DISSERTATION DEFENSE

Author :  Matthew Sharp
Advisors: Dr. Chin-Tser Huang
Date: Aug 14, 2026
Time: 04:00 pm
Location: Online
Link:  https://teams.microsoft.com/meet/211658530586897?p=AAPp2DUFHhla2JclD4

Abstract

Traditional blockchain systems typically process transactions using uniform ordering policies and fixed validation procedures, regardless of urgency, workload type, or contextual importance. While this design supports consistency and security, it limits the effectiveness of blockchain systems in applications where time-critical or trust-sensitive operations must be handled quickly without compromising fairness, accountability, or auditability. This dissertation addresses these limitations by introducing adaptive blockchain mechanisms that combine reputation signals, ledgernative structural controls, and priority-aware scheduling policies to support more responsive transaction processing.

In this dissertation, we propose priority-aware and adaptive blockchain mechanisms for time-sensitive and trust-sensitive environments. The motivation is to improve the responsiveness and fairness of blockchain systems when processing heterogeneous workloads that may differ in urgency, trust level, service requirements, or application context. The goal is to provide flexible blockchain mechanisms that can dynamically distinguish between urgent and ordinary work while preserving the trust
guarantees expected from distributed ledger systems. To achieve this goal, the dissertation builds on a smart-marker-based blockchain design that encodes control semantics directly within the ledger. These markers support operations such as branching, merging, referencing, and priority-aware processing while maintaining auditability and consistency. The proposed approach is grounded in distributed consensus, trustbased reasoning, and fairness-aware scheduling. Rather than treating blockchain
processing as a uniform sequence of transactions, this work frames blockchain opii eration as a decision-making problem shaped by dynamic workload characteristics, reputation signals, and policy constraints. By combining reputation-aware evaluation, ledger-native control structures, and bounded scheduling policies, the proposed system balances responsiveness, fairness, and security. This enables structured adaptation without abandoning the accountability and verification properties that make blockchain systems trustworthy. As a result, the dissertation supports the practical deployment of adaptive blockchain designs across distributed applications where timing, trust, and workload differentiation are critical.

The main research questions in this dissertation are as follows:

How can blockchain mechanisms support multiple concurrent computational outcomes without sacrificing ledger integrity?
Our first contribution proposes a smart-marker-based reputational probabilistic blockchain architecture that enables branching and merging within the ledger.
This design allows multiple agents or algorithms to produce concurrent outcomes, which are preserved as branchchains rather than discarded through premature consensus. Each branchchain is associated with a probabilistic reputation score that reflects historical performance and reliability. This approach enables the system to maintain alternative results while guiding decision-making through trust-aware evaluation. Experimental results demonstrate that the architecture supports multi-agent collaboration and improves interpretability in decision-oriented applications.

How can adaptive blockchain mechanisms be applied in real-world, resource-constrained environments?
Our second contribution evaluates the proposed mechanism through an application study in blockchain-based electronic voting. A smart-marker-based branching architecture is used to execute multiple vote-counting algorithms in parallel, improving robustness and decision reliability. The system is implemented on a resource-constrained platform to assess feasibility under limited computational resources. Experimental results demonstrate that the architecture supports scalable processing, low power consumption, and transparent auditability in practical deployment scenarios.

3. How can blockchain systems process heterogeneous workloads in a fair and efficient manner?
Our third contribution introduces a priority-aware scheduling mechanism for blockchain systems. Transactions are classified based on urgency, service tier, and workload characteristics. A scheduling mechanism is developed to allocate processing resources while enforcing fairness constraints such as bounded delay and anti-starvation guarantees. The system balances faster service for highpriority transactions with continued progress for lower-priority workloads. Evaluation metrics include latency, throughput, and contract satisfaction, demonstrating that the proposed scheduling approach improves responsiveness while maintaining equitable access.

4. How can blockchain systems reduce operational latency for timesensitive applications without weakening consensus guarantees?
Our fourth contribution proposes an early admission mechanism that allows privileged blocks to become partially validated and visible before full validation is complete. Admission decisions are governed by reputation thresholds and partial validation criteria. Smart markers are used to record provisional states and ensure auditability. The mechanism reduces time-to-inclusion while preserving the security guarantees of eventual consensus. A game-theoretic model is planned be developed to analyze adversarial behavior and determine safe operating parameters for early admission policies.

This dissertation advances blockchain design from static, uniform transaction processing toward adaptive, fairness-aware, and application-aware operation. By integrating reputation, structural control, and scheduling mechanisms, the proposed
mechanisms enable blockchain systems to support both ordinary and time-sensitiveworkloads within a unified and trustworthy infrastructure.

DISSERTATION DEFENSE
 

Author :  Ziyu Zhao
Advisors: Dr. Song Wang
Date: Aug 17, 2026
Time: 01:00 pm
Location:  2277 (M. Bert Storey Engineering and Innovation Center)
Link:  https://teams.microsoft.com/meet/240422703068254?p=EsBfT7Q4WmKzfnWa8K

Abstract
While the accuracy of semantic segmentation has improved rapidly, extending a trained model to new categories remains expensive: each addition still requires collecting
dense annotations and training again, which limits the use of segmentation where the categories of interest are not known in advance. This dissertation studies how external prior information can assist semantic segmentation in open-vocabulary and few-shot settings, and refers to this as Prior-Conditioned Dense \mbox{Prediction}. In these settings the target category is specified by an input, consumed the same way in training and at test time, rather than learned into the model's parameters, so new categories require no retraining. This is where assistance is needed: the inputs themselves are difficult to use directly, for two different reasons. A category name identifies what to segment but provides no appearance evidence with which to localize the category in a particular image. A labeled example identifies what to segment by showing it, but in 3D segmentation such examples conventionally take the form of point clouds in which the annotation is given point by point, and producing even a few of them is laborious. The dissertation therefore constructs auxiliary prior evidence, generated from the names, transferred from a cheaper modality, or synthesized from what is given, and converts it into a form directly comparable with the scene. Dense matching between the constructed evidence and the scene then yields the labels at every pixel or point. Where the constructed evidence is unreliable, because it crosses modalities or contains generated content, the model additionally adapts it to the scene or weights it by estimated quality.

Three studies instantiate this view, ordered by how much of the evidence must be constructed. In the first study, for open-vocabulary 2D segmentation, category names are paired with diffusion-generated exemplars that supply the appearance the names lack, combined into a dual-prompt cost volume refined at inference. In the second study, for label-efficient 3D segmentation, the point-wise labeled 3D examples are replaced by labeled 2D images, lifted into pseudo RGB-D point clouds by monocular depth estimation and matched to the scene through prototypes in a shared embedding space. In the third study, where a single labeled view leaves the geometry incomplete, further views are synthesized by RGB-D inpainting; since the newly revealed regions are generated rather than observed, learned view and point weights discount them when prototypes are formed. On standard 2D and 3D benchmarks, each study improves over methods that consume the supplied inputs in their original form.

Together, the studies show that auxiliary prior evidence can strengthen dense prediction well beyond what the task's own inputs support, once it is constructed into a comparable
form and consumed through localized matching. They further indicate that adaptation and weighted aggregation are best introduced according to the semantic, cross-modal, or visibility mismatch present in each setting, rather than adopted uniformly