Computer Sciences Commons^™

Basis Design For Electronic Structure And Beyond, Weishi Wang

Dartmouth College Ph.D Dissertations

At the intersection of quantum physics, quantum chemistry, and materials science, electronic structure is the study of electrons in solid-state and molecular systems. Electronic-structure computation relies on discretizing the many-electron Hamiltonian with a finite single-particle basis set. However, basis-set construction is conventionally treated as an ad hoc preprocessing step. This thesis develops an expressive and flexible framework for active, system-oriented basis-set design and numerical modeling strategies that treat basis functions as tunable representations to encode electronic ground-state information.

We first introduce a multi-layered, differentiable basis-construction framework that embeds a set of primitive parameters into mixed-contracted Gaussian-type orbitals. We then develop …

A Trigger For The Autonomous Decommissioning Of Smart Devices, Ravindra Mangar, Jared Chandler, Jingyu Qian, Carl A. Gunter, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

Smart devices are ubiquitous in modern environments, yet their decommissioning phase remains poorly studied and often overlooked in system design. We define secure decommissioning as the process by which a smart device securely disconnects from its environment and makes sensitive data inaccessible. If not decommissioned, devices may retain sensitive information – such as security credentials or user-behavior data that could be recovered by an adversary. Unfortunately, some users may forget to decommission a device when they dispose or sell it, and cannot decommission a device that is lost or stolen. This paper investigates a trigger mechanism for individual wireless smart …

Confidential, Attestable, And Efficient Inter-Cvm Communication With Arm Cca, Sina Abdollahi, Amir Al Sadi, Marios Kogias, Hamed Haddadi, David Kotz

Other Faculty Materials

Confidential Virtual Machines (CVMs) are increasingly adopted to protect sensitive workloads from privileged adversaries such as the hypervisor. While they provide strong isolation guarantees, existing CVM architectures lack first-class mechanisms for inter-CVM data sharing due to their disjoint memory model, making inter-CVM data exchange a performance bottleneck in compartmentalized or collaborative multi-CVM systems. Under this model, a CVM's accessible memory is either shared with the hypervisor or protected from both the hypervisor and all other CVMs. This design simplifies reasoning about memory ownership; however, it fundamentally precludes plaintext data sharing between CVMs because all inter-CVM communication must pass through hypervisor-accessible …

Visionglow: Evaluating Minimal-Disruption Smart-Home Control In Apple Vision Pro, Hongxiao Zheng

Dartmouth College Master’s Theses

Smart-home control in mixed-reality environments like Apple Vision Pro often relies on disruptive, application-based paradigms, such as using a smartphone or a windowed virtual interface. These methods create a “mode switch” that imposes cognitive load and pulls users from their primary tasks. We present VisionGlow, a minimal-disruption spatial interaction technique for Vision Pro. VisionGlow represents devices as spatially-anchored “orbs.” To control a device, the user looks at its orb and performs a pinch gesture, which invokes a compact, contextual control panel. We conducted a within-subjects study (N=18) comparing VisionGlow against two baselines: the standard Apple Home app on a smartphone …

Optimal Hypergraph Connectivity With Cut Queries, Hang Liao

Dartmouth College Ph.D Dissertations

Finding connected components in undirected hypergraphs—hypergraph connectivity—is a fundamental problem in computer science. It can be framed as a special case of Symmetric Submodular Function Minimization (SSFM), where the objective is to determine if the non-trivial minimizer is zero. This thesis develops an optimal algorithm for hypergraph connectivity within the $\CUT$ query model, where an algorithm probes a subset of vertices to learn the weight of the hyperedges ``cut" by that partition.

Our approach is constructive, culminating in an optimal algorithm for the general problem by first developing the necessary tools for two foundational subproblems. The main contributions of this …

Revitalization Of Endangered Languages With Ai, Ivory Yang

Dartmouth College Master’s Theses

The preservation and revitalization of endangered languages, particularly those with minimal digital presence, presents significant challenges for computational linguistics. This thesis addresses these challenges by proposing novel methods for language identification and data generation, focusing on underrepresented Indigenous languages, specifically Nüshu, Native American and Native Alaskan languages.

In the first study, a COLING 2025 paper, we present NüshuRescue, an AI-driven framework designed to facilitate the preservation of Nüshu, an endangered script used exclusively by Yao women in China. Using minimal seed data, we demonstrate how GPT-4-Turbo can generate new translations, expanding a publicly available Nüshu-Chinese corpus, achieving 48.69% accuracy in …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Multi-Level Differentiable Moving Particles With Partition Of Unity, Jinjin He

Dartmouth College Master’s Theses

Representing implicit geometry with intricate features has long been a challenge. Recent advances in Implicit Neural Representations (INRs) have shown great promise in applications such as 3D reconstruction, inverse rendering, and dynamic surface evolution. These methods leverage neural networks to model complex shapes continuously, offering advantages in resolution and flexibility over traditional discrete representations. Despite their success, efficiently handling fine geometric details and evolving dynamic scenes remains an open problem.

We introduce a differentiable moving particle representation based on the multi-level partition of unity (MPU) to model dynamic implicit geometries efficiently. Our approach employs two types of particles—feature particles and …

A Framework For Scalable And Controlled Hallucination Data Collection, Lin Ting Liang

Computer Science Senior Theses

This thesis addresses a key bottleneck in hallucination research: the scarcity and limitations of hallucination benchmark datasets. Existing datasets typically focus on a single type of hallucination and are expensive to produce due to the need for manual prompt creation and annotation. To overcome these challenges, we propose a novel mixture-of-experts (MoE) adversarial framework that actively induces hallucinations. Our framework employs three large language model (LLM) agents that iteratively and adversarially revise prompts to provoke hallucinated responses from a target question-answering model. It automates the generation of both intrinsic hallucinations (logical inconsistencies) and extrinsic hallucinations (inclusion of unverifiable external information). …

K-Mshc: Unmasking Minimally Sufficient Head Circuits In Large Language Models With Experiments On Syntactic Classification Tasks, Pratim Chowdhary, Peter Chin, Deepernab Chakrabarty

Computer Science Senior Theses

Understanding which neural components drive specific capabilities in mid-sized language models ($\leq$10B parameters) remains a key challenge. We introduce the $(\bm{K}, \epsilon)$-Minimum Sufficient Head Circuit ($K$-MSHC), a methodology to identify minimal sets of attention heads crucial for classification tasks as well as Search-K-MSHC, an efficient algorithm for discovering these circuits. Applying our Search-K-MSHC algorithm to Gemma-9B, we analyze three syntactic task families: grammar acceptability, arithmetic verification, and arithmetic word problems. Our findings reveal distinct task-specific head circuits, with grammar tasks predominantly utilizing early layers, word problems showing pronounced activity in both shallow and deep regions, and arithmetic verification demonstrating a …

Evaluating Vision Language Model Capabilities For Time Series Interpretation: An Empirical Study With Conversation Duration And Psychological Flourishing Data From The Studentlife Dataset, Jusung Park

Computer Science Senior Theses

This research investigates the capability of Vision Language Models (VLMs), specifically ChatGPT‑4o, to interpret and predict psychological outcomes based on visual representations of time series data. Leveraging conversation duration metrics and psychological flourishing scores from the StudentLife dataset, this study rigorously evaluates the predictive accuracy of VLMs using various methods, including zero‑shot on raw data, zero‑shot on graph data, few‑shot learning, qualitative labeling, and chain‑of‑thought reasoning. Despite multiple methodological enhancements, predictive performance remains modest, revealing significant challenges in quantitative interpretation of visualized temporal data by current multimodal models. We demonstrate that standardizing input tokens by using graph images rather than …

A Steiner Tree Vc Set System In Minor-Free (Di)Graphs, Eli Friedman

Computer Science Senior Theses

We propose a set system of maximum-covering minimum-density partial Steiner trees for planar and minor-free graphs. We show that this system has VC dimension at most h-1 for edge-weighted K_h-minor-free graphs, both directed and undirected. We also consider its geometric interpretation as a range space, proving it to be piercing.

In addition, we demonstrate how one can form a junction tree set system of bounded VC dimension from such Steiner trees. This is motivated by refining the junction tree set cover approach used in Chekuri and Jain's polylogarithmic approximation algorithm for Directed Steiner Forest in planar graphs [CJ25].

Character Relationship Prediction In Movies: Toward Emotionally-Aware Automatic Audio Descriptions, Seung Hyun Hahm

Computer Science Senior Theses

Automatic audio description (AD) systems support visually impaired audiences by narrating visual content, but they often fail to capture the interpersonal dynamics that underpin narrative understanding. In this work, we introduce a novel framework for character relationship prediction as a means of enriching audio descriptions with socially grounded context. Our contributions are threefold: (1) we propose the Character Relationship Module (CRM), which extends identity-aware video captioning with directed sentiment inference between character pairs; (2) we develop a scalable weak supervision pipeline that uses large language models to generate 669,520 relationship annotations across 202 films; and (3) we construct a complementary …

Bayesian Segmentation–Driven Informative Path Planning For Uav-Based Water Orthomosaic Generation, Phuc Dai Tran

Computer Science Senior Theses

This paper presents a comprehensive implementation

study of an informative path planning (IPP) algorithm

for autonomous water body detection and mapping using

unmanned aerial vehicles (UAVs). We propose a hybrid IPP

framework that seamlessly integrates Bayesian probabilistic

classification and real-time uncertainty quantification to achieve

superior flight efficiency and mapping accuracy compared

to conventional systematic coverage methods. Our approach

employs the state-of-the-art SegFormer deep learning segmentation

model in conjunction with log-odds-based orthomosaic

generation to produce high-fidelity water body maps under

diverse environmental conditions. Through random sampling of

the FloodNet dataset, we demonstrate that our IPP algorithm

maintains flight distance while achieving …

Motionteller: Multi-Modal Integration Of Wearable Time-Series With Llms For Health And Behavioral Understanding, Aiwei Zhang, Arvind Pillai, Andrew Campbell, Nicholas C. Jacobson

Computer Science Senior Theses

As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy - minute-level movement data collected via accelerometers? In this work, we introduce MotionTeller, a generative framework that natively integrates minute-level wearable activity data with large language models (LLMs). MotionTeller combines a pretrained actigraphy encoder with a lightweight projection module that maps behavioral embeddings into the token space of a frozen decoder-only LLM, enabling free-text, autoregressive generation of daily behavioral summaries.

We construct a novel dataset of 54,383 ⟨actigraphy, text⟩ pairs derived from real-world NHANES recordings, and …

An Efficient Algorithm For Finding High Harmonic Centrality Vertices In Graphs, John Balson

Computer Science Senior Theses

In this thesis we consider the problem of harmonic centrality in graphs. This measure is used widely in the study of real-world complex networks. In particular, we consider the problem of finding a high harmonic centrality vertex in time much faster than $O(mn)$, the time required to calculate the exact harmonic centrality of all vertices in a graph. The problem of calculating centrality measures faster has received much attention in recent years, since calculating the exact harmonic centrality of all vertices can be infeasible for large graphs; hence, faster algorithms are needed. This thesis proposes a new algorithm for finding …

An Approach To Stylometry Using Causal Language Models, Harrison F. Stropkay

Computer Science Senior Theses

We present a novel stylometric approach using large language models. By training separate models on individual authors' works, we find that each model achieves lower cross-entropy loss when predicting text from its training author compared to other authors' texts. Moreover, for any given text, the model trained on its true author’s corpus yields the lowest loss. We suggest that, in this way, a model trained on one author's works embodies the unique writing style of that author. We demonstrate our approach on works by eight known authors. This approach also confirms that R. P. Thompson wrote the well-studied 15th book …

Multi-Modal Emotion Appraisals Using Large Language Models, Carlos Guerrero Alvarez

Computer Science Senior Theses

This thesis investigates the capabilities of large language models (LLMs), specifically GPT4 and GPT4o, in appraising human emotional responses within strategic social scenarios. Building on the work of Houlihan et. al[7], we evaluate LLMs using a dataset of human emotion ratings from game-theoretic situations, later extending the experimental paradigm to include both text and multimodal (image and profession) inputs. Our methodology introduces novel prompting techniques for the experiment at hand, and we compare the performance of these techniques with expert perspectives to assess how different prompts influence model predictions. Results show that LLMs can approximate human emotional appraisals, with the …

Machine Verification Of Correctness For A Concurrent Union-Find Object, Karun Ram

Computer Science Senior Theses

We consider a family of near-optimal randomized multiprocessor implementations for the union-find problem due to Jayanti and Tarjan–known as the Jayanti-Tarjan Randomized-Linking (JT-RL) union-find objects–and provide the first formal and fully machine-verified proof of their strong linearizability (i.e., correctness). Their algorithms are efficient both in theory and in practice: numerous benchmarking works demonstrate that they perform faster, or as fast, as all other known implementations for the union-find object on both CPUs and GPUs.

The correctness of the JT-RL algorithms is subtle, which motivates the need for formal verification. To this end, we first specify the JT-RL objects in TLA+, …

Cross-Modality Learning For Predicting Ihc Biomarkers From H&E-Stained Whole-Slide Images, Amit Das

Computer Science Senior Theses

Hematoxylin and Eosin (H&E) staining is a cornerstone of pathological analysis, offering reliable visualization of cellular morphology and tissue architecture for cancer diagnosis, subtyping, and grading. Immunohistochemistry (IHC) staining, an important ancillary study, provides molecular insights by detecting specific proteins within tissues, enhancing diagnostic accuracy, and improving treatment planning. However, IHC staining is costly, time-consuming, and resource-intensive, requiring specialized expertise. To address these limitations, this study proposes HistoStainAlign, a novel deep learning framework that predicts IHC staining patterns directly from H&E whole-slide images (WSIs) by learning joint representations of morphological and molecular features. The framework integrates paired H&E and IHC …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Coda: A Digital System For Generation Of Piano Practice Exercises From Symbolic Music Notation, Annie Tang

Computer Science Senior Theses

Effective practice remains one of the greatest challenges in music education, yet cur- rent digital music tools primarily support only passage engagement or surface-level feedback, failing to provide proactive guidance for overcoming technical challenges within piano repertoire. This thesis presents Coda, a digital system that generates custom piano practice exercises from symbolic music notation based on established piano pedagogy principles that have historically been taught orally.

Unlike existing tools that only provide a viewable symbolic music notation display or only an interface to take notes and record a practice session, Coda uses rule- based algorithmic transformations rooted in pedagogical logic …

Are Cycles Of Neural Activity The Algorithm Of The Brain?, Edwin Omondi Onyango

Computer Science Senior Theses

We propose that precisely timed neural activity cycles can serve as structural primitives for memory and computation in a system that exhibits associative learning like the brain. Inspired by biologically grounded mechanisms such as calcium-dependent plasticity, spike-timing-dependent learning, and phase-sensitive excitability, we construct a spiking neural network model in which repeated temporal coincidences drive the formation of self-sustaining activity loops. These cycles, once formed, persist as dynamic memory traces: not stored as static weights, but as reverberating patterns that replay in time when these loops are restarted. We show that noise alone fails to induce stable structure, but even sparse, …

Exploring The Facets Of Responsible Ai: Interpretability, Biases, And Morality Of Large Language Models, Sean Xie

Dartmouth College Ph.D Dissertations

This thesis investigates critical aspects of responsible artificial intelligence (AI) — specifically model interpretability, bias detection and mitigation, and moral alignment in large language models (LLMs) — due to their pivotal role in the deployment of transparent, fair, and ethical AI systems. By addressing these dimensions of responsible AI, we hope to foster the increased trust and understanding necessary for wider AI adoption.

We begin by surveying the existing landscape of interpretability metrics and critically assess the effectiveness of interpretability methods designed to generate reliable explanations. Building upon this evaluation, we introduce novel model architectures and frameworks explicitly developed to …

Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng

Dartmouth College Ph.D Dissertations

Listening to fast-tempo piano sonatas of the Classical period (circa 1750-1820) has been shown to have therapeutic effects for neurological disorders such as epilepsy. The limited existing repertoire of music in this style motivates the creation of more long-form, coherent compositions with clearly defined structure. Despite the long history of computer-based music generation and recent progress in deep learning, particularly transformer-based models, generating structurally coherent long-form music remains a major challenge. This difficulty stems from the scarcity of reliable structural annotation datasets, the computational demands of modeling very long musical sequences, and the lack of effective structural encoding in both …

Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao

Dartmouth College Ph.D Dissertations

The rapid advancement of robotics necessitates systems capable of adapting to complex, unstructured environments. Soft robots, with their flexibility and compliance, excel in delicate interactions, making them ideal for medical applications and search-and-rescue missions. Modular robots, on the other hand, offer reconfigurability, enabling diverse task-specific adaptations in dynamic settings. Despite their individual advantages, the integration of soft and modular robotics remains underexplored. This proposal aims to develop soft modular robots that combine the adaptability of soft robotics with the versatility of modularity. These systems will be capable of autonomously transitioning between locomotion, manipulation, and infrastructure assembly across land, water, and …

Learning Parent Strategies: A Trajectory-Based Approach To Analyzing Parent Child Interactions With Llms, Chelsea Joe

Computer Science Senior Theses

Recent advancements in large language models (LLMs) have demonstrated their ability to leverage its immense world knowledge to excel in traditional reinforcement learning tasks. Building on these capabilities, we introduce a framework that uses LLMs to learn from human behavior video data and generate insights that serve as guidelines for predicting how individuals are likely to act. Our framework focuses on parent- child dialogic reading, with an emphasis on understanding each parent’s unique parenting strategies. The system identifies meaningful interaction episodes within analyzed video data. The learning component of the framework utilizes a long-term memory system, which stores and retrieves …

Deep Learning For Fine-Grained Digital Histopathology Image Analysis, Joseph Dipalma

Computer Science Technical Reports

As digital pathology becomes increasingly popular, it is critical to develop machine learning solutions to utilize this data. While other image modalities have seen exponential increases in methodology availability, the same has not been true for histopathology images. This is likely in part because histopathology whole slide images possess unique characteristics that prevent simply applying existing methods as-is.

In this thesis, we identify and propose solutions to 3 open problems with histopathology images: 1. large raw image size (up to 150,000×150,000 pixels in size), 2. low class-positivity (low ratio of positive to negative patches), and 3. limited image availability with …

Examining Intersectional Queer Biases In Large Language Models: A Combined Statistical And Visual-Qualitative Approach For Quantification And Explanation, Huu Duong (Chip) Nguyen

Computer Science Senior Theses

Despite significant advancements in research on (intersectional) social biases in Large Language Models (LLMs), intersectional biases affecting subgroups within the LGBTQ+ community remain critically understudied. Existing bias detection methodologies often prioritize quantification but lack depth in explaining the specific stereotypes/biases that shape evaluation metrics. To address these gaps, this study proposes a combined statistical and visual-qualitative approach to quantify and identify persistent intersectional queer biases in five recent, state-of-the-art LLMs through a downstream story generation task. Findings from analysis uncover substantial evidence of stereotypes that perpetuate harmful, reductive narratives against intersectionally marginalized groups within the LGBTQ+ community. To promote public …

Designing Accessible Ui/Ux For Epileptic Patients: A Scalable Solution For Music Therapy Delivery, Amethyst G.H. Mckenzie

Computer Science Senior Theses

How can we design an accessible, scalable UI/UX system tailored to the cognitive, visual, and motor impairments of epileptic patients, that ensures safe and effective interactions with music therapy applications? This research explores the intersection of accessibility, user-centred design, and digital health, using an iterative design process to develop and refine the SONATA app—a clinically deployable music therapy platform.

Through two prototype iterations, usability testing, and quantitative event logging, this study compares the effectiveness of structured versus flexible navigation in improving user experience. Key findings reveal that structured navigation reduces unintended detours, while progressive disclosure techniques enhance instructional clarity. Additionally, …