Research
Two research threads, kept deliberately separate.
Interpretable deep learning on one side, and an in-progress behavioral prediction study on the other. Different supervisors, different departments, different stages of completion — presented here exactly as far as each one has actually gone.
Advanced Deep Learning Research Assistant
Supervisor: Dr. Wang Longwei
This role focused on prototype-based, interpretable deep learning — a family of image-classification approaches designed so a model's reasoning can actually be inspected, rather than treated as a black box. I studied three related systems in depth:
- — ProtoPNet — the original this-looks-like-that prototype architecture
- — TesNet — a transparent, evidence-based extension of the prototype idea
- — ProtoPool — a model using differentiable, shared prototype assignment
My work included:
- — Studying the research papers, model architectures, and source code behind each system
- — Understanding their training pipelines in PyTorch
- — Writing technical research reports for my supervisor on prototype-based and explainable/interpretable AI, including differentiable prototype assignment
- — Reproducing and training the ProtoPool model, following the project's public GitHub repository with slight modifications
This was a research-assistant position, not an original publication. I reproduced and trained the existing ProtoPool model — I didn't create it, and this work hasn't been presented at a conference.
How the three systems relate
01 — ProtoPNet
Learns a fixed set of class prototypes and classifies by comparing an image's patches to them.
02 — TesNet
Reworks the prototype space to be more transparent and better separated between classes.
03 — ProtoPool
Shares a pool of prototypes across classes using a differentiable assignment — fewer prototypes, less manual tuning.
Using Machine Learning to Predict Habit Formation and Maintenance in College Students
Supervisor: Dr. Isaiah Cohen · University of South Dakota, Department of Anthropology and Sociology
This project asks whether behavioral, psychological, and contextual signals — sleep, mood, stress, energy, and daily routine — can be used to predict whether a college student will successfully form and maintain a habit. It's a genuinely in-progress project, presented here at exactly the stage it's at.
Current and planned components:
- — Literature review and research planning
- — Survey development covering habit adherence and behavioral signals
- — Candidate models: Logistic Regression, Decision Tree, Random Forest
- — Candidate features: habit adherence, mood, stress, energy
No sample size, accuracy figures, or statistical results are reported here because none exist yet — the broader project remains a work in progress.
