About Me

When I came to Stanford, I wanted to work on renewable energy technologies as an electrical engineer. As I continued my studies, I discovered a passion for optimization and machine learning, particularly their applications to pressing social challenges. I quickly discovered that I could combine my engineering background and my understanding of these challenges to prototype, evaluate, and communicate potential solutions. This evolving focus has shaped my identity as a science communicator who uses technical expertise to drive socially beneficial outcomes. At the same time, I began teaching as an undergraduate TA in the CS department. In this role I learned the importance of effective technical communication in the classroom, and began creating resources to more effectively teach computer science topics.

Finally, in my junior year of college, I took a computer science research course. I joined an AI for sustainability section where I learned where I could combine my passion of using machine learning to solve socially impactful problems. Here, I began conducting academic research, writing proposals, and presenting findings to technical audiences. This experience has propelled me to pursue a PhD in machine learning for sustainability, and, post-graduation, I will begin an EECS PhD at MIT where I will be advised by Sara Beery and Sherrie Wang.