Currently, powerful computational methods are being heavily used to solve problems in genomics and other areas in biology. This has been made possible by advances in next-generation sequencing, which have resulted in large amounts of genomic data that we can computationally analyze. Such analysis has helped us understand the molecular basis of diseases, so that we can develop individualized treatments. I am very interested in developing computational methods – advanced algorithms, machine learning, statistical methods, and automation – to understand the genetic and molecular basis of disease. I have been involved in research in this area, starting in high school and throughout my undergraduate. For example, I have developed machine learning frameworks, including Transformers, to address problems such as inferring gene regulatory networks (GRNs). Currently, for my Master’s thesis, I am implementing statistical and modeling approaches to understand the genomic basis of opioid addiction.

I also find sharing ideas with others incredibly rewarding. I enjoy introducing programming to middle school girls through MIT’s CodeIt program and teaching undergraduates as a Lab Assistant for MIT’s Software Construction course (6.1020). I am excited to serve as a Graduate Teaching Assistant for 6.1020 next semester. In addition, as a Senior Associate of the MIT Biotech Group (MBG), I introduce students to the Boston biotech startup/venture ecosystem by organizing the Founder & Venture Capital Dinner Series. For this series, I have planned dinners with guest speakers (such as Ankit Gupta from Reverie Labs and Tony Kulesa from Pillar VC) so that students can learn about their personal journeys over a free shared meal.