I am a computational scientist working at the intersection of chemoinformatics, molecular modeling, and computational biology. My research focuses on applying physics-based simulation and data-driven methods to understand biomolecular systems and accelerate drug discovery. I have a strong background in structure-based design, cheminformatics, and machine learning, and I develop reproducible computational workflows in Python for analyzing complex biological and chemical data. My work spans small-molecule discovery and protein therapeutics, including protein structure–function analysis, docking, molecular dynamics, and ML-assisted modeling. A recurring theme in my career is bridging disciplines: I transitioned from computational chemistry and ligand design into protein/Fc-domain modeling and computational biology, rapidly adopting new toolsets and delivering useful pipelines for research teams. I’m particularly interested in low-data ML for drug discovery, multimodal biological modeling, and translating computational insights into experimentally testable hypotheses. I enjoy working in interdisciplinary environments, collaborating closely with experimentalists and data scientists, and contributing both scientific ideas and practical software to shared research platforms.