Projects to look for at the Spring Symposium of Student Scholars

KENNESAW, Ga. | Apr 16, 2026

The Office of Undergraduate Research organizes the Symposium of Student Scholars twice per year, offering students a unique opportunity to present their research to a diverse audience, including faculty, donors, and the general public. Each student collaborates with a faculty research mentor to complete their research throughout the semester, with the Symposium representing the culmination of their work.

The Spring Symposium of Student Scholars will take place April 22-24 on the Kennesaw Campus, beginning Wednesday, April 21 with poster presentations.

Below is a look at several of the projects that will be presented, from non-invasive cancer treatments to understanding tipping norms in coffee shops to music theory.

Turning Waste into Architecture: Sustainable Design with 3D Printing

Undergraduate Students: Darius Ebihara, Tristan Alvin, Kara Gladden, Violet Schultz
Research Mentors: Jeffrey Collins

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: The project explores how waste materials such as sawdust, liquid rubber, sand, and other materials can be mixed to create a clay-like substances. These mixtures are tested and put within a 3D printer to see whether the mixtures can be used on a larger scale.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: Kara: In high school, I missed several opportunities because I was too afraid to put myself out there. I decided when I got to college, I would try different things, and this project was one of them.

Violet: I was inspired to sign up for this project because I have always had an interest in sustainability. When I got to college, I saw an opportunity to make an impact through this project.

Tristan: I was inspired to sign up for this project because I have a strong dislike for pollution, so I signed up for this project because I believe it will help the world reduce pollution.

Q: Were there any surprises or lessons learned along the way?

A: Kara: My biggest surprise was when mold started growing on mixes I consider "good," since they resembled clay the most. We had to pivot our experimentation to account for the mold.

Violet: I was surprised to see how much testing goes into research. Since starting this project, I have probably tested about 40 different recipes over the course of a month. There is a lot of trial and error in the process, but I am glad that I am able to be a part of it.

Tristan: My biggest surprise during this project was how many test recipes were thrown out because of how badly they failed. This showed me how Important it is to create many recipes to test as a lot of these wouldn鈥檛 work and each recipe that was successful had their own properties that stood out.

Q: What do you hope is the end result of this project?

A: We hope that at least one of the mixes that passes the 3D printing test can be used on a larger scale construction site in the future.

Al-Assisted Inverse Design of Magnetic Nanoparticles for Cancer Thermal Therapy

Undergraduate Student: Chanell Scott
Research Mentor: Asahi Tomitaka

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: This research focuses on improving magnetic hyperthermia, a non-invasive cancer treatment in which magnetic nanoparticles (MNPs) are injected into a tumor and heated using magnetic fields to destroy cancer cells from the inside out. The problem is finding the perfect recipe. There are millions of possible combinations of particle sizes and magnetic settings, making it difficult to find the perfect MNP for that tumor.

For this project, we use AI to solve this problem. Instead of the traditional trial-and-error method, we鈥檝e developed a model using machine learning where we input the desired heat dose needed and the AI works backward to provide the exact physical specifications required to achieve it.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: My interest in this field is dedicated both by curiosity and personal experience. Having navigated a thyroid cancer diagnosis, I know firsthand how much a diagnosis changes your life. It makes you realize how vital it is to have treatments that are effective but also kind to the body. As a computer science major, this sparked my curiosity about how machine learning can bridge the gap between theoretical lab research and clinical application. I am motivated by the potential to reduce the physical toll of cancer removal through non-invasive removal technology.

Q: Were there any surprises or lessons learned along the way?

A: The ultimate goal is to achieve personalized medicine and significantly reduce the time and cost associated with traditional lab-based trial-and-error. Rather than spending the time testing physical samples, researchers can use this model to pre-screen and find the most promising nanoparticle designs for a patient鈥檚 unique tumor profile. Ultimately, this work serves as a step toward non-invasive cancer therapy, where the treatment is digitally optimized before it ever reaches the clinic.

Q: What do you hope is the end result of this project?

A: As this was my first time working on research and using machine learning, I had to learn patience and the code needed to train the AI. I encountered a common hurdle called mode collapse, where the model becomes stuck and produces repetitive, unhelpful outputs. This experience taught me that successful machine learning is as much about troubleshooting as it is about persisting when the machine doesn鈥檛 do what you expect.

Spill the Beans: Understanding Tipping Norms from the Perspective of Coffee Shop Patrons

Undergraduate Student: Alexandria Nottage
Research Mentor: Brandon Lundy

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: This research intends to examine how small, routine decisions around tipping in coffee shops represent a larger discussion about cultural values and ongoing tensions in society. Through methods of direct observations and semi-structured interviews, my goal was to examine customers鈥� perspectives and how their behaviors demonstrate the divide between feelings of obligation and fairness. This project emphasizes cultural beliefs toward labor and service and analyzes tipping norms through a broader lens. Everyone encounters the dilemma of tipping, therefore, offering insight into the decision-making process determines how other factors influence why someone selects the 鈥測es鈥� or 鈥渘o鈥� button.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: As a regular customer of coffee shops and a barista, I think it is important to understand how internalized norms and ideas about fairness, or lack thereof, are based on cultural values rooted in the American service system, which is pertinent to the understanding of how people view their personal tipping culture. Although many Americans do not enjoy the practice of tipping, it has reached the point where the choice of 鈥渘o鈥� feels less like an actual option. The increasing expectation that customers will compensate for an unrealistic minimum wage reveals how the service industry does not support its employees. Examining cultural backgrounds and economic history and considering how these factors exert control over tipping systems become increasingly important when tipping norms become less about service quality and more about societal standards.

Q: Were there any surprises or lessons learned along the way?

A: This research taught me patience and attention to detail, specifically during the interview process. I was surprised to learn how many people still value cash systems, especially with the increase in cashless businesses. It was very interesting to see how this influenced cash tips and how that compared to digital tips.

Q: What do you hope is the end result of this project?

A: This project focused on the analytical aspects of tipping norms, with the goal of gaining further insight into the coffee shop customer perspective. The result is to encourage individuals to recognize how their biases and beliefs influence their personal tipping culture. Furthermore, I hope this research helps coffee shops understand the importance of customer tipping practices and how their patronage and employees alike can benefit from adapting to economic changes and societal challenges through wage support.

Do you Use AI?: A Talk-Aloud Study to Determine How Students are Using AI in their Writing

Undergraduate Students: Ruth Sikhamani, Vara Nath, Kylee Johnson, Kaylee Ward
Research Mentor: Jeanne Law

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: Our project focuses on the use of AI, specifically among college students. We studied how college students genuinely incorporate AI into their writing process. Rather than relying on students鈥� self-reports, we used a think-aloud approach, where participants completed a writing assignment while verbalizing their thoughts and having their screen activity recorded. This approach allows us to track their choices in real time and observe where students are consulting AI. Whether students decide not to use AI, change the wording of the response, or rely on their own thinking, the goal is to gain a clearer understanding of how AI is integrated into real writing practices, rather than assuming it replaces the entire writing process.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: Most of us involved in this project are students, so we see firsthand how AI is being integrated into our academic work every day. While there is a lot of debate about whether AI is harming or helping our education, there is surprisingly little research on how it specifically shapes students鈥� writing processes. This gap made this project especially compelling to us, as it allows us to contribute to a conversation about technology and education that directly impacts our generation. As an English education major, this project is also personally meaningful 鈥� it鈥檚 helping me better understand how to support my future students in a world where AI will be a natural part of learning.

Q: Were there any surprises or lessons learned along the way?

A: One interesting finding is that the vast majority of students using AI fear its impact on humanity. After watching the Think-Aloud Protocols, it was interesting to see the wide range of tasks that students rely on AI to complete. Some used AI to generate full essays, while others used it for smaller tasks such as finding synonyms or definitions. It also makes you think about the complexity of the writing process and how many shortcuts AI now offers to writers.

Q: What do you hope is the end result of this project?

A: Our research team hopes that by examining the ways students interact with generative AI for the writing process, we can provide a better understanding for educators. In providing the empirical foundation for AI in education, educators can put forth effective teaching practices, clearer academic policies, and have more realistic conversations regarding AI in the classroom. Our next phase seeks to understand how AI can support neurodivergent student writers.

Analytical Assessment of Neonicotinoid Residues in Georgia Honey

Undergraduate Student: Rusty Hooper
Research Mentor: Christopher Sumner

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: Humans have an astounding ability to control our environment, but that control often comes with unintended consequences. The use of insecticides has been a blessing to the field of agriculture, but they have the ability to impact untargeted pest populations 鈥� like the honeybee. There have been studies done globally that look for the presence of neonicotinoids 鈥� a class of chemicals that are widely used as insecticides 鈥� in the honey from honeybee colonies. This work aspires to bring the scope of similar studies inward and focus on the hives located in various parts of Georgia. Using analytical chemical techniques and instrumentation, I hope to not only detect the presence 鈥� or lack thereof 鈥� of these chemicals, but also to determine the amount of neonicotinoids in the honey.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: Environmental conservation and sustainability is a field that is seeing increasing importance as many nations around the world seem reticent to take meaningful action to stymie the effect of anthropogenic influence on the Earth. Bees are pollinators that have the potential to be the most impacted by human-environmental interaction. One such factor is the inadvertent contact of honeybees with these pesticides causing Colony Collapse Syndrome 鈥� where the worker bee population of a hive die or disappear, leading to the destruction of the hive. Additionally, this has downstream effects on agriculture, the ecosystem, and the economy.

Q: Were there any surprises or lessons learned along the way?

A: This project is definitely full of lessons learned; the chief among them being: research never goes exactly as planned. I hit a lot of pivot points throughout the course of working on this analytical project that have ranged from minor changes in methodology to shifting the focus of the entire part of the work to something else entirely. It has been a fun exercise in seeing the scientific method and the research process play out and playing an active role in it.

Q: What do you hope is the end result of this project?

A: Ultimately, I would like to see the project cause ripples in such a way that change can happen. It's not often that somebody in their day-to-day life is thinking about the chemical footprint that they leave or how that footprint impacts the ecosystem around them. Regardless of this specific outcome, I want to refine a process that empowers communities to understand their chemical footprint and have that understanding translate to real, sustainability focused policy change.

Developing Peptide Analogs Using Artificial Intelligence Tools to Treat Parkinson鈥檚 Diseases

Undergraduate Students: Ericka Tate & Jackson Kohn
Research Mentor: Mohammad Abdul Halim

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: Proteins perform several vital functions throughout the body that are essential to life. These proteins are formed by varying sequences of 20 different amino acids which correspond to the specific structure and function of the protein. Sometimes proteins misfold, resulting in the formation of fibrils which can cause a variety of issues. One such example is the formation of fibrils in the protein Alpha-synuclein, which is believed to be responsible for Parkinson鈥檚 disease. The goal of our project was to generate peptide analogs to prevent the formation of Alpha-synuclein fibrils using ChatGPT. The sequences generated showed promise in reducing the formation of Alpha-synuclein fibrils opening doors for further research into the use of peptides to treat Parkinson鈥檚 disease.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: Ericka: Initially, I applied to work in Dr. Halim鈥檚 lab because of his focus on using peptides to treat neurodegenerative, infectious, and cancerous diseases. Having had first-hand experience in the way that neurodegenerative and cancerous diseases impact patients and their families, I was excited by the opportunity to get involved in research with a topic I was already passionate about.

Jackson: What initially inspired me to pursue this project is how amazing an opportunity it is to participate in research of this caliber as a freshman at KSU. Not only that, but my family has had firsthand experience with the sad reality of neurodegenerative diseases. Specifically, in my family there is an impending fear surrounding Huntington鈥檚 disease. So, when I was presented with an opportunity to do research that was both in line with my major and so close to home, I knew this would be a project I pursued.

Q: Were there any surprises or lessons learned along the way?

A: With a methodology as sensitive as peptide synthesis, it's important to ensure that every step is completed to a certain standard. Small details such as not properly cleaning the tools or preparing the peptide synthesizer can negatively impact the result. This attention to detail also makes problem solving easier when mistakes do occur, as it's easier to pinpoint which step in the process the mistake occurred and come up with solutions to prevent it from happening again.

Q: What do you hope is the end result of this project?

A: The analogs generated using AI showed a 60% reduction in the formation of Alpha-synuclein fibrils, which is an improvement compared to the original base sequences. We hope that the end result of this project expands the knowledge of the peptide database and contributes to the treatment of Parkinson鈥檚 disease.

Examining the Frequency and Structure of Compound B-Sections in Recent Popular Music

Undergraduate Students: V Markey, Karla Mino, & Seraphim Duarte
Research Mentor: Jeffrey Yunek

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: Our project examines pop songs from 2010 and beyond that feature one or more A section(s) followed by a contrasting B section where the B section may either introduce entirely new material or adapt elements from the A section with melodic or harmonic variations. These strategies are most often employed as an instrumental solo, a breakdown, a bridge, or a blend of these different roles. While it is common to see just one of these strategies employed per song, we find that numerous songs from the Top 40 from the 2010s employ two or more strategies making their B sections compound (hence, the name, compound B sections).

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: What inspired me to pursue this project was my interest in exploring music through a research lens rather than a composing or performance one, but more within music theory. Music is incredibly versatile, offering such endless possibilities for analysis in a broad range of subjects (education, business, therapy, performance; the list goes on!). We hear charted pop music almost every day, and I've been curious about how and why certain songwriting techniques are so effective.

Q: Were there any surprises or lessons learned along the way?

A: One of the biggest surprises was how common compound B sections actually are: about 24% of the Top 25 songs from the 2010s. That is higher than we initially expected given the limited discussion of B section in existing research. We also found a consistent pattern in how these sections develop: Strategies like rap breaks, solos, or bridges often transitioned into a blend of these strategies that led to a full, representation to the chorus. This ultimately showed us that B sections not only provide a contrast of material in a song but provide a structured way to shape listener expectations in a fun, predictable way.

Q: What do you hope is the end result of this project?

A: I hope this project expands the conversation around pop song form by bringing more attention to compound B sections as they have little conversation surrounding them compared to A sections. By identifying their frequency and the pattern of their structures, our goal is to hopefully provide a clearer framework for understanding how modern pop songs build contrast and emotional intensity while also being useful to both scholars and musicians alike in terms of songwriting.

SoilBus: Advanced Soil Health Diagnosis with Clustering and Random Forest Machine Learning Models

Graduate Students: Carter Corbin, Daniel Byers, & Lakshay Battu
Undergraduate Students: Emanuel Allen, Sheraz Saudagar, Aaditya Moore, & Saam Haghighat-Grami

Research Mentors: Muhammad Tanveer

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: My project focuses on making farming easier and more accessible for farmers to understand health stats of their soil without needing to manually test it. We have designed a small device like a lawn dart that can be dropped from a drone after this process the lawn dart embeds itself into the ground. Once it has contacted the soil, the deal process beings! It collects important data such as temperature, moisture, and wirelessly transmits this data to the computer. We then use machine learning models like clustering and random forests to analyze the data provided. In simple terms, this machine is a smart way to 鈥渃heck鈥� for soil health and to help farmers make better decisions.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: I was inspired by the growing need for more efficient and sustainable farming practices. Agriculture is seen as an essential part of our life on Earth as it is a fundamental building block to human life, yet so many farmers rely on time-consuming methods to monitor soil conditions. I thought to myself why not combine technology like drones, sensors and machine learning to solve real-world problems.

Q: Were there any surprises or lessons learned along the way?

A: One of the biggest things which surprised me was how challenging it was to integrate both the hardware and software in particular. I found it challenging to get the software and hardware to communicate with each other into a single working system. After work and dedication, we were able to create our first protype, which was successful. Overall, the project taught me the value of patience and interdisciplinary problem solving.

Q: What do you hope is the end result of this project?

A: The goal of this project is to create a scalable and cost-effective solution for automated soil monitoring. Ideally, this technology will help farmers reduce labor costs as well as being able to improve farming efficacy. This technology can help farmers make more informed decisions about crop management. In the long term, I hope it contributes to more sustainable agricultural practices by enabling precise data driven farming.

Step Counts in the Prediction of Cardiometabolic Risk in Middle-Aged Adults

Undergraduate Student: Mackenzie Burgess
Research Mentor: Bob Buresh

Q: How would you explain your project to someone knows nothing about your field of study, such as a parent or sibling?

A: Our project is examining how the number of steps people take each day is related to their risk for developing cardiometabolic diseases, such as heart disease and diabetes. Particularly, we are interested in whether step counts, when considered relative to the amount of body fat mass, can help predict markers of cardiometabolic health. Everyone has heard of the universal 10,000 steps a day goal, but our objective is to better understand how simple, everyday physical activity like walking can serve as a meaningful indicator of long-term health when personalized for the patient.

Q: What inspired you to pursue this project? Do you have a personal connection to it?

A: How physical activity influences long-term health and disease prevention has always been an area within the field of exercise science that has truly interested me through my undergraduate journey. What drew me to this project in particular was the fact that step counts are something almost everyone can understand and track. It's easy, cost effective, and the adherence to the behavior is high because it is something we already do on a daily basis. Our study doesn鈥檛 require you to go on some crazy diet or even commit to a gym membership, you simply just need to walk!

Q: Were there any surprises or lessons learned along the way?

A: One interesting takeaway has been how complex the relationship between physical activity and health outcomes can be. The relationship between steps relative to fat mass to all cardiometabolic risk is not always perfectly linear because we are looking at outcomes that are very multifaceted. This project has truly highlighted the importance of thorough data analysis and meticulous interpretation of the results when working with large health datasets.

Q: What do you hope is the end result of this project?

A: Ultimately, I hope this research helps not only demonstrates that simple measures like daily step counts can be useful tools for understanding and predicting cardiometabolic risk, but also encourages people who may be looking to improve some existing issues, and that they come to realize that losing weight or significantly enhancing health may not be as difficult or out of their reach as they think. If these relationships are better understood, it may help guide future physical activity recommendations. I think a lot of times, people struggling with these health issues feel overwhelmed at the idea of having to change so much of their daily lifestyle at one time. Our goal is to be able to prescribe an individualized step count that can contribute to more personalized and accessible approaches to improving public health.

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Projects to look for at the Spring Symposium of Student Scholars

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