Computer Sciences Commons^™

Rvit-Fusionnet: A Local Cross-Attention Feature Fusion-Based Hybrid Framework For Brain Tumor Classification, Naima Islam, Sajeeb Kumar Ray, Md Anwar Hossain, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

Accurate brain tumor classification via MRI is essential for diagnosis and treatment. This study introduces RViT-FusionNet, a hybrid deep learning model that integrates convolutional and transformer architectures for enhanced tumor detection. The model utilizes ResNet-50 to capture textural details and a Vision Transformer for extracting global context. A Local Cross-Attention (LCA) module is proposed to align and merge these features, allowing the network to model local structures and long-range dependencies concurrently. To enhance generalization across varied imaging conditions and tumor types, a domain discriminator is included to discern spatial and domain-specific patterns, fostering the learning of domain-invariant representations. The approach …

Scaling Up Multi-Agent Reinforcement Learning For Large Agent Teams And Long-Horizon Tasks: A Survey, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) empowers multiple autonomous agents to acquire effective policies for collaborative problem-solving. Over the last decade, MARL has seen significant advancements, with numerous algorithms achieving impressive performance across various benchmarks and real-world applications. Nevertheless, the scalability of multi-agent systems, in terms of the number of agents and the length of the task horizon, remains a critical consideration for applying MARL methods to complex problem-solving. Given that a dedicated review of the existing approaches and challenges in scaling up multi-agent systems remains largely absent, this survey aims to bridge this gap by delivering a comprehensive review of MARL …

Benchmarking Gaslighting Negation Attacks Against Multimodal Large Language Models, Bin Zhu, Yinxuan Gui, Huiyan Qi, Jingjing Chen, Chong-Wah Ngo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) have exhibited remarkable advancements in integrating different modalities, excelling in complex understanding and generation tasks. Despite their success, MLLMs remain vulnerable to conversational adversarial inputs. In this paper, we systematically study gaslighting negation attacks—a phenomenon where models, despite initially providing correct answers, are persuaded by user-provided negations to reverse their outputs, often fabricating justifications. We conduct extensive evaluations of state-of-the-art MLLMs across diverse benchmarks and observe substantial performance drops when negation is introduced. Notably, we introduce the first benchmark GaslightingBench, specifically designed to evaluate the vulnerability of MLLMs to negation arguments. GaslightingBench consists of multiple-choice …

Videocreator: An Agentic System For Multi-Turn Video Production, Zhengyang Liang, Yan Shu, Cathal Gurrin, Nicu Sebe, Lizi Liao

Research Collection School Of Computing and Information Systems

Recent advances in video generation models enable visually compelling single clips. However, real-world video creation is inherently continuous and iterative: creators refine content over multiple rounds while maintaining narrative, style, and entity consistency. Existing standalone generators are largely stateless and lack memory of previously generated segments, making it difficult to produce a coherent and consistent video project. To address this gap, we present VideoCreator, a unified video agent that integrates generation and understanding with a project-level memory system. VideoCreator leverages understanding capabilities to perform fine-grained analysis of newly produced content and uses persistent memory to retain and reuse prior context …

Frozen Lvlms For Micro-Video Recommendation: A Systematic Study Of Feature Extraction And Fusion, Huatuan Sun, Yunshan Ma, Changguang Wu, Yanxin Zhang, Pengfei Wang, Xiaoyu Du

Research Collection School Of Computing and Information Systems

Frozen Large Video Language Models (LVLMs) are increasingly employed in micro-video recommendation (MVR) due to their strong multimodal understanding. However, existing apporches typically deploy LVLMs as fixed black-box feature extractors without systematically comparing alternative representation strategies. To address this gap, we present the first systematic empirical study on various feature extraction paradigms and integration strategies, along with hierarchical representations from frozen LVLMs for MVR. Extensive experiments on representative LVLMs reveal that hidden states from multiple decoder layers provide richer and more effective representations for MVR. Guided by this insight, we propose the Dual Feature Fusion (DFF) Framework, a lightweight approach …

Language Embeddings Meet Shallow Autoencoders, Rodrigo Alves, Vojtěch Vančura, Pavel Kordík, Antoine Ledent

Research Collection School Of Computing and Information Systems

Shallow autoencoders are appealing recommenders due to their simplicity, scalability, and competitive retrieval quality, but they struggle in strict cold-start settings where new items have no interactions. We propose an inductive shallow autoencoder that leverages item side information (language embeddings) by fixing the decoder to item features and learning only an encoder in the same semantic space. To prevent trivial self-reconstruction without enforcing a hard zero diagonal, we introduce diagonal gating: a leave-one-item-out objective that blocks the self-copy shortcut only for the item being updated while retaining context from the rest of the user history. An alternating-style optimization trains the …

History To Future: Evolving Agent With Experience And Thought For Zero-Shot Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Zihan Wang, Guo-Sen Xie, Yang Yang, Jinshan Pan, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve …

Task Complexity Matters: An Empirical Study Of Reasoning In Llms For Sentiment Analysis, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families—including adaptive, conditional, and reinforcement learning-based reasoning architectures—on sentiment analysis datasets of varying granularity (binary, five-class, and 27-class emotion). Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions: (1) Reasoning shows task-complexity dependence—binary classification degrades up to -19.9 F1% points (pp), while 27-class emotion recognition gains up to  +16.0 pp; (2) Distilled reasoning variants underperform base models by 3–18 pp on simpler tasks, …

A Novel Hierarchical Multi-Agent System For Payments Using Llms, Donghao Huang, Joon Kiat Chua, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language model (LLM) agents, such as OpenAI’s Operator and Claude’s Computer Use, can automate workflows but unable to handle payment tasks. Existing agentic solutions have gained significant attention; however, even the latest approaches face challenges in implementing end-to-end agentic payment workflows. To address this gap, this research proposes the Hierarchical Multi-Agent System for Payments (HMASP), which provides an end-to-end agentic method for completing payment workflows. The proposed HMASP leverages either open-weight or proprietary LLMs and employs a modular architecture consisting of the Conversational Payment Agent (CPA - first agent level), Supervisor agents (second agent level), Routing agents (third agent …

“Alexa, Do Not Say That In Front Of My Boss!” A Cross-Cultural Comparison Of User And Ai Preferences For Privacy-Aware Smart Speaker Interactions Across Contexts, Lynne Warin, Emily Aurelia, Anthony Tang, Emily Aurelia, Delphine Reinhardt

Research Collection School Of Computing and Information Systems

Due to their limited ability to reason about the social context in which they are used, smart speakers pose significant privacy risks by responding in ways that may violate people's implicit social boundaries. We conducted a cross-cultural vignette study (N = 944) in Germany and Singapore to investigate how situational factors—specifically social context (bystander relationships and closeness), physical context (location), and interaction context (topic and deceptive intent)—regulate user preferences for smart speaker responses. Our results demonstrate that these factors are superior predictors of response preferences than dispositional user traits (i.e., intrinsic personal traits). We identify two distinct social dynamics: a …

Integer Quantization And Embedded Deployment Of Cnn Star Trackers For Cubesats, Meora R. Giusiano

Master's Theses

A star tracker determines spacecraft orientation by photographing the star field, detecting stars in the image, matching them against a catalog, and computing the rotation between observed and cataloged directions. Convolutional neural networks (CNNs) have been proposed as replacements for the detection and centroiding stage, offering improved sub-pixel accuracy and recovering faint stars that classical thresholds lose to stray light and sensor noise. The improvement comes at higher computational cost; the PolySat systemboard targeted in this work lacks the floating-point hardware these networks assume.

This thesis closes the gap between floating-point desktop evaluation and embedded integer deployment. Nine encoder-decoder CNN …

What Makes A Modern Attention Implementation?, Brian H. Slonim

Master's Theses

Since the seminal assertion by Vaswani et al. in 2017 that “Attention Is All You Need,” transformer models have risen to ubiquity due to their ability to learn extremely complex patterns from sequence data, culminating in the unprecedented generative capabilities of large language models. These models’ strength lies in their scale: hundreds of millions (e.g., BERT-LARGE) to billions or trillions of learned parameters. Running inference with these models, let alone training them, would be intractable without significant innovations in the hardware and software that support them. This need has driven an enormous demand for GPU compute and associated software ecosystems, …

High-Resolution Queries, Low-Resolution Context: Scaling Vision Transformers With Asymmetric Spatial Reduction, James M. Dwyer

Master's Theses

Vision Transformers (ViTs) have demonstrated great performance on image classifica tion benchmarks, however, the quadratic complexity of the self-attention mechanism with respect to sequence length limits their scalability to higher resolution inputs. The attention score matrix grows as O(N2) in both compute and memory, where N is the number of patch tokens, making ViTs computationally expensive and memory intensive for applications that require real-time inference or operate under resource constraints.

This thesis investigates whether the key and value sequences of the self-attention mechanism can be compressed using the local spatial structure of the image — while keeping queries at full …

Scorespeak: An Agentic System For Natural Language Control Of Musical Scores, Nathan S. Lim

Master's Theses

With the recent popularization of large language models (LLMs), natural language has become one of the most accessible and powerful ways for people to interact with creative tools. Although they have become common in mainstream domains like image and audio editing, there is currently no robust AI-based system that can reliably turn free-form language into edits for symbolic musical scores. This gap represents a missed opportunity to improve human workflows for creating and editing sheet music, but it is also a fundamental limitation for other agentic music systems; without a robust mechanism for translating free-form language into structured scores, AI …

Political Inconsistency Detection Across Legislative Speech And Public Communications, Scott M. Pramuk

Master's Theses

Political actors communicate about legislation across multiple contexts, including committee hearings, recorded votes, and public-facing press releases. Differences between these forms of communication can provide useful signals for journalists and researchers seeking to understand how legislators present policy positions to different audiences.

This thesis extends the Digital Democracy Project, a legislative transparency initiative that provides access to California state legislative hearing transcripts, voting records, and related legislative data. Specifically, this work incorporates publicly accessible, legislator-authored news releases into the Digital Democracy Database and develops a pipeline for analyzing legislative communication across multiple sources. The system collects news releases from California …

Multimodal Machine Learning For Soil Burn Severity Mapping Across California Wildfires, Sanjana Checker

Master's Theses

Accurate mapping of soil burn severity (SBS) is critical for post-fire watershed management, erosion risk assessment, and ecological recovery planning, yet traditional field-based approaches remain costly, time-intensive, and spatially limited. This thesis presents a machine learning pipeline for wall-to-wall SBS classification across California wildfires using multi-sensor satellite imagery, terrain derivatives, and bioclimatic covariates. Field-collected SBS observations (n = 2,180) from 52 wildfires occur- ring between 2013 and 2025, sourced from the U.S. Forest Service and CAL FIRE, were used to train and evaluate multiple classification architectures within a Google Earth Engine and Google Cloud-based prediction framework. After upsampling the unburned …

Ai, Medicine, And Social Determinants Of Health Data, Ryan Doyloo, Nicholson Price

Articles

This article examines how medical AI systems are incorporating SDoH data and the governance challenges that follow. The authors show that while SDoH integration can enhance clinical workflows and predictive accuracy — potentially improving outcomes for underserved populations — it also introduces acute risks of proxy discrimination, where facially neutral variables replicate protected characteristics. Surveying U.S., EU, and international frameworks, the authors argue that existing regimes lack clear ex ante guidance to distinguish beneficial from harmful uses of SDoH data. In response, they advance post-market monitoring as a pragmatic and scalable pathway: generating real-world, SDoH-stratified evidence that can support enforcement, …

Principles Of Privacy And Security In Artificial Intelligence And Applications, Khang Tran

Dissertations

Modern artificial intelligence (AI) systems have transformed critical domains such as healthcare, software engineering, finance, and the legal system. Despite their broad impact, concerns about trustworthiness, especially regarding privacy and security, remain major obstacles to wider adoption. Addressing these concerns requires both a systematic understanding of the privacy and security risks inherent in AI systems and the development of principled foundations for trustworthy AI that safeguard client privacy and security. This goal is particularly challenging because of the complexity of modern large-scale AI systems, the trade-offs between privacy and model utility, and the need to simultaneously ensure other important properties …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

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 …

Edge Co-Occurrence Regularization For Node Classification, Kadir Altunel

Theses

We propose a simple yet effective regularization technique for node classification on graphs that leverages edge-based label co-occurrence patterns. We first train an MLP on node features to produce class probability distributions, then compute a fixed penalty matrix from edge-based co-occurrence statistics of these predictions. This penalty matrix, which captures unlikely class combinations on connected nodes, is then used to regularize GNN training without further updates. We evaluate this approach across multiple homophilic datasets (Cora, CiteSeer, PubMed, ogbn-arxiv) and heterophilic benchmarks (Chameleon, Squirrel, Actor, Roman-Empire) using three GNN architectures: GCN, GraphSAGE, and H2GCN. Results show consistent improvements on homophilic graphs, …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Mri-Based Deep Learning Radiomics Model For Automated Classification Of Disc Degeneration In The Lumbar Spine, Shiv Patil, Om Gandhi, Mert Karabacak, Matthew Carr, Konstantinos Margetis

Student Papers, Posters & Projects

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging (MRI) is critical for clinical management and patient selection for spine surgery. This study aims to develop and evaluate machine learning (ML) models that combine features from deep learning (DL) and radiomics for the automated prediction of Pfirrmann grade (PG), a measure of disc degeneration, using multi-parametric lumbar spine MRI. Sagittal T1, T2, and T2 SPACE MRIs of 218 patients with LBP were acquired from the SPIDER dataset. For each intervertebral disc and available sequence, …

The Lorekeeper’S Trial: Teaching Ai Literacy Through Active Learning In The Library Classroom, Taylor Greene, Douglas R. Dechow

Library Articles and Research

How can librarians engage students in critical, hands-on learning about artificial intelligence within the limitations of a one-shot session? At Chapman University, librarians have developed an AI literacy session that integrates ethics and hands-on exploration into workshops and course-embedded sessions. This presentation highlights how to weave AI literacy into information literacy instruction, with a focus on a First-Year Foundations program.

Presenters will discuss their efforts to reach students, staff, and faculty through AI literacy initiatives across campus. They will also demonstrate how the Lorekeeper’s Trial—a research quest inspired by RPGs—transforms AI and information literacy concepts into collaborative challenges. Through a …

Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee

Faculty Publications

Background: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. Methods: Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient’s history. Hyperparameter-tuned …

Generation Z And The Ai Misinformation Paradox: Understanding A New Digital Vulnerability, Cecilia Cooley, Elizabeth Sperber

DU Undergraduate Research Journal Archive

This paper asks: How and why is Generation Z more vulnerable to AI-generated misinformation and disinformation than older generations? Using a comparative review of recent empirical studies, survey data, and meta-analyses from 2019–2025, this paper synthesizes research on Gen Z’s exposure to and interaction with AI-produced content across social media platforms. Although it is commonly assumed that Gen Z ’s technological exposure and fluency make them better equipped to recognize false information, findings show the opposite: Gen Z is consistently outperformed by older cohorts in detecting AI-generated falsehoods. This vulnerability stems from three intersecting factors: (1) the sheer volume of …

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.

Panda-Plus-Bench: A Clinical Benchmark For Evaluating The Robustness Of Ai Foundation Models In Prostate Cancer Diagnosis, Joshua L. Ebbert, Dennis Della Corte

Faculty Publications

Artificial intelligence foundation models are increasingly deployed for prostate cancer Gleason grading, where GP3/GP4 distinction directly impacts treatment decisions (active surveillance vs. intervention). However, these models may achieve high validation accuracy by learning specimen-specific artifacts rather than generalizable biological features, limiting real-world clinical utility. We introduce PANDA-PLUS-Bench, a curated benchmark dataset derived from expertly annotated prostate biopsies designed specifically to quantify this failure mode. The benchmark comprises nine carefully selected whole slide images from nine unique patients containing diverse Gleason patterns, with non-overlapping tissue patches extracted at both 512 × 512 and 224 × 224-pixel resolutions across eight augmentation conditions. …

High-Throughput Robotic Ethanol Inhibition Assays For Engineered Thermophilic Biofuel Strains, Kevin He, Daniel Olson, Marybeth Maloney, Anthony Lanahan

Wetterhahn Science Symposium Posters

Ethanol stress assays are commonly used to evaluate microbial tolerance, metabolic adaptation, and fermentation performance. However, manual liquid handling introduces variability across replicate wells and small-volume pipetting steps, limiting reproducibility and throughput. This study developed an automated OT-2 robotic workflow to generate replicated ethanol concentration gradients for high-throughput inhibition assays in engineered thermophilic biofuel strains. Kinetic plate-reader measurements were used to quantify ethanol-dependent growth responses under anaerobic fermentation conditions. The reasearch question is: How do engineered thermophilic biofuel strains differ in ethanol-dependent growth inhibition under anaerobic fermentation conditions, and can automated robotic assays improve the reproducibility of these measurements? Can …