I am passionate about understanding proteins — how they fold, behave, and function — and I’m currently expanding into machine learning methods in protein biology.
My goal is to build strong computational skills while contributing to meaningful biological research.

I have hands-on experience with essential wet-lab techniques, including:

• Recombinant DNA cloning
• Protein expression in bacterial and yeast systems
• Protein purification workflows (IMAC, IEX, GF)
• Cryo-EM data processing (CryoSPARC, RELION)

I enjoy bridging experimental insights with computational approaches as I grow in ML-driven biological analysis.

I am currently learning and practicing:

• Python & R
• scikit-learn, TensorFlow fundamentals
• Data analysis workflows
• Introductory ML modelling in biological contexts

• Learning projects in ML for general and biological datasets
• Notes and mini-projects documenting my journey into computational biology
• Code exploring protein-related datasets, structure-based features, and basic model building
• Open practice notebooks as I improve step-by-step

• Strengthening my computational skillset
• Working on ML-based biological research projects
• Building a solid foundation for PhD-level computational biology work

Feel free to reach out if you're working on something exciting in protein biology or computational methods — I’m always happy to learn and collaborate!