I am currently a Master's student at Columbia University, studying Computer Science with a Machine Learning specialization. During my undergrad at UCLA I studied Bioengineering with a Computer Science technical breadth. I followed an interest in computational protein engineering to opportunities to conduct research and collaborate with scientists at the most cutting-edge companies and laboratories in the world, ranging from Dr. David Baker's Institute for Protein Design at the University of Washington to Codexis, a leader in protein engineering.

While I gained in-depth knowledge of the Biotech industry at Codexis, I also saw many outdated processes and methods. I felt that the field could benefit from new computational tools, especially by leveraging Machine Learning. I taught myself how to create these tools and proposed, devised, and built an LSTM (Long-Short Term Memory) based tool for Post-Translational Modification Prediction for a gene therapy project and multiple proteins in our portfolio. The tool achieved an industry-leading MCC score and was deployed in the company-wide software suite. Since then, I’ve used ML in many side projects, learning more each time.

I decided to come to Columbia University to continue to build my ML expertise. I hope to apply my knowledge to fields that interest me such as protein engineering and drug discovery, as well as non-biotech fields like robotics, NLP, computer vision, and autonomous vehicles.