Daniel is a rising junior in the Chemical and Biological Engineering department. He is deeply curious about methods to cheaply and efficiently simulate the complex biophysical principles underlying protein folding and interaction. His current interest in machine learning prompts an eagerness to leverage machine learning techniques for the modeling and optimization of physical systems. His peripheral interests in experimental molecular biology and information theory follow from these.

Daniel, whom you can also call Dan, is from Redwood City, CA. Outside of the ‘student’ mode of living, he co-leads Alimtas, a biotech-focused undergraduate entrepreneurship club, and loves playing tennis, DJing, designing clothes, cooking, and reading on semiotics and media theory.