Bess Hagan

· Graduating December 2025 ·

Seeking full-time opportunities in AI, data, and software.

🎓 About Me

Profile Picture

I'm a computer science undergrad at Southwestern University with a passion for data, AI, and practical problem-solving. Before SU, I earned an Associate of Science in Computer Science from Temple College in 2023.

Through undergraduate research in the 2024 DREU and 2025 SURF programs, I’ve worked with real-world datasets, contributed to meaningful codebases, and tackled creative technical challenges.

These experiences showed me how much I enjoy tackling complex problems that require both creativity and persistence.

I’ll be graduating in December 2025 and am looking for a full-time role where I can dive into data analytics, geospatial science, software development, or applied AI. I want to bring curiosity and practical know-how to projects that demand both creativity and technical rigor.

  • 🔍 Interests: data science, machine learning, LLMs, GIS
  • 🛠 Tools: Python, Java, C++, R, SQL, ArcGIS Pro, GitHub Desktop
  • 📚 Libraries: PyTorch, Pandas, NumPy, scikit-learn, Matplotlib

🔬 Projects

🧠 SURF 2025

Screenshot of Mario level generation

Trained and evaluated diffusion models to generate playable Mario levels and built a real-time multiplayer LLM Story Game using WebSockets.

SURF Research Page

🎮 Capstone: Senior Seminar in Software Engineering

Research & Creative Works (RCW) poster

Designed and co-developed Evergrove, an educational game in Godot to teach data science to children.

Download and play it on itch.io See repo

🗺️ GIS Portfolio

ArcGIS Pro logo

Created maps in ArcGIS Pro highlighting geographic and demographic data.

View GIS Portfolio

🍄 Mushroom Classification with KNN

KNN Result: Confusion Matrix (edible or poisonous)

Built a K-Nearest Neighbors classifier to predict whether mushrooms are edible or poisonous using a classic public dataset.

View the Colab Notebook

🐼 DREU 2024 Research

Research & Creative Works (RCW) poster

Built a SQL-to-Python data pipeline and applied network analysis to study student behavior in a Minecraft-based learning environment.

DREU Research Page

📬 Contact

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