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.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Learning to Collaborate: Toward Robust, Adaptive Policies for Human–Robot Teams
Sagheb, Shahabedin (Virginia Tech, 2026-05-27)
Robotics, automation, and the use of Machine Learning (ML) algorithms have been steadily making progress. They have been adopted in various sectors, including manufacturing, education, healthcare, and transportation. Although intelligent algorithms behind text-based, image-based, and commerce platforms are very prominent and often cited as examples of progress, there exists a gap in applying these algorithms to robotics applications with humans in the loop. In addition to human acceptance, robotic systems require safety-critical interfaces with humans (e.g., self-driving technology and robotic-assisted living). There is also a need for robot-specific datasets to train these algorithms. Providing efficient ways to train algorithms and building intuitive and safe interfaces with humans can lead to increased adoption and trust between end-users and robotic systems. The paradigm of Human--Robot Collaboration (collaborative autonomy) has been one of the most promising approaches to gathering data from human users and incrementally building trust between the human and the machine. However, humans are not static agents. Algorithms working with humans must consider the dynamic nature of their interactions with human users. This creates an exciting and challenging opportunity to develop algorithms that learn from humans and adapt to the requirements of an evolving task.
In this dissertation, we investigate how robots can be trained efficiently and robustly given the dynamic nature of humans. Concretely, we explore three key objectives: (1) developing algorithms that learn efficiently from limited human demonstration datasets, (2) developing decision-making policies for long-term interaction, and (3) developing robot policies that communicate the learning to humans. This research leverages existing methods and builds on them to present novel approaches for learning from, communicating with, and adapting to human users. Our results are agnostic to the application domain (e.g., healthcare or driving) and to the type of robot (e.g., robot arm vs. autonomous car).
Our main contributions are: (1) a learning algorithm for efficiently learning from human teachers, (2) a foundational optimization framework for influencing human partners over long-term interactions, and (3) a game-theoretic approach to communicating robot learning to human partners. We provide algorithms and experimental results from evaluations in simulated and real environments that demonstrate the effectiveness of our proposed approaches.
The Cost of Custody: Security Tradeoffs and Fee Dynamics in Bitcoin Covenant Vaults
Gunasekaran, Praneeth (Virginia Tech, 2026-05-27)
Four Bitcoin Script extension proposals—OP_CTV (BIP-119), OP_VAULT (BIP-345), OP_CCV (BIP-443), and the OP_CAT + OP_CHECKSIGFROMSTACK composition (BIP-347 + BIP-348)— enable self-custody vaults that enforce spending conditions beyond simple signature checks, but no prior work compares them empirically. We build all four vault implementations, run 15 experiments on Bitcoin regtest, and additionally implement Simplicity on Elements regtest as a cross-substrate reference point. We formalise a defender-loss metric L and, threat by threat, characterise how L varies with the fee rate across designs. Two results constrain the design space: (1) a Griefing–Safety Incompatibility (no vault simultaneously achieves permissionless recovery and griefing resistance), and (2) a Structural Decomposition theo- rem (covenant designs decompose along four axes whose values mechanically imply attack- class susceptibility, and no Bitcoin reference vault occupies either of the two non-dominated anchors of the resulting lattice). We measure OP_VAULT and CCV watchtower split- ting capacity (3,427 and 6,172 splits/block respectively), show that defender-side batched recovery—spec-level in BIP-345 §"Batching" and BIP-443 aggregation—approximately dou- bles the dust-threshold fee rate and flips the CCV/OP_VAULT ordering under watchtower exhaustion, and provide the first parameterised attack cost functions across fee environ- ments. Alloy bounded model checking verifies fund conservation, recovery liveness, and no- extraction properties. Simplicity's federated-sidechain threat model is treated separately.
The framework, measurements, and formal models are open-source.
From Pixels to Partners: A Hierarchical Approach to Adaptive Human-Robot Collaboration
Parekh, Sagar (Virginia Tech, 2026-05-27)
The increasing adoption of robots across various industries, including warehousing, healthcare, and meat processing, highlights their efficiency and ability to handle hazardous, repetitive tasks. However, the deployment of these systems often remains confined to isolated robot cells. To ensure reliability, current frameworks impose significant environmental constraints, such as fixed object placement, uncluttered workspaces, and uniform backgrounds. While robot learning performs well in such structured environments, it fails in the inherently unstructured and unpredictable real-world industrial settings. This dissertation addresses this fragility of current robot learning frameworks. We propose a hierarchical approach to developing adaptive robot learners, moving from foundational data efficiency to environmental generalization and, ultimately, sophisticated human-robot coordination.
The first tier of this research focuses on data-efficient skill acquisition. We introduce an autonomous reweighting framework designed to correct biases within imbalanced human datasets. This ensures the robot masters a full spectrum of behaviors, including critical edge cases, without the need for intensive manual labeling. The second tier addresses environmental generalization in the presence of static, task-irrelevant noise. We propose a masking approach that effectively "blinds" the robot to environmental distractions --- such as clutter or varying table texture --- allowing the policy to condition exclusively on the invariant features essential for expert decision-making. Building upon these foundations, the third tier tackles dynamic generalization through intentional human-robot collaboration. We develop an algorithmic framework capable of reasoning over high-level representations of human strategy and its evolution. This allows the robot to not only adapt to shifting human behaviors but also proactively influence partners toward high-reward, collaborative strategies.
Finally, we validate the technical feasibility of collaborative robots through a pilot study in a high-stakes industrial context: collaborative meat processing. By implementing specialized vision and safety control protocols for complex tasks like slicing and trimming, this work demonstrates a robust roadmap for integrating robots as safe, adaptive, and truly collaborative partners in the modern workforce.
Repurposing a Kinase Inhibitor for Rift Valley Fever Virus: Combating Hepatic and Neurological Disease Through Antiviral and Immunomodulation Strategies
Machado Flor, Rafaela (Virginia Tech, 2026-05-27)
Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic pathogen endemic across the African continent. It represents a significant concern to local agriculture, veterinary and human health, also possessing great pandemic risk. Transmission occurs through infected mosquito vectors or direct exposure to contaminated animal tissues and fluids, disproportionately affecting individuals in close contact with livestock. Human infection is typically self-limiting, but severe manifestations include hepatitis, encephalitis, retinitis, and hemorrhagic disease. No approved antiviral therapeutics currently exist for RVFV infection.
This dissertation investigated the role of host casein kinase 1 (CK1) in RVFV replication and NF-κB-mediated inflammation. CK1 regulates multiple cellular processes, including cell cycle progression, apoptosis, DNA damage repair, circadian rhythm, and immune signaling, making it a potential host-directed therapeutic target. Human lung, liver, and neural two-dimensional cell culture models were used to evaluate CK1 inhibition with BTX-A51. BTX-A51 treatment significantly reduced NF-κB-associated cytokines and inflammatory enzymes, while also demonstrating antiviral activity in monolayer cultures. Interactions between NF-κB and β-catenin signaling were additionally observed and discussed. These results were further tested in a human induced pluripotent stem cell (hiPSC)-derived cerebral organoid model. Cerebral organoids were permissive to RVFV infection, also exhibiting some NF-κB-mediated inflammation. BTX-A51 treatment attenuated inflammation while preserving interferon-α and β responses, although antiviral effects promoted by drug treatment in two-dimensional systems were not significant in organoids.
Collectively, these findings identify CK1 as a potential host-directed target for immunomodulatory treatment of RVFV pathogenesis and support BTX-A51 as a repurposed therapeutic candidate. This work also demonstrates the value of cerebral organoids as translational models that may reduce reliance on animal studies.
Keisho: A Non-Custodial Dual-Threshold Protocol for Bitcoin Inheritance with Legal Compliance
Vaghasiya, Dhruv Chandrakant (Virginia Tech, 2026-05-27)
Bitcoin's ownership model, where control is defined by possession of a private key rather than institutional authority, creates a fundamental inheritance problem. When an owner dies without transferring key material, their Bitcoin becomes permanently inaccessible. An estimated 17 to 23 percent of the total mined Bitcoin supply is already believed lost or inaccessible. Existing solutions force a tradeoff: custodial services sacrifice self-sovereignty, inactivity timers cannot distinguish death from temporary unavailability, and multisig wallets depend on single verification entities with no fallback if they fail. This thesis presents Keisho, a dual-threshold Bitcoin inheritance protocol that resolves this tradeoff through a novel composition of existing Bitcoin primitives. The protocol encodes two settlement paths within a single Taproot address. Path P1 enables accelerated settlement after timelock T1 with M-of-N heir signatures, m-of-k independent verifier attestations, and a coordination oracle co-signature. Path P2 provides a guaranteed fallback after timelock T2 using only M-of-N heir signatures, with no oracle, no verifier, and no infrastructure dependency of any kind. The design requires no new opcodes or consensus changes and is deployable on Bitcoin mainnet today. The protocol achieves four simultaneous guarantees that no existing solution provides together: (1) non-custodial operation, where no party holds keys to the inheritance funds; (2) guaranteed liveness, where heirs receive Bitcoin even if all non-Bitcoin infrastructure fails simultaneously; (3) legal framework compliance, where every cryptographic operation maps to specific provisions of the Uniform Probate Code, UCC Article 12, the ESIGN Act, and IRC 1014; and (4) formal security, established through eight security properties and a ten-adversary threat model. The protocol is implemented on Bitcoin regtest with role-isolated browser interfaces, a seven-document legal evidence pipeline with Nostr Schnorr signatures, Shamir-escrowed verifier identity protection across five jurisdictions, and deterministic verifier assignment using block-hash randomization. Three end-to-end protocol demonstrations and eleven additional property verifications against a fixed evaluation scenario provide systematic coverage of all four guarantee dimensions. The P2 fallback path was confirmed on-chain with all non-Bitcoin infrastructure offline, demonstrating that the liveness guarantee reduces to a single irreducible assumption: Bitcoin continues to operate.