Terry (Taehan) Kim

Hi! I’m Terry (Taehan) Kim, a Computer Science student at UC Berkeley working at the intersection of machine learning and biology. I’m passionate about building useful, interpretable tools that are accessible and trustworthy to both biologists and computer scientists.

During undergrad, I’ve done my best to explore a wide range of problems in computational biology — from small molecule generative design to protein structure prediction, RNA secondary structure learning, and single-cell modeling with probabilistic inference. I began with traditional ML tasks and gradually pushed myself toward projects that required deeper integration with biological domain knowledge, bridging the gap between engineering and life sciences.

I also love building educational content to make these ideas more accessible — you can find it on AI4Science Blog.

🔬 Research & Work Experience

• Independent Reserach Project
  Generative Toxicity-Aware Pesticide (Small Molecule) Design Model

• QSURE Fellow @ Memorial Sloan Kettering (MSKCC)
  Single-cell probabilistic modeling and gene expression inference

• ML Research Intern @ Diffuse Bio
  AI Drug Discovery: Protein structure (nanobody binder) design

• Undergraduate Researcher @ Doudna & Cate Labs (IGI)
  ML-Based RNA secondary Structure Prediction

• Undergraduate Researcher @ ACE Lab (Berkeley)
  LLM-based tools for CS Education

• Data Science Intern @ LG Chem
  Resolving Data Leakage Issues in Plant Disease Classification Model

Kaggle Competition Expert Rank

Kaggle is where I started learning ML. I occasionally participate in different competition to generate educational content for my AI4Science Blog. Trying my best to give back to the community.

• Detect AI Generated Text (Top 0.7%, 34/4484), Lead All Submissions
• LLM Science Exam (Top 8%)

✍️ Communicating Science

I enjoy explaining hard things simply, having coffee chat, and bridging the gap between AI researchers and life scientists.
Let’s connect on GitHub, LinkedIn, or AI4Science Blog.