Computer Science Major Continues his Research While Completing his Masters

Christopher Paradiso, Verrazano Class of  2019, Completed Major in Computer Science & Minor in Mathematics

During this project, I learned a lot about the software MATLAB, and also about the field of Machine Learning in Computer Science. This project also exposed me to many different concepts in Mathematics. To learn these concepts, I needed to learn how to read complex mathematical formulas, which is an invaluable skill to gain. 

When I first started back in 2016, I was a bit overwhelmed by the nature of the project, due to the sheer amount of mathematical knowledge involved. However, I was enthusiastic and excited about it, and put a lot of effort into my code and learning. My favorite mathematical concept I learned was the Euclidean Distance, due to how it calculates the distance between 2 objects in any given number of dimensions. I found it interesting how similar the formula is to the Pythagorean theorem, changing the formula from finding the distance given the length to finding the distance given two points.

The project opened my mind to other fields in computer science that I wasn't initially aware of, and I am grateful for that. These fields would be the study of machine learning, data analysis, data mining, and deep learning. The knowledge from these fields can help make problems that are normally impossible or incredibly difficult to solve into something reliably achievable.

 In my research, I got better at my ability to effectively read and trace other people’s code, which is  a necessary skill for team projects throughout the Computer Science field. The project taught me a huge portion of the MATLAB programming language, including the ability to import data, export data, using excel spreadsheets, using mathematical functions, formatting output, and developing GUIs.

I plan to continue working on this project as I pursue my Master’s degree in Computer Science at CSI. The codebase I developed is important for future research, which will involve the clustering of data with more datasets. We also plan on adding features such as Fuzzy Clustering, which is when points may be a member of 2 different clusters, due to uncertainty. SVMs (Support Vector Machines) are another component of the research that requires further development. There are many potential directions the research can go in towards the future, including fields such as data mining and neural networks. Overall, I’m highly satisfied with the results of my research and enjoyed presenting at the undergraduate conference.