Welcome to my page! I’m a research scientist at Google in New York. My research is broadly on machine learning and optimization. Recently, I’ve been focusing on important challenges that arise when deploying models in practice, such as model efficiency and robustness. I’m also directly involved in productionizing research and have served as a technical lead for multiple product launches. As part of my role, I’ve also been managing Google-sponsored research collaborations with universities.
My research has been recognized with best paper awards and honorable mentions from INFORMS ICS (2023), KDD (2022), INFORMS IOS (2020), INFORMS ICS (2020), MIT (2020), MIP Workshop (2019).
I completed my PhD at MIT where I was advised by Rahul Mazumder and worked on scalable algorithms for sparse learning. Before that, I did my masters at UIUC where I worked with ChengXiang Zhai on improving information recall in search engines.