The Offense

Deanna Mujaj, Verrazzano Class of 2026, completed major in English Writing and minor in Philosophy

Studying English with a concentration on writing, I naturally decided to write a short story for my capstone. The hard part of beginning any creative task is finding the right inspiration. My parents are from Dnipro, Ukraine, and immigrated to the U.S. just a few years before I was born. With most of my extended family still living in Ukraine, the ongoing war between Ukraine and Russia has been a constant topic of discussion (and sorrow) among my family. It is difficult to imagine that had my parents not decided to come to America, I would be living in Ukraine during this horrible time. This topic was the inspiration for my short story, which follows a young man and his family in Ukraine at the start of the war, their fear, their reactions to the threat of death, and the sense of duty which compels every citizen who loves their country.

I expected the capstone to be a challenge; I had never written anything so long before. I anticipated the final work to be about 18 pages, but it ended up being 22 (and could have easily been much longer if the semester was not so short!). Once the topic had been established, the writing process was quite easy, and ideas flowed freely onto the page. My mentor, Simon Reader, was a tremendous help with the brainstorming of the plot, the character arcs, and making sure the story was compelling. We held regular meetings in which he provided notes for my writing, acting as both an editor and a guide. Looking back, the work was much easier and much more fulfilling than I anticipated. Writing about a topic that interests me, and taking inspiration from my own family truly helped me express the emotions that had been building up within me.

Part of my research was reading short stories and books that revolve around the war in Ukraine, including You Don't Know What War Is: The Diary of a Young Girl from Ukraine by Yeva Skalietska, and Jonathon Safran Foer's comedic, but heartbreaking novel, Everything is Illuminated. As further research, I try to keep up with the news concerning the war, and I occasionally read short stories and flash non-fiction concerning the topic. Overall, this experience has inspired me to write more and not shy away from taking inspiration from my family and my day-to-day life. 

Coach-Athlete Relationships: Impacts, Motivation, Coach Mental Health

Ryan Healey, Verrazzano Class of 2026, completed major in Psychology

As someone who enjoys keeping up with sports, I chose to research this topic because I have seen so many careers ruined due to poor relationships between the athlete and coach. In addition to their careers falling apart, players and coaches’ mental health can be negatively impacted from these relationships.

My expectations for the capstone was for it to be difficult because research for this subject is limited, as it is something that has really only become popular after the 2010’s. Mental health was an overlooked factor in sports before that time, and if you had a problem with a coach and/or were underperforming you were dropped, no questions asked. However, it was surprising to find that there was new research from the coach’s perspective on said relationships with athletes and how they too are affected. I was originally going into this project only focusing on athletes, but after seeing this additional research, I changed course a bit.

After completing my capstone, I am satisfied with what I found because it gives me hope. In recent years there has been evidence of support for coaches and athletes going through issues like this and they are no longer completely on their own. I do believe though that more can be done in the future to further accommodate people in situations with mental health. An example could be sports organizations as a whole taking accountability for the athletes and coaches that represent them. They could do this by making required training regimens for all members who are involved in the organization about the concern for safety not just physically, but mentally.

The Gut Microbiome's Influence on Cancer Progression and Treatment Outcomes Through the Gut-Brain Axis

Menatalla Aboukhalia, Verrazzano Class of 2026, completed major in Biology

My research explored how the tiny organisms living in our digestive systems called the gut microbiome affect the development and treatment of cancer through a communication system known as the gut-brain axis. This axis is the way our gut and brain send messages to each other using nerves, hormones, and immune signals.

Scientists have recently discovered that the health and balance of these gut microbes may not only affect our digestion and mental health but also influence cancer growth, response to therapy, and even survival rates. I focused on several types of cancer, including gastrointestinal, brain, breast, prostate, and pancreatic cancers. I analyzed over 100 studies to understand how disruptions in the gut microbiome (known as dysbiosis) are linked to tumor development. For example, harmful bacteria can produce toxins that promote inflammation and tumor growth, while helpful bacteria may boost the body’s immune response or improve the effects of chemotherapy and immunotherapy. One of the most exciting findings was that personalized treatment tailored to a patient’s specific microbiome could lead to better results in fighting cancer. In other words, understanding a patient’s gut microbiota might help doctors choose the most effective treatments. Some studies even showed that modifying the gut microbiome with probiotics, diet, or fecal transplants could improve treatment success.

I’ve always been fascinated by the connections between the body’s systems especially how something as "small" as bacteria could influence something as serious as cancer. I was drawn to this topic because it bridges biology, medicine, and even mental health. The more I read about the gut-brain axis and the microbiome, the more I realized how central this topic is to understanding modern disease. I also have a personal motivation: several family members have battled cancer, and I wanted to contribute to an area that might help improve care and outcomes in the future.

At first, I thought the capstone would mostly involve summarizing articles and writing a long paper. I underestimated the amount of critical thinking it required—especially when comparing studies with different methods or drawing conclusions from conflicting results. It wasn’t just about collecting information; it was about understanding patterns, questioning assumptions, and synthesizing new insights. That part was more difficult than I expected but also more rewarding.

The most challenging part was narrowing down the scope. The gut microbiome is connected to almost every part of health, and cancer is already such a complex disease. It was easy to get overwhelmed by the volume of research. Creating a structured methodology and sticking to my inclusion/exclusion criteria helped me stay focused. One of the easier (and more enjoyable) parts was presenting the material visually, designing charts and thematic summaries. I found that these visuals helped clarify patterns and communicate my findings more effectively. I was surprised by how fast this field is evolving. Some studies I found were already outdated after just a few years, replaced by newer, more advanced research using artificial intelligence to predict patient responses based on their microbiomes.

In the future, I’d like to explore how diet and lifestyle changes can be used alongside medical treatments to optimize the gut microbiome for cancer patients. I’m also curious about how the microbiome affects pediatric cancer and survivorship. Another promising area is the integration of machine learning to predict treatment outcomes based on a person’s microbiome profile. These tools could one day help doctors create highly personalized cancer therapies.

This research taught me how to critically evaluate scientific evidence and stay organized when working with a large number of sources. More importantly, it showed me that science is never static it’s a living conversation with new voices and discoveries all the time. I now feel more confident in my ability to take on complex problems and contribute to ongoing scientific questions. I’m leaving this experience with a deeper understanding of how biology, medicine, and technology come together and with a renewed motivation to pursue graduate studies in the health sciences.

Detecting Anomalies in Network Traffic Data

Isabel Loci, Verrazzano Class of 2026, completed major in Computer Science and minor in Mathematics

Somewhere in the last year, I fell down a rabbit hole of learning more about the most popular cyber-attacks in history. These attacks dated back decades ago, when the online world was still fairly new and became more complex as it developed. I have always been concerned about the safety and privacy of any information I use online, so it got me thinking: how do people mitigate attacks at such a high scale? At the time, I was participating in a one-year long data science boot camp, and we were focusing on building AI-models using machine learning. The base idea of machine learning is a system that is able to learn from previously collected data, detect patterns, and then make decisions on its own without any explicit programming beforehand. This method allows systems to handle unknown data well, as they can make decisions independently after training. Neat, right?

Theoretically, I thought that it should be possible to create a machine learning-based AI model that is able to distinguish normal network traffic from malicious network traffic. So, me and a few friends decided to bring this idea to life. Our project was built in Python and then deployed through HuggingFace. The final accuracy percentage of the model was 92.94%. Good, but could've been way better.

When I was brainstorming ideas with my mentor, Dr. Huo, I mentioned my project. I talked about how the methodology we chose to implement had a lot room for improvement, and that I would've liked to go back and start all over again if I knew what approach I wanted to try next. Then she suggested this simple, but very brilliant idea to me: What if my capstone was about a deep research on the topic of anomaly intrusion detection?

It all clicked then. I had previously read one or two works of people that attempted the same project, but not much more beyond that. What I didn't realize was just how many ways data scientists had solved this problem with in the past, not with just traditional machine learning methods but also deep learning ones. My work revolved around studying as many research papers as I could, summarizing my findings, and presenting them in the form of a survey paper.

What I found hardest (and a little funny) was how when I came across a concept I hadn't heard of before, four more fundamental concepts were attached to it that I could absolutely not exclude from the survey paper. I spent hours upon hours studying these concepts, breaking down complex formulas and comparing results of different methodologies simply to understand the hidden connections between them. There is a general step-by-step process that data scientists follow when building a ML model, and it is the following: sourcing the data to be used for model training, data preprocessing, selecting the appropriate algorithm, model training, and finally model evaluation. Data preprocessing is important because a dataset could have issues such as missing values, duplicate columns, bad column names and inconsistent feature names, all of which can interfere with the accuracy of the model. Then the dataset could have too many samples of one attack and not enough of another attack, needing to be balanced. Like we mentioned before, ML algorithms are split into two categories, and each category is split into several unique algorithms. After the model is trained, it then needs to be evaluated using industry standard performance metrics that give an estimation on how the model is doing. If the methods are implemented correctly, the metrics will reflect the true performance of the model. Just be careful, a score that is too good to be true is often misleading! We strive to be as close-- but not too close-- to the 100% accuracy score.

This has been the most extensive research I have gotten to work on throughout my undergraduate years, and it has sparked a love in me to do more. Already I wish to delve into more papers written for different fields. I want to study Literature, Psychology, Physics, the Arts, and so much more. It has changed the way I interact with the world around me and how I decide to embrace and utilize new information.

Working on a thesis might have not been my first idea for completing my capstone, but I am so glad it's what I chose in the end.

Improving Sepsis Prevention and Early Detection in Immunocompromised Patients Through Targeted Nursing Interventions

Aldina Tafa, Verrazzano Class of 2026, completed major in Nursing and Psychology, and minors in English Linguistics and Speech Language Pathology

Working on my capstone project became one of the most meaningful academic undertakings of my nursing education. When I first began this project, I knew sepsis was a critical issue in healthcare, but I had not yet understood the extreme vulnerability of immunocompromised patients or how consistently their early symptoms can be overlooked. As I progressed through the literature and developed my analysis, I realized how crucial nurses truly are in bridging the gap between early recognition, prevention, and lifesaving intervention. The process changed the way I view nursing practice, health equity, and my own role as a future clinician.

One of the most important things I learned through this project was how different sepsis looks in immunocompromised populations. Many patients do not present with the “classic” signs that nurses are traditionally taught to look for, no fever, no elevated white blood cell count, and often no obvious signs of infection. Understanding these atypical presentations deepened my appreciation for the complexity of nursing assessment and the level of critical thinking required to protect high-risk patients. I learned that early detection is not just about following a set of guidelines; it requires situational awareness, strong clinical judgment, and a willingness to question whether something subtle might actually be the first sign of a life-threatening decline.

This project also taught me how essential prevention truly is. Much of the existing literature focuses on treatment, what to do once sepsis is already present, but preventing it from occurring in the first place is where nurses have the greatest potential to save lives. As I reviewed studies from 2020–2025, I found myself becoming increasingly passionate about hand hygiene initiatives, aseptic technique, early warning tools, and patient education. It was eye-opening to realize how many infections are preventable, and that something as “simple” as consistent hand hygiene can literally cut the risk of sepsis in half for immunocompromised patients. Working through the evidence reinforced how powerful nursing interventions are, even when they seem routine or basic.

A major part of this capstone experience was collaborating closely with my mentor, who encouraged me to dig deeper into the policy and systemic side of sepsis prevention. Through her guidance, I learned how research connects to real-world nursing practice, hospital protocols, and national initiatives like the CDC’s Sepsis Core Elements. It helped me see how bedside nurses contribute not only to individual patient outcomes but also to broader quality improvement and institutional change. This mentorship made the project feel personal, meaningful, and tied to my future role in nursing leadership.

If I were to continue developing this research, I would want to build a project that examines sepsis prevention directly within clinical environments, particularly in oncology units, transplant floors, and long-term care facilities, where immunocompromised patients are cared for daily. I would also like to explore how technology, such as machine-learning prediction models, can be integrated into nursing workflows to support earlier recognition of subtle symptoms.

Another area worth expanding is patient and family education: creating clear, accessible tools that teach high-risk individuals how to identify early infection signs before a hospital visit.