Andrew Bilodeau

Andrew’s project explored the practical uses of artificial intelligence tools to improve productivity, spark creativity, and make complex tasks easier. It began with tools like SlidesAI, Copy.ai, and Otter.ai, which helped streamline everyday activities. SlidesAI, for instance, turned basic content into polished presentations, saving time even if adjustment was still needed. Andrew found that Copy.ai acted as a writing partner, quickly generating content for blogs, captions, and brainstorming, while Otter.ai converted speech into text, making it easier to keep track of ideas from lectures or meetings.

Next, the project shifted to creative AI tools. Platforms like DALL-E, NightCafe Creator, and Stable Diffusion allowed the creation of detailed, vibrant digital art just by providing text prompts. These tools made it easy to experiment with artistic ideas without needing specialized art skills.

Finally, Andrew looked at AI-driven programming and video creation. GitHub Copilot and Replit Ghostwriter offered interactive coding support, suggesting code snippets, helping with debugging, and writing comments as he worked with it. Synthesia simplified video production by using customizable AI avatars, making it possible to create professional-quality videos without prior editing experience.

This experience demonstrated the value of adapting to new technologies and collaboration of automation with human input, and explored the growing accessibility of AI for students interested in Computer Science.

Carly Carrol

Carly’s project focused on designing new datasets and teaching strategies for CSC 220, an experimental course that introduced non-coding students to data science by exploring global health crisis data. Using tools like Tableau, the course emphasized hands-on data visualization and analysis activities. Weekly lessons combined foundational data concepts with practical exercises, enabling students to uncover patterns and trends in real-world health datasets. The goal was to make data science accessible by removing the barrier of coding and creating an inclusive environment where students from diverse backgrounds could engage with data-driven decision making.

Experimenting with different teaching approaches and datasets to identify the most effective strategies for conveying data science concepts helped students gain valuable experience in data literacy, ethical data analysis, and visualization techniques.

Future iterations of the course will explore Power BI as a primary tool, comparing its features to Tableau to refine how these platforms influence learning outcomes. These findings aim to improve course design and ensure that data science education remains inclusive and effective for a wide range of learners.

Lesson Plans for CSC 220:
https://drive.google.com/drive/folders/12z-i9izRg9v8GzwMHzsZUx2RXuFoAK-M?usp=sharing

Morgan Prior

Morgan Prior, class of 2024, has been working for 2 years with Prof. Noah Daniels’ research group. She originally attended a Ram Hacks meeting at which Dr. Daniels presented his work on manifold mapping, and applied to be an Arts & Sciences Fellow for Summer 2022 with Dr. Daniels as a mentor. Beyond the fellowship, she continued research through her junior and senior years. 

Morgan’s project has focused on a fast, sublinear-time algorithm for k-nearest neighbor (kNN) search, an important and commonly used algorithm for recommendation systems, data science, and machine learning. The resulting algorithm and software implementation (in the Rust programming language) provides fast, exact kNN search on very large datasets. This approach, called CAKES (CLAM-accelerated K-nearest-neighbor Entropy-scaling Search), allows for search of enormous datasets such as those seen in genomics and astronomy.

Morgan is first author on a paper introducing CAKES, titled “Let them have CAKES: A Cutting-Edge Algorithm for Scalable, Efficient, and Exact Search on Big Data”, currently under review at SIAM Mathematics of Data Science (SIMODS). A preprint is available on the arXiv. Since graduating, Morgan is moving up to the Boston area to pursue a Ph.D in Theoretical Computer Science at Tufts University.