As a physicist by training and computational chemist in practice, I work at the intersection of fundamental physics of molecular systems, machine learning (ML), and high performance computing (HPC). I have over 6 years of experience in

• Developing and deploying atomistic and coarse grained ML force fields for molecular dynamics
• Creating Python and C++ computational workflows and optimizing for CPU and GPU machines
• Applying a variety of computational chemistry techniques to a host of material problems
• Leading projects from conception to publication in high-impact peer-reviewed journals

My research interests center on creating impact and bringing meaningful change. Atomistic and coarse grained simulations are particularly valuable in drug discovery, battery materials research, and climate-impacting initiatives such as carbon capture. You can keep up to date on my research efforts here.

As an extension of doing impactful work, I aim to make science more accessible to a larger audience. Journal articles are often geared towards field experts, making it challenging for both scientists outside the field and non-scientists to keep up with scientific progress. Moreover, I am a firm believer that not everyone learns in the same way. To this end, having access to a variety of approaches to teaching can enhance the learning experience for others. As such, I hope to contribute to the body of educational content available by providing my own perspectives and presentations of scientific topics. These resources are freely available through this website.

I’m always happy to engage with the community at large, in addition to offering support to under represented groups. If you’d like to get in touch about opportunities, collaboration, or general discussion, please feel free to connect on LinkedIn and/or contact me with the information below.

Getting in touch: Please fill out this form or message me on LinkedIn to reach me.

I received my B.S. degree in Physics from The University of Texas in Austin. Advised by Professor Andrew C. Potter, I completed an honors thesis on quantifying quantum information transfer in chiral Floquet phases. As an undergraduate, I was recognized as a Dean’s Honored Graduate for excellence in academics and research and graduated with highest honors in physics. I completed my M.S. and PhD in applied physics in 2021 and 2024, respectively. Working in the Materials Intelligence Research group of Professor Boris Kozinsky, I led the groups efforts in designing coarse grained free energy models with different machine learning frameworks. During this time, I received an award from the National Aeronautics and Space Administration (NASA), administered as a NASA Space Technology Graduate Research Opportunity. The award offered full funding for my PhD research and was renewed succesfully all 4 years of eligibility.