Digital Commons Network^™

How Do Anthropogenic Disturbance, Insect Diversity And Bird Diversity Intersect? A Study Based On Three Sites In Northern Ohio, Ella Kavanaugh, Micaela A. Tahta Dadourian, Isla Mccullough, Jason Gleditsch

Research Symposium

Human land use can influence bird and arthropod diversity as they change food availability, create edge effects between forest patches and developed land, and impact habitat composition. Many studies have been done to understand the impact of anthropogenic disturbance on bird and arthropod diversity showing forest-dependent species decreasing and generalist species increasing as forest composition changes. Our research aims to expand upon the factors that affect bird and arthropod diversity in three sites that vary in land use and human disruption. To determine the influence of anthropogenic activity, we focused on the variation of bird diversity across our sites related …

Anomalous Voting Patterns In The Rural South, Michael Herscovici

Research Symposium

This project analyzes community-level voting patterns in the rural American South to identify and better understand communities that defy longstanding patterns of race-based voter polarization. Specifically, this project uses advanced statistical methods as well as an in-depth ethnography in order to understand the relationships between ideology and identity in predominately Black communities that vote more Republican than other communities with similar Black populations. I argue that these Republican-leaning Black communities are the result of low-propensity voters shifting towards the GOP and defying traditional political socialization patterns in Black communities. The implications of this study hint at the future of race-based …

Art Criticism As Categorical Reasoning, Madeline Jester

Research Symposium

In this paper, I offer an interpretation of Kendall Walton’s framework of art appreciation in Categories of Art. According to Walton, in order to appreciate an artwork, we must perceive it as belonging to some artistic ‘category’, because categorization determines which features of an object are aesthetically relevant. Different categorizations of the same object, then, can produce different artworks. I’ll argue for a bipartite analysis, where the intended categories of an art object can come apart from the categories through which the object is the most aesthetically valuable work, and that which way of categorizing is ‘correct’ depends on our …

Tappan Square: A Ghost Story, Camille Thorp

Research Symposium

The purpose of this research is twofold: producing a genre critique of paranormal investigation literature and critically engaging further with its rhetorical strategies through a case study of the ghosts of Tappan Square. The genre is established in a survey of texts from varying disciplines and degrees of veracity, ranging from literary journalism to American Gothic fiction to parapsychology. These disparate works are linked by their dual disenchantment and re-enchantment of reality in the act of both locating ghosts and working to understand them. From their spiritual language emerges an experiential grammar; the personal, obsessively dot-connecting narration of such ghost …

The Oberlin Parking Problem: The Pet Peeve And The Potential Payoff Of Asking Questions, Danielle Leydon

Research Symposium

No abstract provided.

End Of Dino Dominance - Ecological Trends In Late Cretaceous Communities, Matthieu Weinberg, Liam Lucas, Katya Borkovskaya

Undergraduate Research and Engagement Symposium

We studied the ecosystem stability of an End-Cretaceous ecosystem out of the Lance Creek Formation in Wyoming. This ecosystem represented a time interval immediately before the mass extinction wiping out all non-avian dinosaurs. Overall, our data suggests that stability was relatively good leading up to the asteroid impact that catalyzed the mass extinction.

Calculating The Abundance Of Primordial Black Holes, Babel Barm, Cole Owen Bucholtz

Undergraduate Research and Engagement Symposium

This project aims to understand the formation of primordial black holes (PBHs) in the early universe. PBHs are hypothetical black holes formed shortly after the Big Bang. They are fascinating because they might explain mysterious cosmic phenomena like dark matter or the origin of supermassive black holes. To estimate how many PBHs could have formed, we will use two different mathematical models: the Press-Schechter formalism and the theory of peaks. Comparing these methods will help us test the reliability of current theories and guide future astronomical observations.

Interference Colors To Thickness: Measuring Thin-Film Dynamics With A Camera And Arduino, Babel Barm, James Knott

Undergraduate Research and Engagement Symposium

In this project, we studied how the thickness of a soap film can be measured using the colors created by thin-film interference. When light shines on a soap film, some light reflects off the front surface and some off the back surface. These light waves combine and create colorful patterns that depend on the film’s thickness. By observing these patterns, we can learn how the thickness of the film changes over time.

We used two methods to collect data: a camera to capture the colors of the film and a phototransistor connected to an Arduino to measure the intensity of …

From Cellphone Surface To Microbial Identity: Characterization Of An Unknown Bacterium, Raayan Haseeb Jafri

Undergraduate Research and Engagement Symposium

This presentation explores the identification of an unknown bacterium isolated from a cellphone, highlighting the presence of microorganisms on everyday surfaces. Through microscopy, biochemical testing, and metabolic analysis, the organism was systematically characterized based on its morphology and physiological properties. Key tests included Gram staining, motility analysis, and multiple biochemical assays to determine metabolic capabilities. The results suggest that the organism is a Gram-positive, non-motile, fermentative bacterium most consistent with a Streptococcus species. This project demonstrates the importance of microbial identification and emphasizes the relevance of microorganisms in human-associated environments.

Examining The Efficacy Of The Allosteric Site In Adp Glucose Pyrophosphorylase From A. Tumefaciens, Shanna Kaldani, Isabella Tyma

Undergraduate Research and Engagement Symposium

ADP-glucose Pyrophosphorylase (ADP-GlcPPase) is a regulatory enzyme in bacteria that synthesizes glycogen. A. tumefaciens ADP-Glc PPase is activated by Fructose 6-phosphate (Fru6P) and Pyruvate (Pyr). To investigate Fru6P binding, the Tyrosine 124 and Arginine 75 residues of A. tumefaciens ADP-Glc PPase were mutated to Alanine.  We found that Y124A and R75A had some activation by Fru6P and Pyr but did not reach a Vmax equivalent to Wildtype. R75A/Y124A  had similar activation to the single mutants. Thermal Shift assays tested the enzyme-ligand binding of the activators. This project expanded our understanding of the allosteric binding in A. tumefaciens ADP-Glc PPase.

Elucidating The Function Of An Evolutionarily Conserved Embryonic Splice Variant Of Type Ii Collagen During Vertebrate Development, Taylor Woolman

Undergraduate Research and Engagement Symposium

This study investigates the role of exon 2 in the zebrafish col2a1a gene, a paralog of the human COL2A1 gene essential for connective tissue and skeletal development. Exon 2 encodes the von Willebrand Factor Type C (VWF-C) domain, which binds TGF-β ligands involved in embryonic dorsoventral patterning. Using CRISPR-Cas9, we aim to delete exon 2 and prevent expression of the embryonic splice variant (v1), forcing premature use of the post-embryonic variant (v2). We hypothesize that loss of the VWF-C domain will disrupt TGF-β signaling, leading to defects in embryonic development and vertebral formation.

Phosphorylation And Isomerization Of The Synaptic Scaffold Protein Psd-95, Sai Kanuru

Undergraduate Research and Engagement Symposium

PSD-95 is an important scaffolding protein located in the postsynaptic density that is abundant in glutamatergic synapses in the brain. It provides structural support for receptors in the postsynaptic membrane. It has been shown to be implicated in various neurological disorders such as Alzheimer's Disease and schizophrenia. It plays a crucial role in mediating NMDA receptor-dependent LTD, which is one of the primary mechanisms behind synaptic plasticity. NMDAR-LTD is dependent on multiple posttranslational modifications, one of which being phosphorylation. Our experiments investigated Pin1 mediated isomerization and phosphorylation of T19.

Social Justice Internship- Girlforward: How A Non-Profit Is Supporting The Refugee Community In Chicago., Liz-Anne Debeauville-Jones

Undergraduate Research and Engagement Symposium

Abstract:

GirlForward operates as a critical stabilizing force for immigrant communities amid an increasingly volatile political climate. This non-profit supports refugee and asylum-seeking girls aged 14- 21 displaced by global conflict. Programming is structured around three core pillars: Mentorship, Education, and Safe Spaces– designed to foster personal development and community integration. Their 4W framework seeks to promote holistic development through Wellness, Wisdom, Wallet, and World knowledge.

Sustained through philanthropic contributions, their services play a pivotal role in mitigating the socio-economic challenges these families face. Without community support, participants encounter significant barriers that affect their stability and integration in Chicago.

Modeling The Influence Of Environmental Factors On Invasive P. Clarkii Catch Per Unit Effort In An Urban River System, Penelope Reynolds, Reuben Keller, Rachel Anelise Rogers

Undergraduate Research and Engagement Symposium

Procambarus clarkii is an invasive crayfish species that has recently become established in the North Shore Channel of the Chicago River. The Keller Lab has been intensively trapping P. clarkii in the NSC and collecting data for the past eight years with the goal of reducing population growth and species spread. My research will use our collection of data and analyze it against multiple different environmental variables to explore the potential relationships between P. clarkii catch-per-unit-effort and environmental conditions. These relationships could provide insight on future trapping methods to maximize our invasive species removal efforts.

Alpha And Beta Particle Motion Ray Tracing In A Cloud Chamber, Faiza Nadeem, Florence Pan, Ryan Madera

Undergraduate Research and Engagement Symposium

This project investigates particle motion within a cloud chamber using a thoriated electrode and Helmholtz coil to artificially “create” alpha and beta particles, respectively, within the chamber. To quantify the motion of these particles, we monitor the chamber’s internal conditions using an Arduino micro-controller and trace the particles’ paths of motion using a video analysis software called Fiji. The value of this research comes from the experimental process itself because it necessitates the use of programming skills, a comprehensive understanding of complex physical principles, and an intensive data collection process by undergraduate students.

Testing The Motivational Effects Of Exciting Lh To Pptg And Ldtg Projections In Rats, Emma Bickford

Undergraduate Research and Engagement Symposium

Drugs of abuse and rewarding stimuli increase dopamine (DA) activity in the mesolimbic system, particularly within the ventral tegmental area (VTA) and nucleus accumbens (NAcc). Mesopontine regions, including the laterodorsal tegmental nucleus (LDTg) and pedunculopontine tegmental nucleus (PPTg), regulate VTA DA activity and contribute to drug sensitization, though their afferent inputs remain poorly defined. The lateral hypothalamus (LH), a key motivational region, projects to both the LDTg and PPTg, suggesting a pathway for modulating mesolimbic DA signaling. Using optogenetics, this study will selectively stimulate LH–PPTg projections in rats to examine their role in motivation, feeding, reward processing, and addiction.

Construction Of A Geiger-Müller Counter, Katie Siobhan Bennett, Benjamin Allen Murdoch

Undergraduate Research and Engagement Symposium

Our investigation describes the construction of a Geiger-Müller Counter from an Arduino Uno kit. With the use of some resistors, a breadboard, DC powersource and a Geiger tube, and other miscellaneous supplies, we were able to not only physically build the circuitry for the Geiger-Müller Counter, but we were also able to program it to produce sounds, lights, and a display screen to show the count rate of the sources used. This investigation allowed for a comparison between our system and an industrial-grade counter, as well as demonstrated the simplicity of creating expensive equipment with accessible and affordable materials.

Investigating The Impact Of Individual Differences In Inattentiveness On Learning From Gesture, Alexander Hurka

Undergraduate Research and Engagement Symposium

Attention Deficit/Hyperactivity Disorder (ADHD) is associated with inattentiveness and lower working memory capacity, which can impair academic performance. Gestures, movements of the hands that express information alongside speech, help maintain attention and support learning. We investigated whether adults learn Japanese vocabulary more effectively through speech alone or speech+gesture instruction. Baseline and visual attentiveness were measured to explore interactions between attention and gesture-based learning. Spontaneous gesture rates were also measured to consider the implications of gesture production in learning. This study helps understand and develop gesture-related tools that can be particularly beneficial to students struggling with attention.

Hand Gesture Control For A Di2erential Drive Robot, Seyoung Kan, Laray Lopez, Zachary Hinnen, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

As technology becomes increasingly integrated into daily life, seamless and multimodal human-robot interaction has become essential. This project presents a lightweight, real-time human-following system implemented on a differential-drive robot (Ham-Bot) using a Raspberry Pi, motivated by the growing need for robotic companionship among elderly and mobility-impaired individuals. The system integrates HSV-based color segmentation, MediaPipe hand gesture recognition, LiDAR-based distance regulation, and a six-state finite state machine to achieve robust human-robot interaction without relying on heavy machine learning models for body detection. A socket-based communication architecture streams camera frames from the robot to a MacOS client, where perception is performed and …

Hardware Cluster For Key Security And Education Experiments, Michelle Mcaveety, Rylan Pietras, Ahmed Ghoneim, Robert Karam

Bellini College REU Symposium (BCREUS)

In response to increased cyberattacks against digital infrastructures, significant contributions have been made to the education of software-based cybersecurity principles through online resources, coursework, and other means. However, there is a lack of accessible platforms dedicated to the learning of hardware-based cybersecurity principles. Specifically, topics such as side-channel attacks or fault injection attacks can be challenging to study without expensive hardware resources. To address this need, we propose the Hardware Cluster for Key Security and Education Experiments (HaCKSEE) platform. HaCKSEE is an experiment-oriented platform designed to democratize hardware security education. The proposed platform allows students to conduct configurable hardware security …

Neurons Of Bias: Power-Mediated Compliance And Mitigation In Large Language Models, Anubhav Gupta, Otavio Rosa, Mahammed Kamruzzaman, Gene Kim

Bellini College REU Symposium (BCREUS)

Introduction & Problem Statement

Large language models (LLMs) are increasingly deployed in sociotechnical systems involving power, authority, and compliance, raising important questions about fairness, consistency, and reliability across diverse user populations. As these systems are integrated into hiring tools, customer service systems, assistive technologies, and decision-support pipelines, they are expected to operate consistently regardless of the identity they are prompted to adopt. However, when demographic attributes are introduced into prompts, model behavior may shift even when identity does not change the underlying task.

In power-mediated settings, such variation is especially important because differences in compliance, refusal, or loophole-seeking behavior may …

Misty: An Llm-Driven Recipe Recommender Robot, Alina Pineda, Stuti Goyal, Jingjing Li, Zhao Han

Bellini College REU Symposium (BCREUS)

Introduction & Problem Statement

Meal planning requires balancing nutritional needs, available ingredients, and user preferences, which can be time-consuming and cognitively demanding. While mobile recipe applications provide personalized recommendations, they lack interactive and socially engaging communication. In contrast, social robots such as Misty enable expressive interaction through speech, gestures, and emotional cues, but typically rely on rigid, pre-programmed behaviors and lack domain-specific knowledge for tasks such as recipe recommendation.

This gap highlights the need for a system that combines the strengths of both technologies. Recipe recommendation is inherently multimodal, requiring both social interaction for engagement and visual presentation for detailed …

Strider: Human Re-Identification Using Gait Recognition, Mark Calvelo, John Pham, Aleksandre Papunashvili, Mauricio Pamplona Segundo

Bellini College REU Symposium (BCREUS)

Gait Recognition pipelines have shown promise in recent years with modern models such as SkeletonGait [1] and GPGait [2] demonstrating decent performance on known benchmarks. These Gait Recognition models utilize resource and computationally expensive pipelines for full end-to-end Gait Recognition. This overhead can prove to be a problem for real-time tasks such as on-device recognition. In this project, we aim to reduce one of the most important phases of the Gait Recognition pipeline which is Skeleton and Silhouette generation. We build off of the YOLOv11 architecture [3] simplifying aspects of the existing model to create a model that can efficiently …

Last-Mile Autonomous Delivery Robot, Lucas Araujo Bianco, Clement Joseph, Chance Hamilton, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

Autonomous delivery robots require robust, real-time navigation systems to safely traverse dynamic outdoor environments like college campuses. This project presents an edge-optimized vision pipeline deployed on a resource-constrained Jetson Nano-based robot to accurately identify drivable surfaces and detect path intersections. We employ a lightweight semantic segmentation Convolutional Neural Network (LRASPP MobileNetV3-Large), initially pretrained on the Cityscapes dataset. By applying transfer learning to a manually refined, locally labeled dataset of our campus, we significantly improved feature extraction for urban textures and adapted the model to our specific use case. To ensure efficient edge deployment, the network is optimized using TensorRT [1], …

Microarchitectural Vulnerabilities And Performance Tradeoffs In Shared Scientific Computing, Zyad Abd-Elrahman, Santosh Pandey

Bellini College REU Symposium (BCREUS)

Introduction & Problem Statement

Shared scientific computing platforms are built for speed and utilization, but they create a security problem that isolation alone can't fully solve. Even with TEEs, containers, or a privilege separation in place, the hardware underneath remains a shared resource. Speculative execution, cache contention, interconnect timing, and similar microarchitectural effects can leak information or create integrity risks across protection boundaries without any malicious code involved. For CI operators running graph workloads, HPC kernels, or ML pipelines in co-tenanted systems, the threat is inherent in normal execution. The real question isn't whether these vulnerabilities exist, but it's what …

Optimizing Machine Learning Workloads Performance In Ml Accelerators, Chowdhury Nafiun Nuru, Santosh Panday

Bellini College REU Symposium (BCREUS)

This project explores how to improve the performance of machine learning systems at the kernel level. As models grow in size, efficient execution depends on reducing data movement and using limited on-chip memory effectively.

Techniques such as tiling and optimized execution order are used to improve data locality and reduce memory overhead. These approaches enable better use of fast on-chip memory and minimize costly data transfers.

Using benchmark workloads, improved scheduling strategies are shown to reduce latency by avoiding suboptimal local decisions and enabling better global execution patterns.

Overall, this work demonstrates that kernel-level optimization can significantly improve performance and …

Autonomous Indoor Mapping And Navigation Using Ros2-Based Slam On A Jetson-Powered Mobile Robot, Daniel Schagen, Mark Halim, Chance Hamilton, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

This project presents the design, implementation, and evaluation of an autonomous indoor mobile robot capable of simultaneous localization and mapping (SLAM) using a ROS 2-based architecture on resource-constrained embedded hardware. The system is built on a Yahboom ROSMASTER X3 platform equipped with a Slamtec RPLidar A1, IMU, and wheel odometry, running ROS 2 Foxy on an NVIDIA Jetson Nano 4GB inside a Docker container. The primary objective is to enable the robot to autonomously map an indoor hallway environment while navigating without human intervention.

The mapping pipeline integrates slam_toolbox for occupancy grid generation, with loop closure enabled and parameters tuned …

Domain Randomization For Robot Navigation, John Hightower, Danae Ferguson, Brendon Johnson, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

Reinforcement learning enables robots to learn navigation policies in simulation, but performance often degrades during real-world deployment due to the sim-to-real gap. This work investigates domain randomization as a strategy to improve transfer for a Soft Actor-Critic navigation policy trained in a maze environment. Six targeted randomization methods are implemented including obstacle layouts, LiDAR noise, motion distance, positional noise, action perturbations, and maze switching to analyze how different sources of variability affect the robot’s ability to effectively navigate a maze. Results demonstrate the extent to which the robot can effectively generalize a policy for navigation given each domain randomization method, …

An Evaluation Of Biologically-Inspired Slam Models Against Traditional Models, Colby Mullins, Asadbek Nematov, Ross Volenec, Chance Hamilton, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

This research presents a comparative analysis of biologically-inspired SLAM models (RatSLAM [2], SeqSLAM [3]) versus traditional feature-based geometric SLAM (ORB-SLAM3 [1]) to establish a standardized framework for evaluating accuracy, robustness, and computational efficiency under varying environmental conditions. To ensure an equitable baseline, models are subjected to a unified testing pipeline across real-world datasets (Oxford RobotCar clear and overcast sequences) and controlled Webots simulations (day/night and seasonal centerlines) using timestamp-aligned 480p monocular PNG images. Within this pipeline, pose estimations are standardized to the TUM trajectory format, and spatial alignment and scale correction are applied via the evo evaluation library [4] to …

Investigating The Effect Of Input Modality On Reasoning In Vlms Using Zero-Shot Evaluation, Rodela Ghosh, Aviral Gupta, Ocean Monjur, Shrestha Datta, Anshuman Chhabra

Bellini College REU Symposium (BCREUS)

This project investigates how input modality affects mathematical reasoning in vision-language models (VLMs). Using 100 samples from the GSM8K benchmark, we evaluate Qwen2-VL-2B-Instruct and LLaVA-v1.6-Mistral-7B across three conditions: text-only, rendered image, and a modality mismatch condition where image and text describe different problems. Both models show substantial accuracy drops under visual input — Qwen2 falls from 55% to 30% and LLaVA from 40% to 21%. In the mismatch condition, both models follow the text modality in over 75% of cases, revealing strong text dominance when modalities conflict. These findings suggest that rendering math problems as images meaningfully impairs reasoning in …