Hello, my name is Sunghyun Park, but I go by Henry in English. I am an undergraduate student majoring in Biochemistry and Computer Science at Yonsei University in Seoul, South Korea.

My research interests lie at the intersection of machine learning for science, optimization and the modeling of complex natural systems. I focus on world model that blends data-driven learning with established domain models to build accurate, robust models. I enjoy developing hybrid approaches that incorporate physical priors, perform structure-aware regularization, and leverage system identification or data assimilation to close the loop between observation, simulation, and prediction.

In parallel, I investigate scientific reasoning in large language models as practical tools for research—designing workflows that pair LLMs with verifiers, external tools, and alignment techniques to improve faithfulness, calibration, and safe decision support. On the application side, I explore representation learning for complex biological data—especially cellular and microscopy images—using self-supervised objectives, segmentation and tracking pipelines, and geometry-aware embeddings to extract interpretable structure from noisy measurements. Across these directions, my goal is to connect theoretical insight with end-to-end practice: principled algorithms that are transparent and reliable, yet deliver real utility in AI-augmented scientific discovery and computational biology.