Undergraduate student exploring the intersection of pharmacy, biological science, protein engineering, and AI agents.

My current focus is building strong foundations in both life science and software engineering. I am especially interested in creating transparent, reproducible tools for scientific workflows rather than treating AI as a black box.

A local, CPU-friendly prototype for exploring enzyme and protein engineering workflows.

• Parses high-level engineering goals into structured tasks
• Analyzes PDB structures and relevant residue context
• Plans candidate mutations and generates readable reports
• Emphasizes traceability, reproducibility, and modular workflow design

EnzyPilot is a learning-oriented engineering demo, not an experimentally validated enzyme design platform.

A learning productivity agent and web app prototype designed to turn academic commitments into practical daily plans.

• Course schedule import and deadline tracking
• Daily study plan generation
• Personalized study suggestions
• A simple workflow for keeping plans current as priorities change

• Python, PyTorch, Git, and collaborative software workflows
• Structural biology, protein structures, and PDB analysis
• AlphaFold-related workflows and deep learning for protein modeling
• AI agents, tool use, and scientific workflow automation

I want to grow toward AI for Science, with a long-term interest in protein engineering, enzyme design, AIDD/CADD, and computational biology.

For now, I am focused on learning carefully, building reproducible prototypes, and developing the scientific judgment needed to work on meaningful research problems.

Learning the biology, building the tools, and connecting the two.