Graphics and Human Computer Interfaces Commons^™

Streamlined Biomedical Image Processing Pipelines, Jiehyun Kim

Graduate Doctoral Dissertations

This dissertation focuses on advancing carotid artery analysis through a series of visualizations and deep learning tools for calcified plaque assessment and related biomedical imaging tasks. Accurate plaque evaluation is essential, but current workflows depend on slow, clinician-dependent manual review. To address these limitations, this work introduces the CACTAS framework, a set of tools and methods that enable fast and reliable plaque segmentation for clinicians.

The first study, the CACTAS-Tool, provides a web-based labeling tool that enables clinicians to label plaque directly in three dimensions through a streamlined one-click interface. This tool significantly reduces the effort required to generate high-quality …

Perceptual And Geometric Advances In Crowd Simulation, Bilas Talukdar

Dissertations

Simulating realistic crowd motion remains a fundamental challenge in computer graphics and multi-agent systems, as it requires modeling both physically plausible interactions and perceptually natural behaviors. Existing crowd simulation methods typically employ simplified geometric abstractions, most commonly circular agent representations, and model navigation using either analytical interaction formulations (e.g., force, velocity, or constraint-based methods) or learned policies derived through reinforcement learning. Despite their effectiveness, these approaches often overlook detailed geometric structure and do not explicitly account for perceptual realism. This dissertation addresses these challenges by improving the realism of virtual crowd simulation through two key advancements: perceptual preference learning and …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Sophia Wismar, Henry Staats, Allison Metzler, Chloe Puckett, Rachel Levine, Christa Kilpatrick, Scott Wolf, Joe Walsh, Grace Doolittle, John Engebreston, Zoe Lopez, Christopher Aaby, Audrey Duff, Timothy Sisk, Katelyn Lamberton, Angela Narayan, Gilkah Argueta, Habiba Samir, Girena Tesfazghi, Genet Kenore, Sinit Tesfamariam, Effley Brooks, Abi Newell, Megan Doherty, Natalie Baer, Lexi Blood, Talya Riciputi, Jessica Jimenez, Devin Hernandez, Lynn Clark, Taj Kumar, Sunil Kumar, Allen Rutman, Mira Pronobis, Tess Carson, Anna Sher, Frankie Stroud, Tamra Pearson D'Estree, Alyssa Wilson, Emily Melnick, Jenalee Doom, Yihang Gao, Gwendolyn Geiger, Noah Gettle, Scott Nichols, Clare Ayoub, Cara Dienno, Sunny Walker, Zoe Hansen, Maya Wheeler, Addison Rice, Patrick Martin, Sanjana Acharya, Daniel Mcintosh, Amanda Mckellips, Calli Cain, Justin Blake, Peter Sokol-Hessner, Natalie Miller, Max Weisbuch, Sophia Dellota, John Macikas, Charlotte Snow, Mark Siemens, Zoe Lynch, Alex Huffman, Prachi Shah, Jason Roney, Halcyon Levi, Nicole Herzog, Andrea Koly, Daniel Linseman, Annie London, Xi Yang, Avery Zwisler, Jane Smith, Chaz Contag, Michael Kerwin, Lucy Rand, Grace Schroeder, Michelle Rozenman, Nissa Tapper, Guiming Zhang, Mateo Mazariego-Halpern, Keith Meyer, Julie Do, Dakota Park-Ozee, Travis Herink, Kara Neu, Jonathan Plomin, Eve-Odine Duchaufour, Debbie Gale Mitchell, Tennyson Anderson-Stricklin, Lily Treitz, Samantha Rosenberger, Sierra Griffith, Finley Joseph, Daniel Sampson, Emmy Davis, Skyler Kasnoff, Evon Lopez, Vivian Nguyen, Cassy Young, Franklin Sellner, Martin Tobon, Ila Graham, Zach Billings, Holden Hedit, Decatur Boland, Paul Kosempel, Cory Chandler, Jay Mahoney, Sam Dragan, Susan Dagget, Yarrow Ator, Heidi Vuletich, Owen Weber, Andrew Kloeppel, Petersen Gray, Mandi Schaeffer-Fry, Razleen Bassra, Bryanna Rodriguez, Christina Blue, Taubie Sanders, Rachel Epstein, Luke Milburn, Camryn Evans, Ezra Martinez, Mary Westwood, Gabri Notov, Robin Tinghitella, Lilou Cabrol, Eli Barbour, Juliet Mendik, Selma Myers, Zac Wise, Noah Fahlin, Michelle Knowles, Abigail Hopper, Michael Greenberger, Romi Laclair, Sarah Watamura, Sabrina Efroymson, Casey Barker, Sydney Seltzer, Bryn Yehle, Jennifer Hoffman, Sara Garcia, Ryuka Nagamine, Trevor Briggs, Remy Le Boeuf, Elena Krone, Eileen Farrell, Regan O'Rourke, Elena Roel, Greg Mortimer, Ali Ayoub, Stefani Langehennig, Caitlin Turk, Logan Scmid, Stefan Chavez-Norgaard, Karen Kim, Tatiana Peccedi, Courtney Cassidy, John Sebesta, Rhianna Lewis, Janice Bening-Lacek, Vivian Lawless, Mckenna Hanson, Jeffrey Amidon, Riya Joshi, Ram Ambre, Brady Worrell, Perrin Schneider, Ali Azadani, Brooke Agulnek, Lyndsie Salvagio, Elise Siemanowki, Yan Qin, Andre Allen, Melodie Nguyen, Megan Livengood, Abby Reams, Saffron Hartreeve, Bri Wylie, Sarah Brookman, Mariah Loiacono, Green Russo, Abhia Lodhi, Gabrielle Welsh, Nika Spehar, Shahked Levin, Evrim Baykal, Kimberly Chiew, Jocelyn Torres, Kailey Hicks, Mykaela Tanino-Springsteen, Audrey Bellows, Akam Chahal, Madeline Tepper, Shannon Murphy, Alexa Fonseca, Deborah Han, Cassandra Perez, Oluwatoyin Alaba, Julia Roncoroni, Vy Nguyen, Nana Burn, Sarah Sasse, Rubin Tuder, Anthony Gerber, Nancy Lorenzon, Christine Vohwinkel, Camryn Gunter, Tristan Weber, Sam Rommel, Brian Michel, Muskan Fatima, Alannah Oleson, Kira Frey, Edward Garrido, Beckett Morris, Kerstin Haring, Drew Middleton, Abigail Walpert, Liam Dee, Gabby Ishaw, Cole Carnes, Maddie Weiser, Claire Fox, Valeriia Vlasenko, Kateri Mcrae, Riley Smith, Abigail Templin, Kushani Rajapaksha

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

Frictional Intelligence, Posheng Cheng

Masters Theses

This is an experimental interaction design project that challenges anthropomorphism in human-computer interaction. In particular, the recent advancement of artificial intelligence technologies like Large Language Models has taken anthropomorphism to new heights. The conversational chatbot interface of AI prioritizes mimicking an inherently human communication medium to maximize human-likeness. However, anthropomorphism has several downsides. Conversational interfaces obscure the limitations and the tangible cost of the technology. They also imply fictional moral status and human-level cognitive capabilities, which means general public sentiment focuses on the ``overhyped'' excitement and fear rather than on other socio-ethical and capacity questions that are far more urgent …

Conditional Product Sampling For Gaussian Process Implicit Surfaces, Song Shi

Dartmouth College Master’s Theses

Gaussian Process Implicit Surfaces (GPISes) provide a powerful and unified stochastic geometry representation for rendering surfaces, volumes, and the rich continuum between them. Recent work has shown that GPISes can model a broad space of visual appearances under a unified light transport framework. However, practical rendering with GPISes remains challenging: existing estimators can become inefficient for particular correlation structures, and highly anisotropic or heightfield-like GPISes require specialized treatment to obtain robust variance reduction.

This thesis extends recent work on GPIS rendering by introducing a new next-event estimation (NEE) technique for anisotropic GPISes.We show that standard NEE provides diminishing benefits as …

Approval Motivations In Sharing Humorous Tiktok's, Mariam Al-Areedy

InnovateHER Meeting 2026

TikTok is a short-form video platform where users create and share content that is often centered around humor, trends, and everyday social experiences. In face-to-face interactions, people typically rely on immediate feedback to navigate conversations, often using approval seeking behaviors to gain positive reactions and rejection-avoidant behaviors to reduce the risk of negative judgement. While these motivations are well-established in in-person settings, less is known about how they function in digital environments like TikTok, where teens privately share humorous content without immediate social cues to guide their interactions. My general hypothesis was that both rejection avoidance and approval-seeking behaviors will …

3d Puzzle Generation Beyond Voxelized Parts, Iris Xia

Computer Science Theses

Burr puzzles are interlocking assemblies whose pieces must be inserted and removed through tightly constrained motions. Designing them is difficult because geometric fit, interlocking behavior, and disassembly order are tightly coupled, while existing computational methods remain largely limited to voxelized or template-based constructions.

This work presents a framework for 3D puzzle generation beyond voxelized parts. The method replaces local mobility heuristics with a certified search over geometry edits. Starting from a topological contact specification, it constructs signed distance fields for individual parts, applies complementary local edits, and validates each candidate using exact geometric checks and a kernel disassembly graph. The …

High-Frequency Vr-Native Eye Tracking: From Data Collection To Machine Learning Models, Meherun Nesa Shraboni, Aryabrata Basu

Research and Creative Works Expo

No abstract provided.

Spaceforge: Spatial Reconstruction For Signal Simulations, Compton Ross

Honors Theses

Signal simulation environments require accurate three dimensional representations of physical spaces, yet current methods for generating these representations, including Light Detection and Ranging (LiDAR) scanning, manual 3D modeling, and commercial photogrammetry, are both costly and time intensive. SpaceForge addresses this gap with a prompt guided pipeline that takes an ordinary indoor photograph and a configurable set of simulation relevant object categories as input and produces a voxelized 3D scene compatible with downstream signal simulation workflows. The pipeline proceeds through five major stages: open set object detection and segmentation, object level preprocessing, single image 3D mesh reconstruction, heuristic pose estimation and …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …

Evaluating The Use Of Extended Reality Technology To Improve Marching Band Conducting Patterns, Nathan R. Fuhrman

Electrical Engineering and Computer Science Undergraduate Honors Theses

Conducting pattern consistency is an essential skill for marching band drum

majors, yet developing this consistency through individual practice remains diffi-

cult without real-time feedback. This thesis investigates if the use of extended

reality technologies can be used to enhance the conducting skills of novice drum

majors. Using the Meta Quest 3’s passthrough capability, the system overlays vi-

sual feedback elements — including a 3D pattern guide, path visualization, tempo

cues, and a real-time score — onto the user’s physical environment. A within-

subjects study with seven participants evaluated eight combinations of three binary

feedback variables: pattern guide visibility, tempo …

Beyond The Interface: Human Perceptions Of Generative-Ai Chatbots As Conversational Partners, Browning W.E. Blair

All Theses

Generative AI (gen-AI) chatbots are becoming embedded in everyday communicative life, yet it remains unclear whether users perceive these systems as socially reciprocative conversational partners. Therefore, this study examines how young adults understand and interact with gen-AI chatbots, focusing on perceptions of conversational partnership, anthropomorphism, politeness, discomfort, and technical understanding. Guided by the CASA framework, Media Equation Theory, and the uncanny valley hypothesis, this study employed four semi-structured, online focus groups with 15 undergraduate students and recent college graduates in the United States. Findings indicate that participants did not broadly perceive gen-AI chatbots as conversational partners in the interpersonal sense. …

Teacher-Student Diffusion Model For Text-Driven 3d Hand Motion Generation, Ching Lam Cheng, Bin Zhu, Shengfeng He

PhD Student’s Publications Collection

Generating realistic 3D hand motion from natural language is vital for VR, robotics, and human-computer interaction. Existing methods either focus on full-body motion, overlooking detailed hand gestures, or require explicit 3D object meshes, limiting generality. We propose TSHaMo, a model-agnostic teacher-student diffusion framework for text-driven hand motion generation. The student model learns to synthesize motions from text alone, while the teacher leverages auxiliary signals (e.g., MANO parameters) to provide structured guidance during training. A co-training strategy enables the student to benefit from the teacher’s intermediate predictions while remaining text-only at inference. Evaluated using two diffusion backbones on GRAB and H2O, …

Analysis Of Volumetric Reconstruction Methods In Archaeology, Cade O'Fallon

All-Inclusive List of Electronic Theses and Dissertations

The use of Structure from Motion (SfM) photogrammetry in archaeological projects is entering a period of transition; a method of producing 3D data that is traditionally embraced as tool for documentation is being explored for its analytic potential. One such way SfM photogrammetry can be used analytically is through the calculation of volumes using photogrammetric data. The tools exist for archaeologists to be able to create and quantify volumetric models; however, the discourse on these methods is still so new there is no consensus on the best method for conducting volumetric work. Different methods of creating and isolating volumetric space …

Utilizing Brain Computer Interfaces That Interact With A Virtual Keyboard, Skye Lilienthal

Honors Theses

A brain-computer interface (BCI) can allow someone to utilize electrical signals in their brain to complete tasks using a computer. BCIs can help people take advantage of technology to type without the need for a traditional keyboard setup. This paper used the OpenBCI Mark IV to test the effectiveness of non-invasive BCIs with dry electrodes within the OpenViBE P300 Speller. This paper shows how to use the P300 speller through a setup pipeline. Results indicate that electrode placement affects P300 accuracy and that areas related to visual processing improve accuracy, suggesting that P300 signals can be detected within OpenBCI Mark …

It's Not Nde Without U And X: Preparing For Change With Inclusive Research, Sanjeet Mann, Heather L. Cribbs

Library Faculty Publications & Presentations

The upcoming Next Discovery Experience (NDE) introduces major changes to how users search, interpret information, and navigate Primo. Preparing for NDE is an opportunity to center the diverse students and faculty who rely on discovery systems every day, ensuring that their lived experiences, accessibility needs, and research practices guide interface design, configuration, and communication. This session presents a consortial approach to NDE readiness that positions students and community members as partners in the development process. We describe strategies for creating ethical and rigorous UX research workflows that include IRB approval, purposeful recruitment, accessible study design, and clear documentation on the …

Research On The Accessibility Elements Of Rural Public Digital Cultural Services Based On Aism-Fmicmac, Lihui Peng, Banruo Gu, Chuang Hong, Xisheng Hu

Journal of Scientific Information Research

[Purpose/significance] A structural analysis of the stratified dependencies among accessibility determinants in rural public digital cultural ecosystems enables identification of pivotal developmental nodes, thereby establishing theoretical foundations for optimizing service delivery mechanisms and achieving spatial equilibrium in digital cultural provision. [Method/process] This study applies grounded theory to conduct a comprehensive coding analysis of policy documents related to China's rural public digital cultural services. Through this process, five dimensions and fifteen sub-components of accessibility are identified. An index system for accessibility factors is then developed using the AISM approach. The identified elements are classified into three hierarchical levels. To further validate …

Drug Risk Knowledge Discovery For Western Medicines Based On Knowledge Graph Link Prediction, Jianxiang Wei, Ma Hengyuan Ma, Yuehong Sun, Wenwen Du, Letian Hu

Journal of Scientific Information Research

[Purpose/significance] The risk information contained in drug instructions is usually incomplete, and some new adverse reactions can only be discovered in actual clinical use. This paper proposes an information organization and knowledge discovery method for pharmacovigilance, in order to timely and accurately identify missing risk knowledge in drug instructions. [Method/process] Drug instructions of 8 152 Western medicines are collected as the research data; On the basis of ontology construction, data annotation, and model training, the UIE model is used to jointly extract entity and relationship triplets from the research data; A new knowledge graph link prediction method CompGCN-RotatE, is proposed, …

Research On Temporal Knowledge Graph Completion Method For Emergent Events Based On Bigru And Graph Contrastive Learning, Peng Wu, Zhenyu Lu, Xuechen Zhang

Journal of Scientific Information Research

[Purpose/significance] During emergencies, social media short texts contain critical information but are heavily interfered with by noise. Traditional static knowledge graph completion techniques struggle to effectively address their dynamic evolution and data sparsity issues, making it imperative to introduce temporal modeling methods. [Method/process] This study proposes a dynamic completion framework that combines the temporal feature capture capability of Bidirectional Gated Recurrent Units (BiGRU) with the noise-resistant representation learning advantages of Graph Contrastive Learning (GCL). At the completion level, the ConBiTE method is introduced, which captures temporal dependencies through self-attention mechanisms and BiGRU, while leveraging GCL to enhance the completion of …