msc thesis defense - computer science: jay vinit lunia

please join the computer science department for the upcoming thesis defense:

presenter: jay vinit lunia

thesis title: trustworthy learning with graph-aware quantum-classical transformers for hyperspectral imaging and nlp tasks

abstract: this thesis develops a unified framework for trustworthy and efficient learning under constrained representations, with experiments spanning hyperspectral image understanding and natural language classification. the central premise is that representation constraints should be designed as explicit modeling choices rather than treated as unavoidable limitations. following this premise, the thesis builds architectures that combine structured inductive bias, compact latent interfaces, and reliability-centered evaluation. for hyperspectral learning, the proposed methodology integrates patch-level graph construction with transformer attention to preserve local spatial coherence while still modeling long-range spectral interactions. this design is extended to multitask prediction by sharing a constrained backbone across classification and regression objectives. for compact hybrid modeling, the thesis introduces a low-dimensional quantum–classical token interface in which a variational quantum encoder acts as a structured bottleneck before lightweight transformer blocks. for reliability analysis, the thesis evaluates not only aggregate predictive accuracy, but also adversarial sensitivity, explanation stability, and cross-setting consistency under matched interface constraints. the empirical study covers multiple benchmark settings with heterogeneous spectral characteristics and class imbalance profiles, together with text classification tasks under fixed encoder conditions. results show that the proposed constrained architectures achieve strong predictive performance while maintaining stable behavior across datasets and tasks. in hyperspectral experiments, the graph-aware transformer pipeline produces consistently high class-level and global metrics. in compact hybrid experiments, the quantum-classical interface remains competitive at low parameter budgets and reveals distinct robustness and attribution patterns when compared with a matched classical head. overall, the thesis establishes a single integrated claim: principled representation constraints can improve reliability, interpretability, and deployment practicality without requiring a trade-off against competitive predictive quality. the work contributes an end-to-end blueprint for designing constrained yet trustworthy learning systems, including architecture design rules, evaluation protocols, and practical guidance for future extensions to stricter generalization tests and hardware-grounded hybrid inference.

committee members:
dr. saad b. ahmed (supervisor, committee chair), dr. garima bajwa, dr. thangarajah akilan (software engineering)