I am an ABD PhD in Theoretical and Computational Chemistry from UCLA with extensive experience in quantitative research, research project management, and programming/data-science. My research lies at the intersection of atomistic simulation, statistical mechanics and thermodynamics, and machine learning. I have a passion for curating actionable insights from complex data, as well as scientific communication.

Expertise

My thesis research focuses on simulating solution phase systems with explicit solvent using molecular dynamics (MD) simulations. I have extensive experience with DFT-based ab initio MD as well as fully classical and mixed quantum-classical methodologies. My work has made particular use of statistical mechanic and thermodynamic theories to compare simulation models with experiment. Beyond my computational chemistry research I have a strong background in programming, data-science, and machine learning. I am a highly adaptable quantitative scientist with strong writing and communication skills.