Determining the Feasibility and Environmental Benefits of Implementing PV Systems to the CSI Campus

 

Emilia Turek, Verrazzano Class of 2026, completed a major in Environmental Science

My capstone project analyzed the benefits of implementing solar energy canopies to the CSI campus. I wanted to learn more about renewable energy and how CSI could benefit if it was implemented on campus.

To do this research, I digitized the parking lots of CSI on ArcGIS Pro to get the area of each row of the parking lots and getting the area of each lot. I then calculated the number of panels that could fit on each parking lot row, the cost of implementation, and the energy that could be generated annually.

From this project, I learned that implementing solar energy is a complex and costly process. Although it is beneficial and the costs will be recouped over time, it is important to research the most efficient ways this can be done to best help the environment while also limiting costs. As part of this project, different programs that could aid CSI with this were considered that would help limit costs. I learned that there are different programs that are specific to helping higher education institutions with decreasing their carbon footprint.

 This project can be developed further by considering different sources of renewable energy that can be implemented on the campus, such as wind energy. In addition, other solar energy companies can be researched, or other assumptions can be made to ensure the most efficient method is used. I found a standard size for solar panels and costs per panel to create calculations on the costs of this, the number of panels that could fit in each row, and the amount of energy that could be produced. These calculations were then used to find the recoup time when CSI would eventually get their return on investment. However, other data can be found and utilized to determine the recoup time.

Future work could also find how much funding this project could receive and use this in calculating the recoup time. I have also learned how to digitize on ArcGIS Pro with maximum accuracy. I precisely mapped out each row to the parking lots by hand using a computer mouse to get as accurate data as possible. It is important for this to be done carefully with as little human error as possible. It was challenging to map out the parking lots as accurately as possible. However, it was easy to do the calculations to find the amount of energy that could be produced.

Overall, from this capstone project, I learned about the many ways CSI would benefit from solar energy on campus. I researched initiatives that other institutions are doing to decrease their carbon footprint, and learned more skills when using ArcGIS Pro.

The Effects of Existential Concerns on Subtle Manifestations, Prejudice, and Political Preference

 Raneem Baalbaki, Verrazzano Class of 2026, completed a major in psychology with a minor in critical criminology and social justice

I joined Florette Cohen's lab two years ago because I found her research on Terror Management Theory intriguing. The theory is the idea that when we as humans are reminded about our own mortality, it causes us psychological anxiety leading us to cope with our world views and values.

When deciding what I wanted to research for my honors/capstone project, I knew I wanted to focus on aggression, as I am studying to become a forensic psychologist. I wanted to understand how far people were willing to go to perform aggressive acts under the right conditions, such as a person in power asking them to. Aggression has always interested me because the act itself is a choice and I’ve wanted to understand why some people choose aggression while others don’t. With the help of Dr. Cohen, we were able to create an experiment that tested Terror Management Theory as well as aggression in participants to develop my thesis.

I expected this project to involve hard work and extensive research; however, I never expected to thoroughly enjoy the process and have fun with it. It was definitely challenging at times, but I learned so many valuable skills and discovered how to overcome challenges in research.

My research has many aspects that could be further expanded in the future, such as why Terror Management Theory seems to be losing its effect and why non-voters scored higher on aggression than voters, and I'm excited for the opportunity to expand on these ideas.

One of the main things I took from this experience is that while research is a long and difficult process, I am capable of doing it. Not only am I capable, but I genuinely enjoy it and will continue to love this work.

ReZon: A Context-Aware Personal AI Assistant Desktop Overlay by Ahmad Basyouni

 Ahmad Basyouni, Verrazzano Class of 2026, completed a major in Engineering Science

My capstone is called ReZon, a personal AI assistant that runs as a small see-through window on top of everything else on your screen. The idea is pretty simple. Instead of leaving your work, opening a chat tab, copy-pasting your code or your chart, and asking a question somewhere else, the assistant comes to you. You press one hotkey and it can see what's on your screen, figure out what app you're in, and either give you a quick answer or do something for you.

It has three modes. Trader Mode watches TradingView and warns me when I switch between stocks, reminding me of rules I set for myself, so I don't make impulsive trades. Coding Mode takes a screenshot of whatever code I'm looking at, reads it, and gives me a short summary, a possible bug, and a fix. Productivity Mode can create calendar events, send iMessages, raise GitHub issues, and make Notion tasks, but only after I click an Approve button. It never does anything on its own.

The whole thing runs on my laptop. The language model (Qwen 2.5 7B) is loaded locally through Ollama, so nothing I'm reading, writing, or trading ever leaves my computer. The system boots up with one command in about 11 seconds, and I check it before every demo with a script that runs 33 tests against all three modes.

My main conclusion was that the AI part was almost never what made things hard. Once the model was warmed up, it gave me good answers in under two seconds. What actually broke the system was the surrounding plumbing. A hardcoded calendar name that didn't exist on my Mac. A screenshot that accidentally captured the assistant's own UI. A retry loop that gave up too early after 61 tries. None of those bugs were about intelligence. They were about details. For an assistant that lives on your screen, the smart part is necessary, but the small interface decisions are what make it feel reliable.

I picked this research area because I was annoyed at myself. I'd be paper trading on TradingView and catch myself about to click Buy on something I already told myself I wouldn't trade, just because the button was sitting right there. And while coding I'd tab over to ChatGPT, paste a function, ask a question, then come back and lose my place. Both problems are the same problem. My apps don't know about my plan, so they let me forget about it. I wanted to build the thing I wished existed for me.

I expected the project to mostly be about AI. Picking the right model, prompting it cleverly. It really wasn't. The hard parts were tiny details. Getting an Electron window to actually float above Fullscreen Chrome on Mac took way more research than I expected. Making sure the screenshot didn't capture my own overlay took an entire afternoon of debugging. AppleScript for Calendar and iMessage was simple to write, but macOS permissions fight you the first few times you run it.

What was easier than I expected was the language model itself. Qwen 2.5 7B running locally was fast enough and accurate enough that I never felt limited by it. What surprised me most was how much one tiny fix mattered. I added a hardcoded fast path that fires a guaranteed sub-second toast on every ticker change, totally separate from the slower AI path. That one design choice is probably what kept my live demo from breaking on stage.

If I keep going on this, the next step is Windows support, because every friend I showed it to asked for it. After that I want to add long-term memory using a vector database, so the assistant remembers preferences across months instead of just sessions.

The biggest thing I'm taking away is that building something for yourself first is a really good way to stay motivated for a whole semester. I learned more from debugging this one project than I did in most of my classes.