: Initially, S2D started with only engineering major students. However, the program now has evolved into a multidisciplinary initiative with over 12% of participants from non-engineering fields such as life sciences, social sciences, and the humanities.
▪ Institutional Pipeline: S2D has recruited undergrads from 41 institutions. While Virginia Tech remains the primary pipeline (with 148 participants), the program maintains deep partnerships with historically black colleges and universities (HBCUs), minority serving institutions (MSI), and national student organizations, making the graduate pipeline more representative.
▪ Graduate School Conversion (2020–2025 Cohorts): Among the 167 participants who have completed the program, 78 (47%) have matriculated into graduate school. Of these, 54 (32%) are pursuing, or have already completed, Master’s degrees, and 24 (14%) have transitioned into PhD programs. Out of these 78 students, 41 (or 53%) have joined Virginia Tech graduate school. Notably, First-Generation participants achieved a 40% graduate school transition rate, outperforming non-first-generation peers (35%).
▪ Publications, Conferences, Research Grants: Systematic collection and analysis of data on the S2D participants and their achievements have resulted in four publications, several conference presentations, and award of five research grants to the S2D team.
CELLOS: Capturing and Elaborating Live-coding Lectures into Organized Summaries
Zheng, Yuhang (Virginia Tech, 2026-10-05)
Development of RAPID-AI, Physics-Based AI-Driven Algorithms for Pin-wise Burnup Dependent Fission Neutron Distribution and Ex-core Reactor Monitoring
Stroh, Brian (Virginia Tech, 2026-10-05)
This dissertation develops advanced neutronics formulations and associated algorithms for nuclear reactor core physics and monitoring. Reactor monitoring calculations require determination of the changing fission neutron source and the response of detector systems in real time. Whole-core fuel burnup calculations require repeated neutron transport and material updates because irradiation changes the material composition and fission neutron source distribution. This dissertation extends burnup-dependent Real-time Analysis for Particle Transport and In-situ Detection (RAPID) from its previous research-reactor application to a full-scale, pin-wise pressurized water reactor calculation. RAPID is based on Multistage Response Function Transport (MRT) methodology and uses the combined fission matrix formulation. This hybrid deterministic and Monte Carlo approach uses precalculated combined fission matrix (CFM) coefficients that vary with burnup. RAPID-AI is developed by optimizing the RAPID calculation and integrating neural-network predictions of material composition and CFM coefficients. The optimized calculation reduces computing time while maintaining agreement with the original RAPID results. A material model predicts the next pin-wise composition and the fission-source quantities needed for the reactor calculation. A CFM coefficient model predicts neutron production between fuel regions from the current composition and source. The models are trained using Serpent Monte Carlo calculations and evaluated through comparisons of the predicted quantities, whole-core eigenvalue, and fission neutron distribution. The integrated burnup calculation (bRAPID) uses both models to calculate pin-wise power, advance burnup, and update the material composition through successive reactor states. The RAPID-AI solution required 8.5 s on one core, compared to 7.6 days for the Serpent calculation on 40 cores. The material prediction model took 3.4 s to accurately predict the fission neutron source, atomic densities, and nuclide dependent energy recovered and ν values for 10 assembly types, compared to 10 hours for the Serpent burnup calculations. Using the RAPID-AI CFM neural network took 45 s to generate the full set of coefficients compared to 40 hours to generate the coefficients using Serpent fixed-source calculations. For the Watts Bar test benchmark, RAPID-AI calculations with predicted and Serpent-generated CFM coefficients gave a relative eigenvalue difference of −105.6 pcm and a fission neutron distribution mean relative difference of −0.04%. For ex-core monitoring, a combined Current Response Function and Detector Response Function methodology is developed enabling neutron transport from the reactor to ex-core neutron detectors beyond the confinement building. The method combines precalculated responses for the reactor structures, shielding, and detector. The methodology is demonstrated using a fresh-core neutron source and a computational model of the miniCHANDLER multi-modal neutron and anti-neutrino detection system. The calculated neutron-capture response is in excellent agreement with the direct Serpent calculation within its reported statistical uncertainty. The integration of these calculation algorithms, the neural network models, and optimization of RAPID-AI is pioneering work for the development of real-time reactor simulations incorporating changing core conditions.
The Authorship Requirement in the Age of Generative AI Music: AI in Copyright Law and the Transformation of the Creative Process in Commercial Music Production
Grehawick, Coleman Daniel (Virginia Tech, 2026-10-05)
This thesis examines the collision between generative artificial intelligence and copyright law in the context of commercial music production, arguing that existing doctrine is structurally unable to address the forms of creative displacement that AI systems produce. Copyright law's human authorship requirement, which was developed over two centuries without the need for questioning an author's humanity, is applied by courts and the U.S. Copyright Office almost exclusively to finished works, leaving the framework unable to examine the creative process through which a work was produced or to distinguish human expressive judgment from AI-generated statistical output. Drawing on Supreme Court fair use precedent (Campbell v. Acuff-Rose, 1994; Andy Warhol Foundation v. Goldsmith, 2023), the Copyright Office's guidance on AI copyrightability, Thaler v. Perlmutter (2025), and the 2024 litigation between major record labels and AI music platforms Suno and Udio, this study traces how doctrine, industry rhetoric, and platform governance have each independently failed to resolve the authorship question that generative AI poses.
The thesis introduces two original analytical contributions. First, it proposes creative initiation as a doctrinal standard regarding whether a human author established a work's expressive direction prior to and independently of AI involvement. Second, it identifies phantom sampling, a phenomenon in which generative systems replicate the market-relevant sonic and stylistic qualities of existing recordings through aggregate statistical patterning rather than identifiable copying, producing market displacement and loss of attribution that existing similarity doctrine has no mechanism to detect. The thesis concludes by proposing mandatory disclosure of AI involvement as a condition of distribution and licensing, and by offering practical guidance for musicians including process documentation, contractual negotiation, and collective advocacy to protect their authorship and attribution interests while formal legal reform remains slow to develop.
Agency Increased the Value of Social Signals in Adolescent Risky Decision-making
Wang, Shuhan (Virginia Tech, 2026-05-08)
Adolescence is characterized as the onset period for multiple risk-taking behaviors. The increased social susceptibility and autonomy during adolescence contribute to this elevated tendency to take risks. However, whether the autonomy to engage in social influence, i.e., the agency to acquire social signals increased their influence on the risky choices in adolescents remains unknown. In this study, we investigated the risky choices of 91 adolescents aged between 13-14 years under social influence. They acquired the social signals (i.e., the choices from two social others) either with or without agency before making risky choices. We found that participants were more likely to conform to others’ choices under agency. The Other-Conferred-Utility model revealed an increased value of social signals underlying this enhanced social influence. The functional neuroimaging analyses identified that the dorsomedial prefrontal cortex (dmPFC) encoded the social signals and the individual difference in their agency effect. Psycho-physiological interaction analyses revealed that the agency to acquire social signals increased dmPFC-vmPFC connectivity when participants conformed to others’ choices, implying agency mechanistically changed the social value integration. The agency effect on the value of signals modulated the association between participants’ risk-taking behaviors and the substance use in their peer groups, implying the possibility to predict the social sensitivity in real-life adolescent risk-taking behaviors.